text:
@inproceedings{10.1145/3250786,
author = {Heuser, Carlos A.},
title = {Session Details: XML Query Processing (DB)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250786},
doi = {10.1145/3250786},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321445,
author = {Brodianskiy, Tali and Cohen, Sara},
title = {Self-Correcting Queries for Xml},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321445},
doi = {10.1145/1321440.1321445},
abstract = {It has been observed that queries over XML data sources are often unsatisfiable. Unsatisfiability may stem from several different sources, e.g., the user may be insufficiently familiar with the labels appearing the documents, or may not be intimately aware of the hierarchical structure of the documents. This difficulty may be compounded by the fact that errors in query formulation lead to an empty answer, and not to some sort of compilation error.To deal with query and document mismatches, previous research has considered returning answers that maximally satisfy (in some sense) the query, instead of only returning strictly satisfying answers. However, this breaks the golden database rule that only strictly satisfying answers are returned when querying. Indeed, the relationship between the query and answers is no longer clear, when unsatisfying answers are returned. To revive the golden database rule, this paper proposes a framework for deriving self-correcting queries over XML. This framework generates similar satisfiable queries, when the user query is unsatisfiable. The user can then choose a satisfiable query of interest, and receive exactly satisfying answers to this query.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {11–20},
numpages = {10},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321446,
author = {Souldatos, Stefanos and Wu, Xiaoying and Theodoratos, Dimitri and Dalamagas, Theodore and Sellis, Timos},
title = {Evaluation of Partial Path Queries on Xml Data},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321446},
doi = {10.1145/1321440.1321446},
abstract = {XML query languages typically allow the specification of structural patterns of elements. Finding the occurrences of such patterns in an XML tree is the key operation in XML query processing. Many algorithms have been presented for this operation. These algorithms focus mainly on the evaluation of path-pattern or tree-pattern queries. In this paper, we define a partial path-pattern query language, and we address the problem of its efficient evaluation on XML data.In order to process partial path-pattern queries, we introduce a set of sound and complete inference rules to characterize structural relationship derivation. We provide necessary and sufficient conditions for detecting query unsatisfiability and node redundancy. We show how partial path-pattern queries can be equivalently put in a canonical directed acyclic graph form. We developed two stack-based algorithms for the evaluation of partial path-pattern queries, PartialMJ and PartialPathStack. PartialMJ computes answers to the query by merge-joining the results of the root-to-leaf paths of a spanning tree of the query. PartialPathStack exploits a topological order of the nodes of the query graph to match the query pattern as a whole to the XML tree. The experimental evaluation of our algorithms shows that PartialPathStack is independent of intermediate results and largely outperforms PartialMJ.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {21–30},
numpages = {10},
keywords = {tree-structured data, partial path-pattern query, query evaluation},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321447,
author = {Li, Guoliang and Feng, Jianhua and Wang, Jianyong and Zhou, Lizhu},
title = {Effective Keyword Search for Valuable Lcas over Xml Documents},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321447},
doi = {10.1145/1321440.1321447},
abstract = {In this paper, we study the problem of effective keyword search over XML documents. We begin by introducing the notion of Valuable Lowest Common Ancestor (VLCA) to accurately and effectively answer keyword queries over XML documents. We then propose the concept of Compact VLCA (CVLCA) and compute the meaningful compact connected trees rooted as CVLCAs as the answers of keyword queries. To efficiently compute CVLCAs, we devise an effective optimization strategy for speeding up the computation, and exploit the key properties of CVLCA in the design of the stack-based algorithm for answering keyword queries. We have conducted an extensive experimental study and the experimental results show that our proposed approach achieves both high efficiency and effectiveness when compared with existing proposals.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {31–40},
numpages = {10},
keywords = {lca, information retrieval, xml keyword search, vlca, cvlca},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250787,
author = {Go\~{n}i, Alfredo},
title = {Session Details: Semantic Annotation (KM)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250787},
doi = {10.1145/3250787},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321449,
author = {Wu, Fei and Weld, Daniel S.},
title = {Autonomously Semantifying Wikipedia},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321449},
doi = {10.1145/1321440.1321449},
abstract = {Berners-Lee's compelling vision of a Semantic Web is hindered by a chicken-and-egg problem, which can be best solved by a bootstrapping method - creating enough structured data to motivate the development of applications. This paper argues that autonomously "Semantifying Wikipedia" is the best way to solve the problem. We choose Wikipedia as an initial data source, because it is comprehensive, not too large, high-quality, and contains enough manually-derived structure to bootstrap an autonomous, self-supervised process. We identify several types of structures which can be automatically enhanced in Wikipedia (e.g., link structure, taxonomic data, infoboxes, etc.), and we describea prototype implementation of a self-supervised, machine learning system which realizes our vision. Preliminary experiments demonstrate the high precision of our system's extracted data - in one case equaling that of humans.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {41–50},
numpages = {10},
keywords = {semantic web, wikipedia, information extraction},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321450,
author = {Fanizzi, Nicola and d'Amato, Claudia and Esposito, Floriana},
title = {Randomized Metric Induction and Evolutionary Conceptual Clustering for Semantic Knowledge Bases},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321450},
doi = {10.1145/1321440.1321450},
abstract = {We present an evolutionary clustering method which can be applied to multi-relational knowledge bases storing semantic resource annotations expressed in the standard languages for the Semantic Web. The method exploits an effective and language-independent semi-distance measure defined for the space of individual resources, that is based on a finite number of dimensions corresponding to a committee of features represented by a group of concept descriptions (discriminating features). We show how to obtain a maximally discriminating group of features through a feature construction method based on genetic programming. The algorithm represents the possible clusterings as strings of central elements (medoids, w.r.t. the given metric) of variable length. Hence, the number of clusters is not needed as a parameter since the method can optimize it by means of the mutation operators and of a proper fitness function. We also show how to assign each cluster with a newly constructed intensional definition in the employed concept language. An experimentation with some ontologies proves the feasibility of our method and its effectiveness in terms of clustering validity indices.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {51–60},
numpages = {10},
keywords = {conceptual clustering, description logics, unsupervised learning, metric learning, randomized optimization, genetic programming, evolutionary algorithms},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321451,
author = {Doran, Paul and Tamma, Valentina and Iannone, Luigi},
title = {Ontology Module Extraction for Ontology Reuse: An Ontology Engineering Perspective},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321451},
doi = {10.1145/1321440.1321451},
abstract = {Problems resulting from the management of shared, distributed knowledge has led to ontologies being employed as a solution, in order to effectively integrate information across applications. This is dependent on having ways to share and reuse existing ontologies; with the increased availability of ontologies on the web, some of which include thousands of concepts, novel and more efficient methods for reuse are being devised. One possible way to achieve efficient ontology reuse is through the process of ontology module extraction. A novel approach to ontology module extraction is presented that aims to achieve more efficient reuse of very large ontologies; the motivation is drawn from an Ontology Engineering perspective. This paper provides a definition of ontology modules from the reuse perspective and an approach to module extraction based on such a definition. An abstract graph model for module extraction has been defined, along with a module extraction algorithm. The novel contribution of this paper is a module extraction algorithm that is independent of the language in which the ontology is expressed. This has been implemented in ModTool; a tool that produces ontology modules via extraction. Experiments were conducted to compare ModTool to other modularisation methods.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {61–70},
numpages = {10},
keywords = {ontology module extraction, ontology engineering},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250788,
author = {Milic-Frayling, Natasa},
title = {Session Details: Natural Language I (IR)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250788},
doi = {10.1145/3250788},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321453,
author = {Roussinov, Dmitri and Turetken, Ozgur},
title = {Semantic Verification in an Online Fact Seeking Environment},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321453},
doi = {10.1145/1321440.1321453},
abstract = {Many artificial intelligence tasks, such as automated question answering, reasoning or heterogeneous database integration, involve verification of a semantic category (e.g. "coffee" is a drink, "red" is a color, while "steak" is not a drink and "big" is not a color). We present a novel algorithm to automatically validate a semantic category. Contrary to the methods suggested earlier, our approach does not rely on any manually codified knowledge but instead capitalizes on the diversity of topics and word usage on the World Wide Web. We have tested our approach within our online fact-seeking (question answering) environment. When tested on the TREC questions that expect the answer to belong to a specific semantic category, our approach has improved the accuracy by up to 14% depending on the model and metrics used.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {71–78},
numpages = {8},
keywords = {question answering, artificial intelligence, online search engines},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321454,
author = {Ouyang, You and Li, Sujian and Li, Wenjie},
title = {Developing Learning Strategies for Topic-Based Summarization},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321454},
doi = {10.1145/1321440.1321454},
abstract = {Most up-to-date well-behaved topic-based summarization systems are built upon the extractive framework. They score the sentences based on the associated features by manually assigning or experimentally tuning the weights of the features. In this paper, we discuss how to develop learning strategies in order to obtain the optimal feature weights automatically, which can be used for assigning a sound score to a sentence characterized with a set of features. The two fundamental issues are about training data and learning models. To save the costly manual annotation time and effort, we construct the training data by labeling the sentence with a "true" score calculated according to human summaries. The Support Vector Regression (SVR) model is then used to learn how to relate the "true" score of the sentence to its features. Once the relations have been mathematically modeled, SVR is able to predict the "estimated" score for any given sentence. The evaluations by ROUGE-2 criterion on DUC 2006 and DUC 2005 document sets demonstrate the competitiveness and the adaptability of the proposed approaches.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {79–86},
numpages = {8},
keywords = {support vector regression, document summarization},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321455,
author = {Pa\c{s}ca, Marius},
title = {Lightweight Web-Based Fact Repositories for Textual Question Answering},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321455},
doi = {10.1145/1321440.1321455},
abstract = {Since answers to fact-seeking questions usually reside within small factual text nuggets, often "hidden" within full-length documents, their relevance to a question is not necessarily correlated to the relevance of the full-length document to the question. Yet previous approaches to open-domain textual question answering from large document collections quasi-unanimously employ a document retrieval stage, in order to apply widely different, often expensive answer mining techniques to only a small subset of documents. Depending on the collection size, 95% or more of the documents in the collection (much more in the case of the Web) are left out of the selected subset for any given query, and thus become invisible to subsequent processing stages for actual answer mining. This paper introduces a new model for answer retrieval for question answering. The collection is distilled offline into large repositories of facts. Each fact constitutes a potential direct answer to questions seeking a particular kind of entity or relation, such as questions asking about the date of particular events. Question answering becomes equivalent to online fact retrieval, which greatly simplifies the de-facto system architecture for fact-seeking question answering. In addition to simplicity, experiments on a fact repository acquired from approximately a billion Web documents illustrate the impact of fact repositories in extracting accurate answers to a standard evaluation set of open-domain test questions and additional sets of domain-specific questions.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {87–96},
numpages = {10},
keywords = {lightweight text analysis, web information retrieval, fact extraction, question answering},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250789,
author = {Tinoco, Lucio},
title = {Session Details: Enterprise Information Management (IND)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250789},
doi = {10.1145/3250789},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321457,
author = {Lempel, Ronny and Mass, Yosi and Ofek-Koifman, Shila and Sheinwald, Dafna and Petruschka, Yael and Sivan, Ron},
title = {Just in Time Indexing for up to the Second Search},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321457},
doi = {10.1145/1321440.1321457},
abstract = {E-commerce and intranet search systems require newly arriving content to be indexed and made available for search within minutes or hours of arrival. Applications such as file system and email search demand even faster turnaround from search systems, requiring new content to become available for search almost instantaneously. However, incrementally updating inverted indices, which are the predominant datastructure used in search engines, is an expensive operation that most systems avoid performing at high rates.We present JiTI, a Just-in-Time Indexing component that allows searching over incoming content (nearly) as soon as that content reaches the system. JiTI's main idea is to invest less in the preprocessing of arriving data, at the expense of a tolerable latency in query response time. It is designed for deployment in search systems that maintain a large main index and that rebuild smaller stop-press indices once or twice an hour. JiTI augments such systems with instant retrieval capabilities over content arriving in between the stop-press builds. A main design point is for JiTI to demand few computational resources, in particular RAM and I/O.Our experiments consisted of injecting several documents and queries per second concurrently into the system over half-hour long periods. We believe that there are search applications for which the combination of the workloads we experimented with and the response times we measured present a viable solution to a pressing problem.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {97–106},
numpages = {10},
keywords = {incremental indexing, search engines, inverted indices},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321458,
author = {Drumm, Christian and Schmitt, Matthias and Do, Hong-Hai and Rahm, Erhard},
title = {Quickmig: Automatic Schema Matching for Data Migration Projects},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321458},
doi = {10.1145/1321440.1321458},
abstract = {A common task in many database applications is the migration of legacy data from multiple sources into a new one. This requires identifying semantically related elements of the source and target systems and the creation of mapping expressions to transform instances of those elements from the source format to the target format. Currently, data migration is typically done manually, a tedious and timeconsuming process, which is difficult to scale to a high number of data sources. In this paper, we describe QuickMig, a new semi-automatic approach to determining semantic correspondences between schema elements for data migration applications. QuickMig advances the state of the art with a set of new techniques exploiting sample instances, domain ontologies, and reuse of existing mappings to detect not only element correspondences but also their mapping expressions. QuickMig further includes new mechanisms to effectively incorporate domain knowledge of users into the matching process. The results from a comprehensive evaluation using real-world schemas and data indicate the high quality and practicability of the overall approach.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {107–116},
numpages = {10},
keywords = {schema matching, schema mapping, mapping discovery, data transformation, data migration},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321459,
author = {Park, Youngja},
title = {Automatic Call Section Segmentation for Contact-Center Calls},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321459},
doi = {10.1145/1321440.1321459},
abstract = {This paper presents a SVM (Support Vector Machine) classification system which divides contact-center call transcripts into "Greeting", "Question", "Refine", "Research", "Resolution", "Closing" and "Out-of-topic" sections. This call section segmentation is useful to improve search and retrieval functions and to provide more detailed statistics on calls. We use an off-the-shelf automatic speech recognition (ASR) system to generate call transcripts from recorded calls between customers and service representatives.We first classify an individual utterance into a call section by applying the SVM classifier and then merge adjacent utterances classified into a same call section. We experiment with the proposed system on 100 automatically transcribed calls. The 10-fold cross validation shows 87.2% classification accuracy. We also compare the proposed algorithm with two other approaches - the most frequent section only method and a maximum entropy-based segmentation. The evaluation shows that our system's accuracy is 12% higher than the first baseline system and 6% higher than the second baseline system respectively.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {117–126},
numpages = {10},
keywords = {classification, call section segmentation, natural language processing, contact center analytics, text mining, speech analytics},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250790,
author = {Rodrigues, Eduarda Mendes},
title = {Session Details: Classification and Clustering I (KM)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250790},
doi = {10.1145/3250790},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321461,
author = {Ertekin, Seyda and Huang, Jian and Bottou, Leon and Giles, Lee},
title = {Learning on the Border: Active Learning in Imbalanced Data Classification},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321461},
doi = {10.1145/1321440.1321461},
abstract = {This paper is concerned with the class imbalance problem which has been known to hinder the learning performance of classification algorithms. The problem occurs when there are significantly less number of observations of the target concept. Various real-world classification tasks, such as medical diagnosis, text categorization and fraud detection suffer from this phenomenon. The standard machine learning algorithms yield better prediction performance with balanced datasets. In this paper, we demonstrate that active learning is capable of solving the class imbalance problem by providing the learner more balanced classes. We also propose an efficient way of selecting informative instances from a smaller pool of samples for active learning which does not necessitate a search through the entire dataset. The proposed method yields an efficient querying system and allows active learning to be applied to very large datasets. Our experimental results show that with an early stopping criteria, active learning achieves a fast solution with competitive prediction performance in imbalanced data classification.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {127–136},
numpages = {10},
keywords = {imbalanced data, support vector machines, active learning},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321462,
author = {Wang, Jun and Lee, Meng Chen},
title = {Reconstructing Ddc for Interactive Classification},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321462},
doi = {10.1145/1321440.1321462},
abstract = {The automated text categorization (TC) has made prominent progress in recent years. However, seldom work is done on automatic classification with library classification systems, the largest and most sophisticated classification systems people ever built, such as the Dewey Decimal classification (DDC). The library classification is a very laborious and time-consuming job that requires qualification and good training.The large-scale classification schemes, such as the DDC, impose several obstacles to the state-of-art TC technologies, including very deep hierarchy, data sparseness, and skewed category distribution. These problems characterize large corpora of real-world applications and it is very hard, if not impossible, to obtain satisfactory results. In this paper, we propose a novel algorithm to reconstruct classification schemes according to the document density and category distribution, and to transform the category hierarchy into a balanced virtual taxonomy by merging sparse categories, lopping dense branches and flattening the hierarchy. To make the classification performance acceptable to real-world applications, we also propose an interactive classification model that only needs two or three times of user interaction. Extensive experiments are conducted on a 10-year bibliographic data collection of the Library of Congress to verify the proposed methodology.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {137–146},
numpages = {10},
keywords = {taxonomy reconstruction, dewey decimal classification, hierarchical classification, bibliographic data, trimming machine, interactive classification},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321463,
author = {Li, Tao and Anand, Sarabjot Singh},
title = {Diva: A Variance-Based Clustering Approach for Multi-Type Relational Data},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321463},
doi = {10.1145/1321440.1321463},
abstract = {Clustering is a common technique used to extract knowledge from a dataset in unsupervised learning. In contrast to classical propositional approaches that only focus on simple and flat datasets, relational clustering can handle multi-type interrelated data objects directly and adopt semantic information hidden in the linkage structure to improve the clustering result. However, exploring linkage information will greatly reduce the scalability of relational clustering. Moreover, some characteristics of vector data space utilized to accelerate the propositional clustering procedure are no longer valid in relational data space. These two disadvantages restrain the relational clustering techniques from being applied to very large datasets or in time-critical tasks, such as online recommender systems. In this paper we propose a new variance-based clustering algorithm to address the above difficulties. Our algorithm combines the advantages of divisive and agglomerative clustering paradigms to improve the quality of cluster results. By adopting the idea of Representative Object, it can be executed with linear time complexity. Experimental results show our algorithm achieves high accuracy, efficiency and robustness in comparison with some well-known relational clustering approaches.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {147–156},
numpages = {10},
keywords = {relational, multi-type, clustering},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250791,
author = {Giles, Lee},
title = {Session Details: Web Retrieval I (IR)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250791},
doi = {10.1145/3250791},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321465,
author = {Najork, Marc A.},
title = {Comparing the Effectiveness of Hits and Salsa},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321465},
doi = {10.1145/1321440.1321465},
abstract = {This paper compares the effectiveness of two well-known query-dependent link-based ranking algorithms, "Hyperlink-Induced Topic Search" (HITS) and the "Stochastic Approach for Link-Structure Analysis" (SALSA). The two algorithms are evaluated on a very large web graph induced by 463 million crawled web pages and a set of 28,043 queries and 485,656 results labeled by human judges. We employed three different performance measures - mean average precision (MAP), mean reciprocal rank (MRR), and normalized discounted cumulative gain (NDCG). We found that as an isolated feature, SALSA substantially outperforms HITS. This is quite surprising, given that the two algorithms operate over the same neighborhood graph induced by the query result set. We also studied the combination of SALSA and HITS with BM25F, a state-of-the-art text-based scoring function that incorporates anchor text. We found that the combination of SALSA and BM25F outperforms the combination of HITS and BM25F. Finally, we broke down our query set by query specificity, and found that SALSA (and to a lesser extent HITS) is most effective for general queries.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {157–164},
numpages = {8},
keywords = {hits, salsa, link-based ranking, web search, retrieval performance},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321466,
author = {Fernandes, David and de Moura, Edleno S. and Ribeiro-Neto, Berthier and da Silva, Altigran S. and Gon\c{c}alves, Marcos Andr\'{e}},
title = {Computing Block Importance for Searching on Web Sites},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321466},
doi = {10.1145/1321440.1321466},
abstract = {In this paper we consider the problem of using the block structure of a Web page to improve ranking results when searching for information on Web sites. Given the block structure of the Web pages as input, we propose a method for computing the importance of each block (in the form of block weights) in a Web collection. As we show through experiments, the deployment of our method may allow a significant improvement in the quality of search results. We ran experiments to compare the quality of search results when using our method to the quality obtained when using no structure information. When compared to a ranking method that considered pages as monolithic units, our block-based ranking method led to improvements in the quality of search results in experiments with two sites with heterogeneous structures. Further, our method does not increase the cost of processing queries when compared to the systems using no structural information.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {165–174},
numpages = {10},
keywords = {block class, block importance, page segmentation},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321467,
author = {Piwowarski, Benjamin and Zaragoza, Hugo},
title = {Predictive User Click Models Based on Click-through History},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321467},
doi = {10.1145/1321440.1321467},
abstract = {Web search engines consistently collect information about users interaction with the system: they record the query they issued, the URL of presented and selected documents along with their ranking. This information is very valuable: It is a poll over millions of users on the most various topics and it has been used in many ways to mine users interests and preferences. Query logs have the potential to partially alleviate the search engines from thousand of searches by providing a way to predict answers for a subset of queries and users without knowing the content of a document. Even if the predicted result is at rank one, this analysis might be of interest: If there is enough confidence on a user's click, we might redirect the user directly to the page whose link would be clicked. In this paper, we present three different models for predicting user clicks, ranging from most specific ones (using only past user history for the query) to very general ones (aggregating data over all users for a given query). The former model has a very high precision at low recall values, while the latter can achieve high recalls. We show that it is possible to combine the different models to predict with high accuracy (over 90%) a high subset of query sessions (24% of all the sessions).},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {175–182},
numpages = {8},
keywords = {web retrieval, re-finding, query log analysis, user modelling, repeat queries},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250792,
author = {Jomier, Genevi\`{e}ve},
title = {Session Details: Spatio-Temporal Databases and Time Series Streams (DB)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250792},
doi = {10.1145/3250792},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321469,
author = {Praing, Reasey and Schneider, Markus},
title = {Modeling Historical and Future Movements of Spatio-Temporal Objects in Moving Objects Databases},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321469},
doi = {10.1145/1321440.1321469},
abstract = {Spatio-temporal databases deal with geometries changing over time. In general, geometries do not only change discretely but continuously; hence we are dealing with moving objects. In the past, a few moving object data models and query languages have been proposed. Each of them supports either historical movements or future movements but not both together. Consequently, queries that start in the past and extend into the future cannot be supported. To model both historical and future movements of an object, two separate concepts with different properties are required, and extra attention is necessary to avoid their conflicts. Furthermore, current definitions of moving objects are too general and vague. It is unclear how a moving object is allowed to move through space and time. For instance, the continuity or discontinuity of motion is not specified. In this paper, we propose a new moving object data model called Balloon model which provides integrated support for both historical and future movements of moving objects. As part of the model, we provide formal definitions of moving objects with respect to their past and future movements. All kinds of queries including past queries, future queries, and queries that start in the past and end in the future are supported in our model.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {183–192},
numpages = {10},
keywords = {gis, continuity of evolutions, historical movements, predictive movements, moving objects databases, spatio-temporal data model, balloon model},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321470,
author = {Combi, Carlo and Montanari, Angelo and Pozzi, Giuseppe},
title = {The T4sql Temporal Query Language},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321470},
doi = {10.1145/1321440.1321470},
abstract = {Time characterizes every aspect of our life and its management when storing and querying data is very important. In this paper we propose a new temporal query language, called T4SQL, supporting multiple temporal dimensions of data. Besides the well-known valid and transaction times, it encompasses two additional temporal dimensions, namely, availability and event times. The availability time records when information is known and treated as true by the information system; the event times record the occurrence times of both the event that starts the valid time and the event that ends it. T4SQL is capable to deal with different temporal semantics (atemporal aka non-sequenced, current, sequenced, next) with respect to every temporal dimension. Moreover, T4SQL provides a novel temporal grouping clause and an orthogonal management of temporal properties when defining the selection condition(s) and the schema for the output relation.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {193–202},
numpages = {10},
keywords = {temporal query language, sql},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321471,
author = {Zhang, Tiancheng and Yue, Dejun and Gu, Yu and Yu, Ge},
title = {Boolean Representation Based Data-Adaptive Correlation Analysis over Time Series Streams},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321471},
doi = {10.1145/1321440.1321471},
abstract = {Correlation analysis is a basic problem in the field of data stream mining. Typical approaches add sliding window to data streams to get the recent results, but the window length defined by users is always fixed which is not suitable for the changing stream environment. We propose a Boolean representation based data-adaptive method for correlation analysis among a large number of time series streams. The periodical trends of each stream series to are monitored to choose the most suitable window size and group the series with the same trends together. Instead of adopting complex pair-wise calculation, we can also quickly get the correlation pairs of series at the optimal window sizes. All the processing is realized by simple Boolean operations. Both the theory analysis and the experimental evaluations show that our method has good computation efficiency with high accuracy.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {203–212},
numpages = {10},
keywords = {boolean representation, time series streams, correlation analysis, data-adaptive},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250793,
author = {Falc\~{a}o, Andr\'{e} O.},
title = {Session Details: Explanation, Knowledge Provenance and Synthesis (KM)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250793},
doi = {10.1145/3250793},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321473,
author = {Don, Anthony and Zheleva, Elena and Gregory, Machon and Tarkan, Sureyya and Auvil, Loretta and Clement, Tanya and Shneiderman, Ben and Plaisant, Catherine},
title = {Discovering Interesting Usage Patterns in Text Collections: Integrating Text Mining with Visualization},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321473},
doi = {10.1145/1321440.1321473},
abstract = {This paper addresses the problem of making text mining results more comprehensible to humanities scholars, journalists, intelligence analysts, and other researchers, in order to support the analysis of text collections. Our system, FeatureLens1, visualizes a text collection at several levels of granularity and enables users to explore interesting text patterns. The current implementation focuses on frequent itemsets of n-grams, as they capture the repetition of exact or similar expressions in the collection. Users can find meaningful co-occurrences of text patterns by visualizing them within and across documents in the collection. This also permits users to identify the temporal evolution of usage such as increasing, decreasing or sudden appearance of text patterns. The interface could be used to explore other text features as well. Initial studies suggest that FeatureLens helped a literary scholar and 8 users generate new hypotheses and interesting insights using 2 text collections.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {213–222},
numpages = {10},
keywords = {frequent closed itemsets, n-grams, digital humanities, text mining, user interface},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321474,
author = {Yu, Jonathan and Thom, James A. and Tam, Audrey},
title = {Ontology Evaluation Using Wikipedia Categories for Browsing},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321474},
doi = {10.1145/1321440.1321474},
abstract = {Ontology evaluation is a maturing discipline with methodologies and measures being developed and proposed. However, evaluation methods that have been proposed have not been applied to specific examples. In this paper, we present the state-of-the-art in ontology evaluation - current methodologies, criteria and measures, analyse appropriate evaluations that are important to our application - browsing in Wikipedia, and apply these evaluations in the context of ontologies with varied properties. Specifically, we seek to evaluate ontologies based on categories found in Wikipedia.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {223–232},
numpages = {10},
keywords = {ontology evaluation, user studies, wikipedia, browsing},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321475,
author = {Mihalcea, Rada and Csomai, Andras},
title = {Wikify! Linking Documents to Encyclopedic Knowledge},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321475},
doi = {10.1145/1321440.1321475},
abstract = {This paper introduces the use of Wikipedia as a resource for automatic keyword extraction and word sense disambiguation, and shows how this online encyclopedia can be used to achieve state-of-the-art results on both these tasks. The paper also shows how the two methods can be combined into a system able to automatically enrich a text with links to encyclopedic knowledge. Given an input document, the system identifies the important concepts in the text and automatically links these concepts to the corresponding Wikipedia pages. Evaluations of the system show that the automatic annotations are reliable and hardly distinguishable from manual annotations.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {233–242},
numpages = {10},
keywords = {keyword extraction, wikipedia, word sense disambiguation, semantic annotation},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321476,
author = {Hu, Meiqun and Lim, Ee-Peng and Sun, Aixin and Lauw, Hady Wirawan and Vuong, Ba-Quy},
title = {Measuring Article Quality in Wikipedia: Models and Evaluation},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321476},
doi = {10.1145/1321440.1321476},
abstract = {Wikipedia has grown to be the world largest and busiest free encyclopedia, in which articles are collaboratively written and maintained by volunteers online. Despite its success as a means of knowledge sharing and collaboration, the public has never stopped criticizing the quality of Wikipedia articles edited by non-experts and inexperienced contributors. In this paper, we investigate the problem of assessing the quality of articles in collaborative authoring of Wikipedia. We propose three article quality measurement models that make use of the interaction data between articles and their contributors derived from the article edit history. Our B<scp>asic</scp> model is designed based on the mutual dependency between article quality and their author authority. The P<scp>eer</scp>R<scp>eview</scp> model introduces the review behavior into measuring article quality. Finally, our P<scp>rob</scp>R<scp>eview</scp> models extend P<scp>eer</scp>R<scp>eview</scp> with partial reviewership of contributors as they edit various portions of the articles. We conduct experiments on a set of well-labeled Wikipedia articles to evaluate the effectiveness of our quality measurement models in resembling human judgement.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {243–252},
numpages = {10},
keywords = {article quality, collaborative authoring, wikipedia, authority, peer review},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250794,
author = {Lalmas, Mounia},
title = {Session Details: IR Modeling (IR)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250794},
doi = {10.1145/3250794},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321478,
author = {Metzler, Donald A.},
title = {Automatic Feature Selection in the Markov Random Field Model for Information Retrieval},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321478},
doi = {10.1145/1321440.1321478},
abstract = {Previous applications of the Markov random field model for information retrieval have used manually chosen features. However, it is often difficult or impossible to know, a priori, the best set of features to use for a given task or data set. Therefore, there is a need to develop automatic feature selection techniques. In this paper we describe a greedy procedure for automatically selecting features to use within the Markov random field model for information retrieval. We also propose a novel, robust method for describing classes of textual information retrieval features. Experimental results, evaluated on standard TREC test collections, show that our feature selection algorithm produces models that are either significantly more effective than, or equally effective as, models with manually selected features, such as those used in the past.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {253–262},
numpages = {10},
keywords = {feature selection, parameter estimation, markov random field model},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321479,
author = {He, Ben and Ounis, Iadh},
title = {Parameter Sensitivity in the Probabilistic Model for Ad-Hoc Retrieval},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321479},
doi = {10.1145/1321440.1321479},
abstract = {The term frequency normalisation parameter sensitivity is an important issue in the probabilistic model for Information Retrieval. A high parameter sensitivity indicates that a slight change of the parameter value may considerably affect the retrieval performance. Therefore, a weighting model with a high parameter sensitivity is not robust enough to provide a consistent retrieval performance across different collections and queries. In this paper, we suggest that the parameter sensitivity is due to the fact that the query term weights are not adequate enough to allow informative query terms to differ from non-informative ones. We show that query term reweighing, which is part of the relevance feedback process, can be successfully used to reduce the parameter sensitivity. Experiments on five Text REtrieval Conference (TREC) collections show that the parameter sensitivity does remarkably decrease when query terms are reweighed.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {263–272},
numpages = {10},
keywords = {query term reweighing, parameter sensitivity, relevance feedback},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321480,
author = {Melucci, Massimo and White, Ryen W.},
title = {Utilizing a Geometry of Context for Enhanced Implicit Feedback},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321480},
doi = {10.1145/1321440.1321480},
abstract = {Implicit feedback algorithms utilize interaction between searchers and search systems to learn more about users' needs and interests than expressed in query statements alone. This additional information can be used to formulate improved queries or directly improve retrieval performance. In this paper we present a geometric framework that utilizes multiple sources of evidence present in this interaction context (e.g., display time, document retention) to develop enhanced implicit feedback models personalized for each user and tailored for each search task. We use rich interaction logs (and associated metadata such as relevance judgments), gathered during a longitudinal user study, as relevance stimuli to compare an implicit feedback algorithm developed using the framework with alternative algorithms. Our findings demonstrate both the effectiveness of our proposed algorithm and the potential value of incorporating multiple sources of interaction evidence when developing implicit feedback algorithms.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {273–282},
numpages = {10},
keywords = {implicit relevance feedback, geometry of information retrieval},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250795,
author = {Gon\c{c}alves, Marcos A.},
title = {Session Details: Record Linkage and Approximate Matching (DB)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250795},
doi = {10.1145/3250795},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321482,
author = {Kim, Hung-sik and Lee, Dongwon},
title = {Parallel Linkage},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321482},
doi = {10.1145/1321440.1321482},
abstract = {We study the parallelization of the (record) linkage problem - i.e., to identify matching records between two collections of records, A and B. One of main idiosyncrasies of the linkage problem, compared to Database join, is the fact that once two records a in A and b in B are matched and merged to c, c needs to be compared to the rest of records in A and B again since it may incur new matching. This re-feeding stage of the linkage problem requires its solution to be iterative, and complicates the problem significantly. Toward this problem, we first discuss three plausible scenarios of inputs - when both collections are clean, only one is clean, and both are dirty. Then, we show that the intricate interplay between match and merge can exploit the characteristics of each scenario to achieve good parallelization. Our parallel algorithms achieve 6.55-7.49 times faster in speedup compared to sequential ones with 8 processors, and 11.15-18.56% improvement in efficiency compared to P-Swoosh.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {283–292},
numpages = {10},
keywords = {record linkage, parallel linkage},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321483,
author = {Leit\~{a}o, Lu\'{\i}s and Calado, P\'{a}vel and Weis, Melanie},
title = {Structure-Based Inference of Xml Similarity for Fuzzy Duplicate Detection},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321483},
doi = {10.1145/1321440.1321483},
abstract = {Fuzzy duplicate detection aims at identifying multiple representations of real-world objects stored in a data source, and is a task of critical practical relevance in data cleaning, data mining, or data integration. It has a long history for relational data stored in a single table (or in multiple tables with equal schema). Algorithms for fuzzy duplicate detection in more complex structures, e.g., hierarchies of a data warehouse, XML data, or graph data have only recently emerged. These algorithms use similarity measures that consider the duplicate status of their direct neighbors, e.g., children in hierarchical data, to improve duplicate detection effectiveness. In this paper, we propose a novel method for fuzzy duplicate detection in hierarchical and semi-structured XML data. Unlike previous approaches, it not only considers the duplicate status of children, but rather the probability of descendants being duplicates. Probabilities are computed efficiently using a Bayesian network. Experiments show the proposed algorithm is able to maintain high precision and recall values, even when dealing with data containing a high amount of errors and missing information. Our proposal is also able to outperform a state-of-the-art duplicate detection system on three different XML databases.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {293–302},
numpages = {10},
keywords = {duplicate detection, bayesian networks, xml},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321484,
author = {Dorneles, Carina F. and Heuser, Carlos A. and Orengo, Viviane Moreira and da Silva, Altigran S. and de Moura, Edleno S.},
title = {A Strategy for Allowing Meaningful and Comparable Scores in Approximate Matching},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321484},
doi = {10.1145/1321440.1321484},
abstract = {The goal of approximate data matching is to assess whether two distinct data instances represent the same real world object. This is usually achieved through the use of a similarity function, which returns a score that defines how similar two data instances are. If this score surpasses a given threshold, both data instances are considered as representing the same real world object. The score values returned by a similarity function depend on the algorithm that implements the function and have no meaning to the user (apart from the fact that a higher similarity value means that two data instances are more similar). In this paper, we propose that instead of defining the threshold in terms of the scores returned by a similarity function, the user specifies the precision that is expected from the matching process. Precision is a well known quality measure and has a clear interpretation from the user's point of view. Our approach relies on mapping between similarity scores and precision values based on a training data set. Experimental results show the training may be executed against a representative data set, and reused for other databases from the same domain.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {303–312},
numpages = {10},
keywords = {data cleaning, data integration, similarity querying},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250796,
author = {Najork, Marc},
title = {Session Details: Miscellaneous (IR)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250796},
doi = {10.1145/3250796},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321486,
author = {Cormack, Gordon V. and G\'{o}mez Hidalgo, Jos\'{e} Mar\'{\i}a and S\'{a}nz, Enrique Puertas},
title = {Spam Filtering for Short Messages},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321486},
doi = {10.1145/1321440.1321486},
abstract = {We consider the problem of content-based spam filtering for short text messages that arise in three contexts: mobile (SMS) communication, blog comments, and email summary information such as might be displayed by a low-bandwidth client. Short messages often consist of only a few words, and therefore present a challenge to traditional bag-of-words based spam filters. Using three corpora of short messages and message fields derived from real SMS, blog, and spam messages, we evaluate feature-based and compression-model-based spam filters. We observe that bag-of-words filters can be improved substantially using different features, while compression-model filters perform quite well as-is. We conclude that content filtering for short messages is surprisingly effective.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {313–320},
numpages = {8},
keywords = {email, filtering, sms, spam, blog, classification},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321487,
author = {Paltoglou, Georgios and Salampasis, Michail and Satratzemi, Maria},
title = {Hybrid Results Merging},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321487},
doi = {10.1145/1321440.1321487},
abstract = {The problem of results merging in distributed information retrieval environments has been approached by two different directions in research. Estimation approaches attempt to calculate the relevance of the returned documents through ad-hoc methodologies (weighted score merging, regression etc) while download approaches, download all the documents locally, partially or completely, in order to estimate "first hand" their relevance. Both have their advantages and disadvantages. It is assumed that download algorithms are more effective but they are very expensive in terms of time and bandwidth. Estimation approaches on the other hand, usually rely on document relevance scores being returned by the remote collections in order to achieve maximum performance. In addition to that, regression algorithms, which have proved to be more effective than weighted scores merging, rely on a significant number of overlap documents in order to function effectively, practically requiring multiple interactions with the remote collections. The new algorithm that is introduced reconciles the above two approaches, combining their strengths, while minimizing their weaknesses. It is based on downloading a limited, selected number of documents from the remote collections and estimating the relevance of the rest through regression methodologies. The proposed algorithm is tested in a variety of settings and its performance is found to be better than estimation approaches, while approximating that of download.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {321–330},
numpages = {10},
keywords = {distributed information retrieval, results merging},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321488,
author = {Anagnostopoulos, Aris and Broder, Andrei Z. and Gabrilovich, Evgeniy and Josifovski, Vanja and Riedel, Lance},
title = {Just-in-Time Contextual Advertising},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321488},
doi = {10.1145/1321440.1321488},
abstract = {Contextual Advertising is a type of Web advertising, which, given the URL of a Web page, aims to embed into the page (typically via JavaScript) the most relevant textual ads available. For static pages that are displayed repeatedly, the matching of ads can be based on prior analysis of their entire content; however, ads need to be matched also to new or dynamically created pages that cannot be processed ahead of time. Analyzing the entire body of such pages on-the-fly entails prohibitive communication and latency costs. To solve the three-horned dilemma of either low-relevance or high-latency or high-load, we propose to use text summarization techniques paired with external knowledge (exogenous to the page) to craft short page summaries in real time. Empirical evaluation proves that matching ads on the basis of such summaries does not sacrifice relevance, and is competitive with matching based on the entire page content. Specifically, we found that analyzing a carefully selected 5% fraction of the page text sacrifices only 1%-3% in ad relevance. Furthermore, our summaries are fully compatible with the standard JavaScript mechanisms used for ad placement: they can be produced at ad-display time by simple additions to the usual script, and they only add 500-600 bytes to the usual request.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {331–340},
numpages = {10},
keywords = {text summarization, text classification},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250797,
author = {Nicholas, Charles},
title = {Session Details: Query Expansion (IR)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250797},
doi = {10.1145/3250797},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321490,
author = {Macdonald, Craig and Ounis, Iadh},
title = {Expertise Drift and Query Expansion in Expert Search},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321490},
doi = {10.1145/1321440.1321490},
abstract = {Pseudo-relevance feedback, or query expansion, has been shown to improve retrieval performance in the adhoc retrieval task. In such a scenario, a few top-ranked documents are assumed to be relevant, and these are then used to expand and refine the initial user query, such that it retrieves a higher quality ranking of documents. However, there has been little work in applying query expansion in the expert search task. In this setting, query expansion is applied by assuming a few top-ranked candidates have relevant expertise, and using these to expand the query. Nevertheless, retrieval is not improved as expected using such an approach. We show that the success of the application of query expansion is hindered by the presence of topic drift within the profiles of experts that the system considers. In this work, we demonstrate how topic drift occurs in the expert profiles, and moreover, we propose three measures to predict the amount of drift occurring in an expert's profile. Finally, we suggest and evaluate ways of enhancing query expansion in expert search using our new insights. Our results show that, once topic drift has been anticipated, query expansion can be successfully applied in a general manner in the expert search task.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {341–350},
numpages = {10},
keywords = {expertise modelling, expert search information retrieval, expert finding, topic drift, query expansion},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321491,
author = {Cao, Guihong and Gao, Jianfeng and Nie, Jian-Yun and Bai, Jing},
title = {Extending Query Translation to Cross-Language Query Expansion with Markov Chain Models},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321491},
doi = {10.1145/1321440.1321491},
abstract = {Dictionary-based approaches to query translation have been widely used in Cross-Language Information Retrieval (CLIR) experiments. However, translation has been not only limited by the coverage of the dictionary, but also affected by translation ambiguities. In this paper we propose a novel method of query translation that combines other types of term relation to complement the dictionary-based translation. This allows extending the literal query translation to related words, which produce a beneficial effect of query expansion in CLIR. In this paper, we model query translation by Markov Chains (MC), where query translation is viewed as a process of expanding query terms to their semantically similar terms in a different language. In MC, terms and their relationships are modeled as a directed graph, and query translation is performed as a random walk in the graph, which propagates probabilities to related terms. This framework allows us to incorporating different types of term relation, either between two languages or within the source or target languages. In addition, the iterative training process of MC allows us to attribute higher probabilities to the target terms more related to the original query, thus offers a solution to the translation ambiguity problem. We evaluated our method on three CLIR benchmark collections, and obtained significant improvements over traditional dictionary-based approaches.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {351–360},
numpages = {10},
keywords = {query translation, markov chain, random walk, cross-language information retrieval, query expansion},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321492,
author = {Yan, Rong and Hauprmann, Alexander},
title = {Query Expansion Using Probabilistic Local Feedback with Application to Multimedia Retrieval},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321492},
doi = {10.1145/1321440.1321492},
abstract = {As one of the most effective query expansion approaches, local feedback is able to automatically discover new query terms and improve retrieval accuracy for different retrieval models. However, the performance of local feedback is heavily dependent on the assumption that most top-ranked documents are relevant to the query topic. Although this assumption might be sensible for ad-hoc text retrieval, it is usually violated in many other retrieval tasks such as multimedia retrieval. In this paper, we develop a robust local analysis approach called probabilistic local feedback (PLF) based on a discriminative probabilistic retrieval framework. The proposed model is effective for improving retrieval accuracy without assuming the most top-ranked documents are relevant. It also provides a sound probabilistic interpretation and a convergence guarantee on the iterative result updating process. Although derived from variational techniques, this approach only involves an iterative process of simple operations on ranking features and thus can be computed efficiently in practice. Our multimedia retrieval experiments on TRECVID'03-'05 collections have demonstrated the advantage of the proposed PLF approaches which can achieve noticeable gains in terms of mean average precision over various baseline methods and PRF-augmented results.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {361–370},
numpages = {10},
keywords = {probabilistic local feedback, multimedia retrieval, query expansion},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250798,
author = {Lee, Dongwon},
title = {Session Details: Query Processing (DB)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250798},
doi = {10.1145/3250798},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321494,
author = {Chan, Edward P. F. and Zhang, Jie},
title = {A Fast Unified Optimal Route Query Evaluation Algorithm},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321494},
doi = {10.1145/1321440.1321494},
abstract = {We investigate the problem of how to evaluate, fast and efficiently, classes of optimal route queries on a massive graph in a unified framework. To evaluate a route query effectively, a large network is partitioned into a collection of fragments, and distances of some optimal routes in the network are pre-computed. Under such a setting, we find a unified algorithm that can evaluate classes of optimal route queries. The classes that can be processed efficiently are called constraint preserving (CP) which include, among others, shortest path, forbidden edges, forbidden nodes and α-autonomy optimal route query classes. We prove the correctness of the unified algorithm. We then turn our attention to the optimization of the proposed algorithm. Several pruning and optimization techniques are derived that minimize the search time and I/O accesses. We show empirically that these techniques are effective. The proposed optimal route query evaluation algorithm, with all these techniques incorporated, is compared with a main-memory and a disk-based brute-force CP algorithms. We show experimentally that the proposed unified algorithm outperforms the brute-force algorithms, both in term of CPU time and I/O cost, by a wide margin.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {371–380},
numpages = {10},
keywords = {route query evaluation, distance materialization, optimal route queries},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321495,
author = {Tamashiro, Manuel and Thomo, Alex and Venkatesh, Srinivasan},
title = {Towards Practically Feasible Answering of Regular Path Queries in Lav Data Integration},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321495},
doi = {10.1145/1321440.1321495},
abstract = {Regular path queries (RPQ's) are given by means of regular expressions and ask for matching patterns on labeled graphs. RPQ's have received great attention in the context of semistructured data, which are data whose structure is irregular, partially known, or subject to frequent changes. One of the most important problems in databases today is the integration of semistructured data from multiple sources modeled as views. The well-know paradigm of computing first a view-based rewriting of the query, and then evaluating the rewriting on the view extensions is indeed possible for RPQ's. However, computing the rewriting is computationally hard as it can only be done (in the worst case) in not less than 2EXPTIME. In this paper, we provide practical evidence that computing the rewriting is hard on the average as well. On the positive side, we propose automata-theoretic techniques, which efficiently compute and utilize instead the complement of the rewriting. Notably using the latter, it is possible to answer a query, and this makes the view-based answering of RPQ's fairly feasible in practice.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {381–390},
numpages = {10},
keywords = {semistructured data, data integration, regular path queries},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321496,
author = {Fu, Tao-Young and Peng, Wen-Chih and Lee, Wang-Chien},
title = {Optimizing Parallel Itineraries for Knn Query Processing in Wireless Sensor Networks},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321496},
doi = {10.1145/1321440.1321496},
abstract = {Spatial queries for extracting data from wireless sensor networks are important for many applications, such as environmental monitoring and military surveillance. One such query is K Nearest Neighbor (KNN) query that facilitates sampling of monitored sensor data in correspondence with a given query location. Recently, itinerary-based KNN query processing techniques, that propagate queries and collect data along a pre-determined itinerary, have been developed concurrently [12] [14]. These research works demonstrate that itinerary-based KNN query processing algorithms are able to achieve better energy efficiency than other existing algorithms. However, how to derive itineraries based on different performance requirements remains a challenging problem. In this paper, we propose a new itinerary-based KNN query processing technique, called PCIKNN, that derives different itineraries aiming at optimizing two performance criteria, response latency and energy consumption. The performance of PCIKNN is analyzed mathematically and evaluated through extensive experiments. Experimental results show that PCIKNN has better performance and scalability than the state-of-the-art.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {391–400},
numpages = {10},
keywords = {k nearest neighbor query, wireless sensor networks},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250799,
author = {Calado, Pavel},
title = {Session Details: Classification and Clustering II (KM)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250799},
doi = {10.1145/3250799},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321498,
author = {Jiang, Jing and Zhai, ChengXiang},
title = {A Two-Stage Approach to Domain Adaptation for Statistical Classifiers},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321498},
doi = {10.1145/1321440.1321498},
abstract = {In this paper, we consider the problem of adapting statistical classifiers trained from some source domains where labeled examples are available to a target domain where no labeled example is available. One characteristic of such a domain adaptation problem is that the examples in the source domains and the target domain are known to follow different distributions. Thus a regular classification method would tend to overfit the source domains. We present a two-stage approach to domain adaptation, where at the first generalization stage, we look for a set of features generalizable across domains, and at the second adaptation stage, we pick up useful features specific to the target domain. Observing that the exact objective function is hard to optimize, we then propose a number of heuristics to approximately achieve the goal of generalization and adaptation. Our experiments on gene name recognition using a real data set show the effectiveness of our general framework and the heuristics.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {401–410},
numpages = {8},
keywords = {semi-supervised learning, classification, logistic regression, feature selection, domain adaptation},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321499,
author = {Rosenfeld, Benjamin and Feldman, Ronen},
title = {Clustering for Unsupervised Relation Identification},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321499},
doi = {10.1145/1321440.1321499},
abstract = {Unsupervised Relation Identification is the task of automatically discovering interesting relations between entities in a large text corpora. Relations are identified by clustering the frequently co-occurring pairs of entities in such a way that pairs occurring in similar contexts end up belonging to the same clusters. In this paper we compare several clustering setups, some of them novel and others already tried. The setups include feature extraction and selection methods and clustering algorithms. In order to do the comparison, we develop a clustering evaluation metric, specifically adapted for the relation identification task. Our experiments demonstrate significant superiority of the single-linkage hierarchical clustering with the novel threshold selection technique over the other tested clustering algorithms. Also, the experiments indicate that for successful relation identification it is important to use rich complex features of two kinds: features that test both relation slots together ("relation features"), and features that test only one slot each ("entity features"). We have found that using both kinds of features with the best of the algorithms produces very high-precision results, significantly improving over the previous work.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {411–418},
numpages = {8},
keywords = {clustering, unsupervised relation identification, relation learning, information extraction},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321500,
author = {Bechchi, Mounir and Raschia, Guillaume and Mouaddib, Noureddine},
title = {Merging Distributed Database Summaries},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321500},
doi = {10.1145/1321440.1321500},
abstract = {The database summarization system coined S<scp>aint</scp>E<scp>ti</scp>Q provides multi-resolution summaries of structured data stored into acentralized database. Summaries are computed online with a conceptual hierarchical clustering algorithm. However, most companies work in distributed legacy environments and consequently the current centralized version of S<scp>aint</scp>E<scp>ti</scp>Q is either not feasible (privacy preserving) or not desirable (resource limitations).To address this problem, we propose new algorithms to generate a single summary hierarchy given two distinct hierarchies, without scanning the raw data. The Greedy Merging Algorithm (GMA) takes all leaves of both hierarchies and generates the optimal partitioning for the considered data set with regards to a cost function (compactness and separation). Then, a hierarchical organization of summaries is built by agglomerating or dividing clusters such that the cost function may emphasize local or global patterns in the data. Thus, we obtain two different hierarchies according to the performed optimisation. However, this approach breaks down due to its exponential time complexity.Two alternative approaches with constant time complexity w.r.t. the number of data items, are proposed to tackle this problem. The first one, called Merge by Incorporation Algorithm (MIA), relies on the S<scp>aint</scp>E<scp>ti</scp>Q engine whereas the second approach, named Merge by Alignment Algorithm (MAA), consists in rearranging summaries by levels in a top-down manner.Then, we compare those approaches using an original quality measure in order to quantify how good our merged hierarchies are. Finally, an experimental study, using real data sets, shows that merging processes (MIA and MAA) are efficient in terms of computational time.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {419–428},
numpages = {10},
keywords = {tree alignment, database summary, distributed clustering},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250800,
author = {Melucci, Massimo},
title = {Session Details: Semantic IR (IR)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250800},
doi = {10.1145/3250800},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321502,
author = {Price, Susan L. and Nielsen, Marianne Lykke and Delcambre, Lois M. L. and Vedsted, Peter},
title = {Semantic Components Enhance Retrieval of Domain-Specific Documents},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321502},
doi = {10.1145/1321440.1321502},
abstract = {We seek to leverage knowledge about information organization in a domain to effectively and efficiently meet targeted information needs of expert users. The semantic components model represents document content in a manner that is complementary to full text and keyword indexing. Semantic component instances are segments of text about a particular aspect of the main topic of the document and may not correspond to structural elements in the document. This paper describes the semantic components model and presents experimental evidence from a large interactive searching study showing that semantic components, used to supplement full text and keyword indexing and to extend the query language, enhanced the retrieval of domain-specific documents in response to realistic queries posed by real users.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {429–438},
numpages = {10},
keywords = {semantic components},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321503,
author = {Pham, Trong-Ton and Maillot, Nicolas Eric and Lim, Joo-Hwee and Chevallet, Jean-Pierre},
title = {Latent Semantic Fusion Model for Image Retrieval and Annotation},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321503},
doi = {10.1145/1321440.1321503},
abstract = {This paper studies the effect of Latent Semantic Analysis (LSA) on two different tasks: multimedia document retrieval (MDR) and automatic image annotation (AIA). The contributions of this paper are twofold. First, to the best of our knowledge, this work is the first study of the influence of LSA on the retrieval of a significant number of multimedia documents (i.e. collection of 20000 tourist images). Second, it shows how different image representations (region-based and keypoint-based) can be combined by LSA to improve automatic image annotation. The document collections used for these experiments are the Corel photo collection and ImageCLEF 2006 collection.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {439–444},
numpages = {6},
keywords = {latent semantic indexing, image indexing and retrieval, automatic annotation, multimedia fusion},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321504,
author = {Milne, David N. and Witten, Ian H. and Nichols, David M.},
title = {A Knowledge-Based Search Engine Powered by Wikipedia},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321504},
doi = {10.1145/1321440.1321504},
abstract = {This paper describes Koru, a new search interface that offers effective domain-independent knowledge-based information retrieval. Koru exhibits an understanding of the topics of both queries and documents. This allows it to (a) expand queries automatically and (b) help guide the user as they evolve their queries interactively. Its understanding is mined from the vast investment of manual effort and judgment that is Wikipedia. We show how this open, constantly evolving encyclopedia can yield inexpensive knowledge structures that are specifically tailored to expose the topics, terminology and semantics of individual document collections. We conducted a detailed user study with 12 participants and 10 topics from the 2005 TREC HARD track, and found that Koru and its underlying knowledge base offers significant advantages over traditional keyword search. It was capable of lending assistance to almost every query issued to it; making their entry more efficient, improving the relevance of the documents they return, and narrowing the gap between expert and novice seekers.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {445–454},
numpages = {10},
keywords = {information retrieval, query expansion, wikipedia, thesauri, data mining},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250801,
author = {Lim, Ee-Peng},
title = {Session Details: OLAP and Multi-Dimensional Databases (DB)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250801},
doi = {10.1145/3250801},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321506,
author = {Inokuchi, Akihiro and Takeda, Koichi},
title = {A Method for Online Analytical Processing of Text Data},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321506},
doi = {10.1145/1321440.1321506},
abstract = {There are increasingly visible demands for structured/ unstructured information integration and advanced analytics. However, conventional database technology has not been able to present a robust and practical implementation of a truly integrated architecture for such purposes. After working on several industrial applications (in particular, in the healthcare and life sciences area), we have identified fundamental issues and technical approaches to tackle the issues. In this paper, we propose data representations and algebraic operations for integrating semantic information (e.g., ontologies) into OLAP systems, which allow us to analyze a huge set of textual documents with their underlying semantic information. The performance of the prototype implementation has been evaluated using real world datasets, and the high scalability and flexibility of our approach have been confirmed with respect to the computation time.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {455–464},
numpages = {10},
keywords = {olap, text mining},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321507,
author = {Eavis, Todd and Taleb, Ahmad},
title = {Mapgraph: Efficient Methods for Complex Olap Hierarchies},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321507},
doi = {10.1145/1321440.1321507},
abstract = {Online Analytical Processing is a database paradigm that provides for the rich analysis of multi-dimensional data. OLAP is often supported by a logical structure known as the Cube. However, supporting efficient OLAP query resolution in enterprise scale environments is an issue of considerable complexity. In practice, the difficulty of the problem is exacerbated by the existence of dimension hierarchies that sub-divide core dimensions into aggregation layers of varying granularity. Common hierarchy-sensitive query operations such as Rollup and Drilldown can be very costly on large cubes. Moreover, facilities for the representation of more complex hierarchical relationships are not well supported by conventional techniques. This paper presents a robust hierarchy infrastructure called mapGraph that supports the efficient and transparent manipulation of attribute hierarchies within OLAP environments. Experimental results verify that, when compared to the alternatives, very little additional overhead is introduced, even when advanced functionality is exploited.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {465–474},
numpages = {10},
keywords = {data cube, attribute hierarchies, olap},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321508,
author = {Eavis, Todd and Lopez, Alex},
title = {Rk-Hist: An r-Tree Based Histogram for Multi-Dimensional Selectivity Estimation},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321508},
doi = {10.1145/1321440.1321508},
abstract = {Database query engines typically rely upon query size estimators in order to evaluate the potential cost of alternate query plans. In multi-dimensional database systems, such as those typically found in large data warehousing environments, these selectivity estimators often take the form of multi-dimensional histograms. But while single dimensional histograms have proven to be quite accurate, even in the presence of data skew, the multi-dimensional variations have generally been far less reliable. In this paper, we present a new histogram model that is based upon an r-tree space partitioning. The localization of the r-tree boxes is in turn controlled by a Hilbert space filling curve, while a series of efficient area equalization heuristics restructures the initial boxes to provide improved bucket representation. Experimental results demonstrate significantly improved estimation accuracy relative to state of the art alternatives, as well as superior consistency across a variety of record distributions.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {475–484},
numpages = {10},
keywords = {multi-dimensional selectivity estimation, data warehousing},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250802,
author = {Mitra, Prasenjit},
title = {Session Details: Information Extraction, Conceptual Clustering, and Prioritization (KM)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250802},
doi = {10.1145/3250802},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321510,
author = {Pa\c{s}ca, Marius and Van Durme, Benjamin and Garera, Nikesh},
title = {The Role of Documents vs. Queries in Extracting Class Attributes from Text},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321510},
doi = {10.1145/1321440.1321510},
abstract = {Challenging the implicit reliance on document collections, this paper discusses the pros and cons of using query logs rather than document collections, as self-contained sources of data in textual information extraction. The differences are quantified as part of a large-scale study on extracting prominent attributes or quantifiable properties of classes (e.g., top speed, price and fuel consumption for CarModel) from unstructured text. In a head-to-head qualitative comparison, a lightweight extraction method produces class attributes that are 45% more accurate on average, when acquired from query logs rather than Web documents.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {485–494},
numpages = {10},
keywords = {knowledge acquisition, textual data sources, query logs, class attribute extraction},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321511,
author = {Chevallet, Jean-Pierre and Lim, Joo-Hwee and Le, Diem Thi Hoang},
title = {Domain Knowledge Conceptual Inter-Media Indexing: Application to Multilingual Multimedia Medical Reports},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321511},
doi = {10.1145/1321440.1321511},
abstract = {Conceptual Indexing is a way to produce only one index for many multilingual documents. Inter-Media conceptual indexing promotes the use of common concepts between two media in order to use a single index for several media. In this paper we explore such an advance indexing point of view. We show the benefit of an automatic conceptual indexing for texts and its extension for text and image documents. Tests are conducted on the multilingual image and text medical document corpus of the CLEF initiative, where we obtain best results on text in 2005 and 2006, and show promising results on images, and best results for the combination of image and text.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {495–504},
numpages = {10},
keywords = {conceptual indexing, inter-media indexing, image indexing, multilingual text indexing},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321512,
author = {Chu-Carroll, Jennifer and Prager, John},
title = {An Experimental Study of the Impact of Information Extraction Accuracy on Semantic Search Performance},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321512},
doi = {10.1145/1321440.1321512},
abstract = {Researchers have shown that various natural language processing techniques can be used in document analysis to impact search performance. For the most part, they examined how an analysis system with certain performance characteristics can be leveraged to improve document and/or passage search results. We have previously shown that semantic queries which utilize named entity and relation information extracted from the corpus can lead to significant improvement in search performance. In this paper, we extend our previous efforts and examine how search performance degrades in the face of imperfect named entity and relation extraction. Our study was carried out by developing gold standard annotated corpora and applying different error models to the gold standard annotations to simulate errors made by automatic recognizers. We identify automatic recognizer characteristics that make them more amenable to our search tasks, show that recognizer recall has more significant impact on semantic search performance than its precision, and demonstrate that significant improvement in both MAP and Exact Precision scores can be achieved by adopting automatic named entity and relation recognizers with near state-of-the-art performance.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {505–514},
numpages = {10},
keywords = {relationship recognition, semantic search, named entity recognition},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321513,
author = {Li, Lida and Muller, Michael J. and Geyer, Werner and Dugan, Casey and Brownholtz, Beth and Millen, David R.},
title = {Predicting Individual Priorities of Shared Activities Using Support Vector Machines},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321513},
doi = {10.1145/1321440.1321513},
abstract = {Activity-centric collaboration environments help knowledge workers to manage the context of their shared work activities by providing a representation for an activity and its resources. Activity management systems provide more structure and organization than email to execute the shared activity but, as the number of shared activities increases, it becomes more and more difficult for users to focus on important activities that need their attention. This paper describes a personalized activity prioritization approach implemented on top of the Lotus Connections Activities management system. Our prototype implementation allows each user to view activities ordered by her/his predicted priorities. The predictions are made using a ranking Support Vector Machine model trained with the user's past interactions with the activities system. We describe the prioritization interface and the results of an offline experiment based on data from 13 users over 6-months. Our results show that our feature set derived from shared activity structures can significantly increase prediction accuracy compared to a recency baseline.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {515–524},
numpages = {10},
keywords = {support vector machine, knowledge worker, activity flood, ranking, activity-centric collaboration, prioritization},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250803,
author = {de Moura, Edleno Silva},
title = {Session Details: Web Retrieval II (IR)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250803},
doi = {10.1145/3250803},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321515,
author = {Sieg, Ahu and Mobasher, Bamshad and Burke, Robin},
title = {Web Search Personalization with Ontological User Profiles},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321515},
doi = {10.1145/1321440.1321515},
abstract = {Every user has a distinct background and a specific goal when searching for information on the Web. The goal of Web search personalization is to tailor search results to a particular user based on that user's interests and preferences. Effective personalization of information access involves two important challenges: accurately identifying the user context and organizing the information in such a way that matches the particular context. We present an approach to personalized search that involves building models of user context as ontological profiles by assigning implicitly derived interest scores to existing concepts in a domain ontology. A spreading activation algorithm is used to maintain the interest scores based on the user's ongoing behavior. Our experiments show that re-ranking the search results based on the interest scores and the semantic evidence in an ontological user profile is effective in presenting the most relevant results to the user.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {525–534},
numpages = {10},
keywords = {ontological user profiles, search personalization, user context},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321516,
author = {Tan, Qingzhao and Mitra, Prasenjit and Giles, C. Lee},
title = {Designing Clustering-Based Web Crawling Policies for Search Engine Crawlers},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321516},
doi = {10.1145/1321440.1321516},
abstract = {The World Wide Web is growing and changing at an astonishing rate. Web information systems such as search engines have to keep up with the growth and change of the Web. Due to resource constraints, search engines usually have difficulties keeping the local database completely synchronized with the Web. In this paper, we study how tomake good use of the limited system resource and detect as many changes as possible. Towards this goal, a crawler for the Web search engine should be able to predict the change behavior of the webpages. We propose applying clustering-based sampling approach. Specifically, we first group all the local webpages into different clusters such that each cluster contains webpages with similar change pattern. We then sample webpages from each cluster to estimate the change frequency of all the webpages in that cluster. Finally, we let the crawler re-visit the cluster containing webpages with higher change frequency with a higher probability. To evaluate the performance of an incremental crawler for a Web search engine, we measure both the freshness and the quality of the query results provided by the search engine. We run extensive experiments on a real Web data set of about 300,000 distinct URLs distributed among 210 websites. The results demonstrate that our clustering algorithm effectively clusters the pages with similar change patterns, and our solution significantly outperforms the existing methods in that it can detect more changed webpages and improve the quality of the user experience for those who query the search engine.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {535–544},
numpages = {10},
keywords = {refresh policy, incremental crawler, sampling, web search engine, clustering},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321517,
author = {Park, Laurence A. F. and Ramamohanarao, Kotagiri},
title = {Mining Web Multi-Resolution Community-Based Popularity for Information Retrieval},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321517},
doi = {10.1145/1321440.1321517},
abstract = {The PageRank algorithm is used in Web information retrieval to calculate a single list of popularity scores for each page in the Web. These popularity scores are used to rank query results when presented to the user. By using the structure of the entire Web to calculate one score per document, we are calculating a general popularity score, not particular to any community. Therefore, the PageRank scores are more suited to general queries. In this paper, we introduce a more general form of PageRank, using Web multi-resolution community-based popularity scores, where each document obtains a popularity score dependent on a given Web community. When a query is related to a specific community, we choose the associated set of popularity scores and order the query results accordingly. Using Web-community based popularity scores, we achieved an 11% increase in precision over PageRank.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {545–554},
numpages = {10},
keywords = {pagerank, symmetric non-negative matrix factorisation},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321518,
author = {Wang, Changhu and Jing, Feng and Zhang, Lei and Zhang, Hong-Jiang},
title = {Learning Query-Biased Web Page Summarization},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321518},
doi = {10.1145/1321440.1321518},
abstract = {Query-biased Web page summarization is the summarization of a Web page reflecting the relevance of it to a specific query. It plays an important role in search results representation of Web search engines. In this paper, we propose a learning-based query-biased Web page summarization method. The summarization problem is solved within the typical sentence selection framework. Different from existing Web page summarization methods that use page content or link context alone, both of them are considered as the sources of sentences in this work. Most of existing learning-based summarization methods treat summarization as a sentence classification problem and train a classifier to discriminate between extracted sentences and non-extracted sentences of all training documents. The basic assumption of these methods is that sentences from different documents are comparable with respect to the class information. In contrast to the classification scheme, a ranking scheme is introduced to rank extracted sentences higher than non-extracted sentences of each training document. The underlying assumption that sentences within a document are comparable is weaker and more reasonable than the assumption of classification-based scheme. Extensive results using intrinsic evaluation metrics gauge many aspects of the proposed method.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {555–562},
numpages = {8},
keywords = {ranking, classification, support vector machines, query-biased web page summarization},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250804,
author = {Oliveira, Arlindo},
title = {Session Details: Graph Based Retrieval (IR)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250804},
doi = {10.1145/3250804},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321520,
author = {Vieira, Monique V. and Fonseca, Bruno M. and Damazio, Rodrigo and Golgher, Paulo B. and Reis, Davi de Castro and Ribeiro-Neto, Berthier},
title = {Efficient Search Ranking in Social Networks},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321520},
doi = {10.1145/1321440.1321520},
abstract = {In social networks such as Orkut, www.orkut.com, a large portion of the user queries refer to names of other people. Indeed, more than 50% of the queries in Orkut are about names of other users, with an average of 1.8 terms per query. Further, the users usually search for people with whom they maintain relationships in the network. These relationships can be modelled as edges in a friendship graph, a graph in which the nodes represent the users. In this context, search ranking can be modelled as a function that depends on the distances among users in the graph, more specifically, of shortest paths in the friendship graph. However, application of this idea to ranking is not straightforward because the large size of modern social networks (dozens of millions of users) prevents efficient computation of shortest paths at query time. We overcome this by designing a ranking formula that strikes a balance between producing good results and reducing query processing time. Using data from the Orkut social network, which includes over 40 million users, we show that our ranking, augmented by this new signal, produces high quality results, while maintaining query processing time small.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {563–572},
numpages = {10},
keywords = {social networks, graphs, shortest path},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321521,
author = {Mart\'{\i}nez-Bazan, Norbert and Munt\'{e}s-Mulero, Victor and G\'{o}mez-Villamor, Sergio and Nin, Jordi and S\'{a}nchez-Mart\'{\i}nez, Mario-A. and Larriba-Pey, Josep-L.},
title = {Dex: High-Performance Exploration on Large Graphs for Information Retrieval},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321521},
doi = {10.1145/1321440.1321521},
abstract = {Link and graph analysis tools are important devices to boost the richness of information retrieval systems. Internet and the existing social networking portals are just a couple of situations where the use of these tools would be beneficial and enriching for the users and the analysts. However, the need for integrating different data sources and, even more important, the need for high performance generic tools, is at odds with the continuously growing size and number of data repositories.In this paper we propose and evaluate DEX, a high performance graph database querying system that allows for the integration of multiple data sources. DEX makes graph querying possible in different flavors, including link analysis, social network analysis, pattern recognition and keyword search. The richness of DEX shows up in the experiments that we carried out on the Internet Movie Database (IMDb). Through a variety of these complex analytical queries, DEX shows to be a generic and efficient tool on large graph databases.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {573–582},
numpages = {10},
keywords = {data representation, social networks, graph databases, query performance, information retrieval},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321522,
author = {Wang, Xuanhui and Sun, Jian-Tao and Chen, Zheng},
title = {Shine: Search Heterogeneous Interrelated Entities},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321522},
doi = {10.1145/1321440.1321522},
abstract = {Heterogeneous entities or objects are very common and are usually interrelated with each other in many scenarios. For example, typical Web search activities involve multiple types of interrelated entities such as end users, Web pages, and search queries. In this paper, we define and study a novel problem: <ul>S</ul>earch <ul>H</ul>eterogeneous <ul>IN</ul>terrelated <ul>E</ul>ntities (SHINE). Given a SHINE-query which can be any type(s) of entities, the task of SHINE is to retrieve multiple types of related entities to answer this query. This is in contrast to the traditional search,which only deals with a single type of entities (e.g., Web pages). The advantages of SHINE include: (1) It is feasible for end users to specify their information need along different dimensions by accepting queries with different types. (2) Answering a query by multiple types of entities provides informative context for users to better understand the search results and facilitate their information exploration. (3) Multiple relations among heterogeneous entities can be utilized to improve the ranking of any particular type of entities. To attain the goal of SHINE, we propose to represent all entities in a unified space through utilizing their interaction relationships. Two approaches, M-LSA and E-VSM, are discussed and compared in this paper. The experiments on 3 data sets (i.e., a literature data set, a search engine log data set, and a recommendation data set) show the effectiveness and flexibility of our proposed methods.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {583–592},
numpages = {10},
keywords = {search, heterogeneous interrelated entities, shine, multiple types},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250805,
author = {Frieder, Ophir},
title = {Session Details: Data Exploration and Discovery (KM)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250805},
doi = {10.1145/3250805},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321524,
author = {Schockaert, Steven and De Cock, Martine},
title = {Reasoning about Vague Topological Information},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321524},
doi = {10.1145/1321440.1321524},
abstract = {Topological information plays a fundamental role in the human perception of spatial configurations and is thereby one of the most prominent geographical features in natural language. As vagueness abounds in geography, flexible formalisms with the ability to capture vague topological information are often needed in practice. While such formalisms have already been introduced by various authors, complete reasoning procedures are usually not discussed. In this paper, we show how many interesting reasoning tasks, such as consistency checking and entailment checking, can be supported in a generalization of the well-known RCC-8 calculus. In particular, we present decision procedures based on linear programming, solving all reasoning tasks of interest. We furthermore show how deciding the consistency of vague topological information can be reduced to the consistency problem of the original RCC-8.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {593–602},
numpages = {10},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321525,
author = {Yang, Di and Rundensteiner, Elke A. and Ward, Matthew O.},
title = {Nugget Discovery in Visual Exploration Environments by Query Consolidation},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321525},
doi = {10.1145/1321440.1321525},
abstract = {Queries issued by casual users or specialists exploring a dataset often point us to important subsets of the data, be it clusters, outliers or other meaningful features. Capturing and caching such queries (henceforth called nuggets) has many potential benefits, including the optimization of the system performance and the search experience of users. Unfortunately, current visual exploration systems have not yet tapped into this potential resource of identifying and sharing important queries. In this paper, we introduce a query consolidation strategy aimed at solving the general problem of isolating important queries from the potentially huge amount of queries submitted. Our solution clusters redundant queries caused by exploration-style query specification, which is prevalent in data exploration systems. To measure the similarity between queries, we designed an effective distance metric that incorporates both the query specification and the actual query result. To overcome its high complexity when comparing queries with large result sets, we designed an approximation method, which is efficient while still providing excellent accuracy. A user study conducted on multivariate data sets comparing our proposed technique to others in the literature confirms that the proposed distance metric indeed matches well with users' intuition. As proof of feasibility, we integrated our proposed query consolidation solution into the Nugget Management System (NMS) framework [22], which is based on a visual exploration system XmdvTool. A second user study indicates that both the efficiency and accuracy of users' visual exploration are enhanced when supported by NMS.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {603–612},
numpages = {10},
keywords = {query redundancy, query consolidation, distance metrics},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr

branch_name: refs/heads/main