text:
440.1321526,
author = {Lang, Kevin J. and Andersen, Reid},
title = {Finding Dense and Isolated Submarkets in a Sponsored Search Spending Graph},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321526},
doi = {10.1145/1321440.1321526},
abstract = {Methods for improving sponsored search revenue are often tested or deployed within a small submarket of the larger marketplace. For many applications, the ideal submarket contains a small number of nodes, a large amount of spending within the submarket, and a small amount of spending leaving the submarket. We introduce an efficient algorithm for finding submarkets that are optimal for a user-specified tradeoff between these three quantities. We apply our algorithm to find submarkets that are both dense and isolated in a large spending graph from Yahoo! sponsored search.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {613–622},
numpages = {10},
keywords = {sparse cuts, dense subgraphs, pareto-optimality, sponsored search, e-commerce, vector-valued optimization, parametric flow},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250806,
author = {Clarke, Charles},
title = {Session Details: IR Evaluation (IR)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250806},
doi = {10.1145/3250806},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321528,
author = {Smucker, Mark D. and Allan, James and Carterette, Ben},
title = {A Comparison of Statistical Significance Tests for Information Retrieval Evaluation},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321528},
doi = {10.1145/1321440.1321528},
abstract = {Information retrieval (IR) researchers commonly use three tests of statistical significance: the Student's paired t-test, the Wilcoxon signed rank test, and the sign test. Other researchers have previously proposed using both the bootstrap and Fisher's randomization (permutation) test as non-parametric significance tests for IR but these tests have seen little use. For each of these five tests, we took the ad-hoc retrieval runs submitted to TRECs 3 and 5-8, and for each pair of runs, we measured the statistical significance of the difference in their mean average precision. We discovered that there is little practical difference between the randomization, bootstrap, and t tests. Both the Wilcoxon and sign test have a poor ability to detect significance and have the potential to lead to false detections of significance. The Wilcoxon and sign tests are simplified variants of the randomization test and their use should be discontinued for measuring the significance of a difference between means.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {623–632},
numpages = {10},
keywords = {hypothesis test, sign, wilcoxon, bootstrap, student's t-test, randomization, statistical significance, permutation},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321529,
author = {Aslam, Javed A. and Yilmaz, Emine},
title = {Inferring Document Relevance from Incomplete Information},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321529},
doi = {10.1145/1321440.1321529},
abstract = {Recent work has shown that average precision can be accurately estimated from a small random sample of judged documents. Unfortunately, such "random pools" cannot be used to evaluate retrieval measures in any standard way. In this work, we show that given such estimates of average precision, one can accurately infer the relevances of the remaining unjudged documents, thus obtaining a fully judged pool that can be used in standard ways for system evaluation of all kinds. Using TREC data, we demonstrate that our inferred judged pools are well correlated with assessor judgments, and we further demonstrate that our inferred pools can be used to accurately infer precision recall curves and all commonly used measures of retrieval performance.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {633–642},
numpages = {10},
keywords = {incomplete judgments, relevance judgments, average precision},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321530,
author = {Carterette, Ben and Smucker, Mark D.},
title = {Hypothesis Testing with Incomplete Relevance Judgments},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321530},
doi = {10.1145/1321440.1321530},
abstract = {Information retrieval experimentation generally proceeds in a cycle of development, evaluation, and hypothesis testing. Ideally, the evaluation and testing phases should be short and easy, so as to maximize the amount of time spent in development. There has been recent work on reducing the amount of assessor effort needed to evaluate retrieval systems, but it has not, for the most part, investigated the effects of these methods on tests of significance. In this work, we explore in detail the effects of reduced sets of judgments on the sign test. We demonstrate both analytically and empirically the relationship between the power of the test, the number of topics evaluated, and the number of judgments available. Using these relationships, we can determine the number of topics and judgments needed for the least-cost but highest-confidence significance evaluation. Specifically, testing pairwise significance over 192 topics with fewer than 5 judgments for each is as good as testing significance over 25 topics with an average of 166 judgments for each - 85% less effort producing no additional errors.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {643–652},
numpages = {10},
keywords = {information retrieval, hypothesis testing, test collections, evaluation},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250807,
author = {Couto, Francisco},
title = {Session Details: Performance Issues (DB)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250807},
doi = {10.1145/3250807},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321532,
author = {Botelho, Fabiano C. and Ziviani, Nivio},
title = {External Perfect Hashing for Very Large Key Sets},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321532},
doi = {10.1145/1321440.1321532},
abstract = {We present a simple and efficient external perfect hashing scheme (referred to as EPH algorithm) for very large static key sets. We use a number of techniques from the literature to obtain a novel scheme that is theoretically well-understood and at the same time achieves an order-of-magnitude increase in the size of the problem to be solved compared to previous "practical" methods. We demonstrate the scalability of our algorithm by constructing minimum perfect hash functions for a set of 1.024 billion URLs from the World Wide Web of average length 64 characters in approximately 62 minutes, using a commodity PC. Our scheme produces minimal perfect hash functions using approximately 3.8 bits per key. For perfect hash functions in the range {0,...,2n - 1} the space usage drops to approximately 2.7 bits per key. The main contribution is the first algorithm that has experimentally proven practicality for sets in the order of billions of keys and has time and space usage carefully analyzed without unrealistic assumptions.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {653–662},
numpages = {10},
keywords = {functions, perfect, key sets, minimal, hash, large},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321533,
author = {Rao, Weixiong and Chen, Lei and Fu, Ada Wai-Chee and Bu, YingYi},
title = {Optimal Proactive Caching in Peer-to-Peer Network: Analysis and Application},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321533},
doi = {10.1145/1321440.1321533},
abstract = {As a promising new technology with the unique properties like high efficiency, scalability and fault tolerance, Peer-to-Peer (P2P) technology is used as the underlying network to build new Internet-scale applications. However, one of the well known issues in such an application (for example WWW) is that the distribution of data popularities is heavily tailed with a Zipf-like distribution. With consideration of the skewed popularity we adopt a proactive caching approach to handle the challenge, and focus on two key problems: where (i.e. the placement strategy: where to place the replicas) and how (i.e. the degree problem: how many replicas are assigned to one specific content)? For the where problem, we propose a novel approach which can be generally applied to structured P2P networks. Next, we solve two optimization objectives related to the how problem: MAX_PERF and MIN_COST. Our solution is called <b>PoPCache</b>, and we discover two interesting properties: (1) the number of replicas assigned to each content is proportional to its popularity; (2) the derived optimal solutions are related to the entropy of popularity. To our knowledge, none of the previous works has mentioned such results. Finally, we apply the results of PoPCache to propose a P2P base web caching, called as Web-PoPCache. By means of web cache trace driven simulation, our extensive evaluation results demonstrate the advantages of PoPCache and Web-PoPCache.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {663–672},
numpages = {10},
keywords = {placement strategy, peer-to-peer, web caching},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321534,
author = {Bhowmick, Sourav S. and Leonardi, Erwin and Sun, Hongmei},
title = {Efficient Evaluation of High-Selective Xml Twig Patterns with Parent Child Edges in Tree-Unaware Rdbms},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321534},
doi = {10.1145/1321440.1321534},
abstract = {Recent study showed that native twig join algorithms and tree-aware relational framework significantly outperform tree-unaware approaches in evaluating structural relationships in XML twig queries. In this paper, we present an efficient strategy to evaluate high-selective twig queries containing only parent-child relationships in a tree-unaware relational environment. Our scheme is built on top of our S<scp>UCXENT</scp>++ system. We show that by exploiting the encoding scheme of S<scp>UCXENT</scp>++, we can devise efficient strategy for evaluating such twig queries. Extensive performance studies on various data sets and queries show that our approach performs better than a representative tree-unaware approach (G<scp>LOBAL</scp>-O<scp>RDER</scp>) and a state-of-the-art native twig join algorithm (TJF<scp>AST</scp>) on all benchmark queries with the highest observed gain factors being 243 and 95, respectively. Additionally, our approach reduces significantly the performance gap between tree-aware and tree-unaware approaches and even outperforms a tree-aware approach(M<scp>ONET</scp>DB/XQ<scp>UERY</scp>) for certain high-selective twig queries. We also report our insights to the plan choices a relational optimizer made during twig query evaluation by visually characterizing its behavior over the relational selectivity space.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {673–682},
numpages = {10},
keywords = {parent child edges, xml, twig query evaluation, tree-unaware rdbms},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250808,
author = {Marx, Maarten},
title = {Session Details: Information Representation and Integration (KM)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250808},
doi = {10.1145/3250808},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321536,
author = {Pa\c{s}ca, Marius},
title = {Weakly-Supervised Discovery of Named Entities Using Web Search Queries},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321536},
doi = {10.1145/1321440.1321536},
abstract = {A seed-based framework for textual information extraction allows for weakly supervised extraction of named entities from anonymized Web search queries. The extraction is guided by a small set of seed named entities, without any need for handcrafted extraction patterns or domain-specific knowledge, allowing for the acquisition of named entities pertaining to various classes of interest to Web search users. Inherently noisy search queries are shown to be a highly valuable, albeit little explored, resource for Web-based named entity discovery.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {683–690},
numpages = {8},
keywords = {weakly supervised information extraction, named entities, query logs, unstructured text, knowledge acquisition},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321537,
author = {von Brzeski, Vadim and Irmak, Utku and Kraft, Reiner},
title = {Leveraging Context in User-Centric Entity Detection Systems},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321537},
doi = {10.1145/1321440.1321537},
abstract = {A user-centric entity detection system is one in which the primary consumer of the detected entities is a person who can perform actions on the detected entities (e.g. perform a search, view a map, shop, etc.). We contrast this with machine-centric detection systems where the primary consumer of the detected entities is a machine. Machine-centric detection systems typically focus on the quantity of detected entities, measured by precision and recall metrics, with the goal of correctly identifying every single entity in a document.However, the simple precision/recall scores of machine-centric entity detection systems fail to accurately reflect the quality of detected entities in user-centric systems, where users may not necessarily want to "see" every possible entity. We posit that not all of the detected entities in a given piece of text are necessarily relevant to the main topic of the text, nor are they necessarily interesting enough to the user to warrant further action. In fact, presenting all of the detected entities to a user may annoy the user to the point where he decides to turn this capability off completely, an undesirable outcome. Therefore, we propose to measure the quality and utility of user-centric entity detection systems in three core dimensions: the accuracy, the interestingness, and the relevance of the entities it presents to the user. We show that leveraging surrounding context can greatly improve the performance of such systems in all three dimensions by employing novel algorithms for generating a concept vector and for finding concept extensions using search query logs.We extensively evaluate the proposed algorithms within Contextual Shortcuts - a large-scale user-centric entity detection platform - using 1,586 entities detected over 1,519 documents. The results confirm the importance of using context within user-centric entity detection systems, and validate the usefulness of the proposed algorithms by showing how they improve the overall entity detection quality within Contextual Shortcuts.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {691–700},
numpages = {10},
keywords = {contextual shortcuts, entity detection, information extraction, context, content syndication, contextual},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321538,
author = {Prager, John and Luger, Sarah and Chu-Carroll, Jennifer},
title = {Type Nanotheories: A Framework for Term Comparison},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321538},
doi = {10.1145/1321440.1321538},
abstract = {We present in this paper Type Nanotheories (TN), a framework for representing the knowledge necessary for performing similarity comparisons between pairs of terms of the same type. TN itself uses another methodology, namely Support Outcomes, which is also introduced. Many IR and NLP applications use redundancy as a factor to increase confidence, and TN-based comparisons can determine redundancy better than simple string comparisons. Results include a showing of a 14% increase in Confidence-Weighted Score for an end-to-end QA system and an up to 68% improvement over baseline in an answer-key equivalencing experiment.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {701–710},
numpages = {10},
keywords = {term comparison, type systems, question answering},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250809,
author = {Shanahan, James G.},
title = {Session Details: Natural Language II (IR)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250809},
doi = {10.1145/3250809},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321540,
author = {Zhang, Wei and Liu, Shuang and Yu, Clement and Sun, Chaojing and Liu, Fang and Meng, Weiyi},
title = {Recognition and Classification of Noun Phrases in Queries for Effective Retrieval},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321540},
doi = {10.1145/1321440.1321540},
abstract = {It has been shown that using phrases properly in the document retrieval leads to higher retrieval effectiveness. In this paper, we define four types of noun phrases and present an algorithm for recognizing these phrases in queries. The strengths of several existing tools are combined for phrase recognition. Our algorithm is tested using a set of 500 web queries from a query log, and a set of 238 TREC queries. Experimental results show that our algorithm yields high phrase recognition accuracy. We also use a baseline noun phrase recognition algorithm to recognize phrases from the TREC queries. A document retrieval experiment is conducted using the TREC queries (1) without any phrases, (2) with the phrases recognized from a baseline noun phrase recognition algorithm, and (3) with the phrases recognized from our algorithm respectively. The retrieval effectiveness of (3) is better than that of (2), which is better than that of (1). This demonstrates that utilizing phrases in queries does improve the retrieval effectiveness, and better noun phrase recognition yields higher retrieval performance.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {711–720},
numpages = {10},
keywords = {dictionary phrase, information retrieval, complex phrase, simple phrase, feedback, noun phrases, verification, proper noun},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321541,
author = {Lad, Abhimanyu and Yang, Yiming},
title = {Generalizing from Relevance Feedback Using Named Entity Wildcards},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321541},
doi = {10.1145/1321440.1321541},
abstract = {Traditional adaptive filtering systems learn the user's interests in a rather simple way - words from relevant documents are favored in the query model, while words from irrelevant documents are down-weighted. This biases the query model towards specific words seen in the past, causing the system to favor documents containing relevant but redundant information over documents that use previously unseen words to denote new facts about the same news event. This paper proposes news ways of generalizing from relevance feedback by augmenting the traditional bag-of-words query model with named entity wildcards that are anchored in context. The use of wildcards allows generalization beyond specific words, while contextual restrictions limit the wildcard-matching to entities related to the user's query. We test our new approach in a nugget-level adaptive filtering system and evaluate it in terms of both relevance and novelty of the presented information. Our results indicate that higher recall is obtained when lexical terms are generalized using wildcards. However, such wildcards must be anchored to their context to maintain good precision. How the context of a wildcard is represented and matched against a given document also plays a crucial role in the performance of the retrieval system.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {721–730},
numpages = {10},
keywords = {adaptive filtering, relevance feedback, named entities},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321542,
author = {Petkova, Desislava and Croft, W. Bruce},
title = {Proximity-Based Document Representation for Named Entity Retrieval},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321542},
doi = {10.1145/1321440.1321542},
abstract = {One aspect in which retrieving named entities is different from retrieving documents is that the items to be retrieved - persons, locations, organizations - are only indirectly described by documents throughout the collection. Much work has been dedicated to finding references to named entities, in particular to the problems of named entity extraction and disambiguation. However, just as important for retrieval performance is how these snippets of text are combined to build named entity representations.We focus on the TREC expert search task where the goal is to identify people who are knowledgeable on a specific topic. Existing language modeling techniques for expert finding assume that terms and person entities are conditionally independent given a document. We present theoretical and experimental evidence that this simplifying assumption ignores information on how named entities relate to document content. To address this issue, we propose a new document representation which emphasizes text in proximity to entities and thus incorporates sequential information implicit in text. Our experiments demonstrate that the proposed model significantly improves retrieval performance. The main contribution of this work is an effective formal method for explicitly modeling the dependency between the named entities and terms which appear in a document.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {731–740},
numpages = {10},
keywords = {expert finding, ne (named entity) retrieval, language models, proximity kernels},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250810,
author = {Ziviani, Nivio},
title = {Session Details: Indexing (IR)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250810},
doi = {10.1145/3250810},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321544,
author = {Cai, Deng and He, Xiaofei and Zhang, Wei Vivian and Han, Jiawei},
title = {Regularized Locality Preserving Indexing via Spectral Regression},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321544},
doi = {10.1145/1321440.1321544},
abstract = {We consider the problem of document indexing and representation. Recently, Locality Preserving Indexing (LPI) was proposed for learning a compact document subspace. Different from Latent Semantic Indexing (LSI) which is optimal in the sense of global Euclidean structure, LPI is optimal in the sense of local manifold structure. However, LPI is not efficient in time and memory which makes it difficult to be applied to very large data set. Specifically, the computation of LPI involves eigen-decompositions of two dense matrices which is expensive. In this paper, we propose a new algorithm called Regularized Locality Preserving Indexing (RLPI). Benefit from recent progresses on spectral graph analysis, we cast the original LPI algorithm into a regression framework which enable us to avoid eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizers can be naturally incorporated into our algorithm which makes it more flexible. Extensive experimental results show that RLPI obtains similar or better results comparing to LPI and it is significantly faster, which makes it an efficient and effective data preprocessing method for large scale text clustering, classification and retrieval.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {741–750},
numpages = {10},
keywords = {regularized locality preserving indexing, dimensionality reduction, document representation and indexing},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321545,
author = {Guo, Ruijie and Cheng, Xueqi and Xu, Hongbo and Wang, Bin},
title = {Efficient On-Line Index Maintenance for Dynamic Text Collections by Using Dynamic Balancing Tree},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321545},
doi = {10.1145/1321440.1321545},
abstract = {Previous on-line index maintenance strategies are mainly designed for document insertions without considering document deletions. In a truly dynamic search environment, however, documents may be added to and removed from the collection at any point in time. In this paper, we examine issues of on-line index maintenance with support for instantaneous document deletions and insertions. We present a DBT Merge strategy that can dynamically adjust the sequence of sub-index merge operations during index construction, and offers better query processing performance than previous methods, while providing an equivalent level of index maintenance performance when document insertions and deletions exist in parallel. Using experiments on 426 GB of web data we demonstrate the efficiency of our method in practice, showing that on-line index construction for dynamic text collections can be performed efficiently and almost as fast as for growing text collections.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {751–760},
numpages = {10},
keywords = {index maintenance, on-line index, garbage collection, information retrieval, dynamic text collections},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321546,
author = {B\"{u}ttcher, Stefan and Clarke, Charles L. A.},
title = {Index Compression is Good, Especially for Random Access},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321546},
doi = {10.1145/1321440.1321546},
abstract = {Index compression techniques are known to substantially decrease the storage requirements of a text retrieval system. As a side-effect, they may increase its retrieval performance by reducing disk I/O overhead. Despite this advantage, developers sometimes choose to store index data in uncompressed form, in order to not obstruct random access into each index term's postings list.In this paper, we show that index compression does not harm random access performance. In fact, we demonstrate that, in some cases, random access into a term's postings list may be realized more efficiently if the list is stored in compressed form instead of uncompressed. This is regardless of whether the index is stored on disk or in main memory, since both types of storage - hard drives and RAM - do not support efficient random access in the first place.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {761–770},
numpages = {10},
keywords = {random access, main memory, ram, index compression},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321547,
author = {Zhu, Mingjie and Shi, Shuming and Li, Mingjing and Wen, Ji-Rong},
title = {Effective Top-k Computation in Retrieving Structured Documents with Term-Proximity Support},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321547},
doi = {10.1145/1321440.1321547},
abstract = {Modern web search engines are expected to return top-k results efficiently given a query. Although many dynamic index pruning strategies have been proposed for efficient top-k computation, most of them are prone to ignore some especially important factors in ranking functions, e.g. term proximity (the distance relationship between query terms in a document). The inclusion of term proximity breaks the monotonicity of ranking functions and therefore leads to additional challenges for efficient query processing. This paper studies the performance of some existing top-k computation approaches using term-proximity-enabled ranking functions. Our investigation demonstrates that, when term proximity is incorporated into ranking functions, most existing index structures and top-k strategies become quite inefficient. According to our analysis and experimental results, we propose two index structures and their corresponding index pruning strategies: Structured and Hybrid, which performs much better on the new settings. Moreover, the efficiency of index building and maintenance would not be affected too much with the two approaches.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {771–780},
numpages = {10},
keywords = {top-k, hybrid index structure, document structure, term proximity, dynamic index pruning},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250811,
author = {Park, EK},
title = {Session Details: Data Mining (KM)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250811},
doi = {10.1145/3250811},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321549,
author = {Xu, Yue and Li, Yuefeng},
title = {Generating Concise Association Rules},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321549},
doi = {10.1145/1321440.1321549},
abstract = {Association rule mining has made many achievements in the area of knowledge discovery. However, the quality of the extracted association rules is a big concern. One problem with the quality of the extracted association rules is the huge size of the extracted rule set. As a matter of fact, very often tens of thousands of association rules are extracted among which many are redundant thus useless. Mining non-redundant rules is a promising approach to solve this problem. The Min-max exact basis proposed by Pasquier et al [Pasquier05] has showed exciting results by generating only non-redundant rules. In this paper, we first propose a relaxing definition for redundancy under which the Min-max exact basis still contains redundant rules; then we propose a condensed representation called Reliable exact basis for exact association rules. The rules in the Reliable exact basis are not only non-redundant but also more succinct than the rules in Min-max exact basis. We prove that the redundancy eliminated by the Reliable exact basis does not reduce the belief to the Reliable exact basis. The size of the Reliable exact basis is much smaller than that of the Min-max exact basis. Moreover, we prove that all exact association rules can be deduced from the Reliable exact basis. Therefore the Reliable exact basis is a lossless representation of exact association rules. Experimental results show that the Reliable exact basis significantly reduces the number of non-redundant rules.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {781–790},
numpages = {10},
keywords = {redundant association rules, closed itemsets, generators},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321550,
author = {Angiulli, Fabrizio and Fassetti, Fabio},
title = {Very Efficient Mining of Distance-Based Outliers},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321550},
doi = {10.1145/1321440.1321550},
abstract = {In this work a novel algorithm, named DOLPHIN, for detecting distance-based outliers is presented.The proposed algorithm performs only two sequential scans of the dataset. It needs to store into main memory a portion of the dataset, to efficiently search for neighbors and early prune inliers. The strategy pursued by the algorithm allows to keep this portion very small. Both theoretical justification and empirical evidence that the size of the stored data amounts only to a few percent of the dataset are provided.Another important feature of DOLPHIN is that the memory-resident data are indexed by using a suitable proximity search approach. This allows to search for nearest neighbors looking only at a small subset of the main memory stored data.Temporal and spatial cost analysis show that the novel algorithm achieves both near linear CPU and I/O cost. DOLPHIN has been compared with state of the art methods, showing that it outperforms existing ones.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {791–800},
numpages = {10},
keywords = {data mining, distance-based outliers, anomaly detection},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321551,
author = {Park, Nam Hun and Lee, Won Suk},
title = {Grid-Based Subspace Clustering over Data Streams},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321551},
doi = {10.1145/1321440.1321551},
abstract = {A real-life data stream usually contains many dimensions and some dimensional values of its data elements may be missing. In order to effectively extract the on-going change of a data stream with respect to all the subsets of the dimensions of the data stream, a grid-based subspace clustering algorithm is proposed in this paper. Given an n-dimensional data stream, the on-going distribution statistics of data elements in each one-dimension data space is firstly monitored by a list of grid-cells called a sibling list. Once a dense grid-cell of a first-level sibling list becomes a dense unit grid-cell, new second-level sibling lists are created as its child nodes in order to trace any cluster in all possible two-dimensional rectangular subspaces. In such a way, a sibling tree grows up to the nth level at most and a k-dimensional subcluster can be found in the kth level of the sibling tree. The proposed method is comparatively analyzed by a series of experiments to identify its various characteristics.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {801–810},
numpages = {10},
keywords = {data mining, grid-based clustering, clustering, adaptive memory utilization, data streams},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321552,
author = {Angiulli, Fabrizio and Fassetti, Fabio},
title = {Detecting Distance-Based Outliers in Streams of Data},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321552},
doi = {10.1145/1321440.1321552},
abstract = {In this work a method for detecting distance-based outliers in data streams is presented. We deal with the sliding window model, where outlier queries are performed in order to detect anomalies in the current window. Two algorithms are presented. The first one exactly answers outlier queries, but has larger space requirements. The second algorithm is directly derived from the exact one, has limited memory requirements and returns an approximate answer based on accurate estimations with a statistical guarantee. Several experiments have been accomplished, confirming the effectiveness of the proposed approach and the high quality of approximate solutions.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {811–820},
numpages = {10},
keywords = {distance-based outliers, data streams, anomaly detection},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250812,
author = {Zaragoza, Hugo},
title = {Session Details: Natural Language III (IR)},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250812},
doi = {10.1145/3250812},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321554,
author = {Feng, Ao and Allan, James},
title = {Finding and Linking Incidents in News},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321554},
doi = {10.1145/1321440.1321554},
abstract = {News reports are being produced and disseminated in overwhelming volume, making it difficult to keep up with the newest information. Most previous research in automatic news organization treated news topics as a flat list, ignoring the intrinsic connection among individual reports. We argue that more contextual information within and across the topics will benefit users in their news understanding process.A news organization infrastructure, incident threading, is proposed in this article. All text snippets describing the occurrence of a real-world happening are combined into a news incident, and a network is composed of incidents that are interconnected by links in certain types. A limited vocabulary of connection types is defined and corresponding rules are established based upon the human experience of news understanding.The incident threading system is implemented with two different algorithms. One starts from clustering of text passages and then creates links with pre-built rules. The other method defines a global score function over the whole collection and solves the optimization problem with simulated annealing. The former achieves higher accuracy in the identification of incidents and the latter generates better links, which is preferred since the links are more important for the formation of the incident network.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {821–830},
numpages = {10},
keywords = {global optimization, automatic news organization, threading rules, simulated annealing, incident threading},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321555,
author = {Zhang, Wei and Yu, Clement and Meng, Weiyi},
title = {Opinion Retrieval from Blogs},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321555},
doi = {10.1145/1321440.1321555},
abstract = {Opinion retrieval is a document retrieval process, which requires documents to be retrieved and ranked according to their opinions about a query topic. A relevant document must satisfy two criteria: relevant to the query topic, and contains opinions about the query, no matter if they are positive or negative. In this paper, we describe an opinion retrieval algorithm. It has a traditional information retrieval (IR) component to find topic relevant documents from a document set, an opinion classification component to find documents having opinions from the results of the IR step, and a component to rank the documents based on their relevance to the query, and their degrees of having opinions about the query. We implemented the algorithm as a working system and tested it using TREC 2006 Blog Track data in automatic title-only runs. Our result showed 28% to 32% improvements in MAP score over the best automatic runs in this 2006 track. Our result is also 13% higher than a state-of-art opinion retrieval system, which is tested on the same data set.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {831–840},
numpages = {10},
keywords = {information retrieval, blog retrieval, trec, opinion retrieval},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321556,
author = {Feuer, Alan and Savev, Stefan and Aslam, Javed A.},
title = {Evaluation of Phrasal Query Suggestions},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321556},
doi = {10.1145/1321440.1321556},
abstract = {This paper evaluates the uptake and efficacy of a unified approach to phrasal query suggestions in the context of a high-precision search engine. The search engine performs ranked extended-Boolean searches with the proximity operator <scp>NEAR</scp> being the default operation. Suggestions are offered to the searcher when the length of the result list falls outside predefined bounds. If the list is too long, the engine suggests narrowing the query through the use of super phrases; if the list is too short, the engine suggests broadening the query through the use of proximal subphrases.We evaluated uptake of phrasal query suggestions by analyzing search log data from before and after the suggestion feature was added to a commercial version of the search engine. We looked at approximately 1.5 million queries and found that, after they were added, suggestions represented nearly 30% of the total queries.We evaluated efficacy through a controlled study of 24 participants performing nine searches using three different search engines. We found that the engine with phrase suggestions had better high-precision recall than both the same search engine without suggestions and a search engine with a similar interface but using an Okapi BM25 ranking algorithm.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {841–848},
numpages = {8},
keywords = {proximity search, web search, query Log analysis, user study},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/3250813,
author = {Laender, Alberto H.F.},
title = {Session Details: Poster Session},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3250813},
doi = {10.1145/3250813},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
numpages = {1},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321558,
author = {Altingovde, Ismail Sengor and Ozcan, Rifat and Cetintas, Suleyman and Yilmaz, Hakan and Ulusoy, \"{O}zg\"{u}r},
title = {An Automatic Approach to Construct Domain-Specific Web Portals},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321558},
doi = {10.1145/1321440.1321558},
abstract = {We describe the architecture of an automatic domain-specific Web portal construction system. The system has three major components: i) a focused crawler that collects the domain-specific pages on the Web, ii) an information extraction engine that extracts useful fields from these Web pages, and iii) a query engine that allows both typical keyword based queries on the pages and advanced queries on the extracted data fields. We present a prototype system that works for the course homepages domain on the Web. A user study with the prototype system shows that our approach produces high quality results and achieves better precision figures than the typical keyword based search.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {849–852},
numpages = {4},
keywords = {querying, information extraction, focused crawling},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321559,
author = {Bache, Richard and Baillie, Mark and Crestani, Fabio},
title = {Language Models, Probability of Relevance and Relevance Likelihood},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321559},
doi = {10.1145/1321440.1321559},
abstract = {This paper proposes a measure of relevance likelihood derived specifically for language models. Such a measure may be used to guide a user on how far to browse through the list of retrieved items or for pseudo-relevance feedback. To derive this measure, it is necessary to make the assumption that a user is seeking an ideal (usually non-existent) document and the actual relevant documents in the collection will contain fragments of this ideal document. Thus, in deriving this measure we propose a novel way of capturing relevance in Language Modelling.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {853–856},
numpages = {4},
keywords = {language modelling, relevance likelihood, ranking function},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321560,
author = {Bast, Holger and Majumdar, Debapriyo and Weber, Ingmar},
title = {Efficient Interactive Query Expansion with Complete Search},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321560},
doi = {10.1145/1321440.1321560},
abstract = {We present an efficient realization of the following interactive search engine feature: as the user is typing the query, words that are related to the last query word and that would lead to good hits are suggested, as well as selected such hits. The realization has three parts: (i) building clusters of related terms, (ii) adding this information as artificial words to the index such that (iii) the described feature reduces to an instance of prefix search and completion. An efficient solution for the latter is provided by the CompleteSearch engine, with which we have integrated the proposed feature. For building the clusters of related terms we propose a variant of latent semantic indexing that, unlike standard approaches, is completely transparent to the user. By experiments on two large test-collections, we demonstrate that the feature is provided at only a slight increase in query processing time and index size.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {857–860},
numpages = {4},
keywords = {wordnet, query expansion, wikipedia, interactive, index building, synsets},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321561,
author = {Bloehdorn, Stephan and Moschitti, Alessandro},
title = {Structure and Semantics for Expressive Text Kernels},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321561},
doi = {10.1145/1321440.1321561},
abstract = {Several Text Categorization applications require a representation beyond the standard bag-of-words paradigm. Kernel-based learning has approached this problem by (i) considering information about syntactic structure or by (ii) incorporating knowledge about the semantic similarity of term features. We propose a generalized framework consisting of a family of kernels that jointly incorporate syntactic and semantic similarity and demonstrate the power of this approach in a series of experiments.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {861–864},
numpages = {4},
keywords = {text classification, machine learning, kernel methods},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321562,
author = {Breitman, Karin K. and Barbosa, Simone D. J. and Casanova, Marco A. and Furtado, Antonio L.},
title = {Conceptual Modeling by Analogy and Metaphor},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321562},
doi = {10.1145/1321440.1321562},
abstract = {Metaphor is not merely a rhetorical device, characteristic of language alone, but rather a fundamental feature of the human conceptual system. A metaphor is understood by finding an analogy mapping between two domains. This paper argues that analogy mappings facilitate conceptual modeling by allowing the designer to reinterpret fragments of familiar conceptual models in other contexts. The contributions of the paper are expressed within the tradition of the Entity-Relation model.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {865–868},
numpages = {4},
keywords = {analogy, metaphor, entity-relationship model},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321563,
author = {Brinkmeier, Michael and Werner, Jeremias and Recknagel, Sven},
title = {Communities in Graphs and Hypergraphs},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321563},
doi = {10.1145/1321440.1321563},
abstract = {In this paper we define a type of cohesive subgroups - called communities - in hypergraphs, based on the edge connectivity of subhypergraphs. We describe a simple algorithm for the construction of these sets and show, based on examples from image segmentation and information retrieval, that these groups may be useful for the analysis and accessibility of large graphs and hypergraphs.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {869–872},
numpages = {4},
keywords = {document collection, components, edge-connectivity, cohesive group, community, graph, image segmentation, hypergraph},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321564,
author = {Carterette, Ben and Allan, James},
title = {Semiautomatic Evaluation of Retrieval Systems Using Document Similarities},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321564},
doi = {10.1145/1321440.1321564},
abstract = {Semiautomatic evaluation of retrieval systems using document similarities.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {873–876},
numpages = {4},
keywords = {evaluation, clustering, information retrieval, test collections},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321565,
author = {Fortuna, Blaz and Rodrigues, Eduarda Mendes and Milic-Frayling, Natasa},
title = {Improving the Classification of Newsgroup Messages through Social Network Analysis},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321565},
doi = {10.1145/1321440.1321565},
abstract = {Improving the classification of newsgroup messages through social network analysis.In this paper, we focus on automatic classification of message replies into several types. For representing messages we consider rich feature sets that combine the standard author reply-to network properties with features derived from four additional structures identified in the data: 1) a network of authors who participate in the same threads, 2) network of authors who post similar content, 3) network of threads sharing common authors, and 4) network of content-related threads.For selected newsgroups we train linear SVM classifiers to identify agreement and disagreement with the original message, and question and answer patterns in the threads. We show that the use of newly defined features substantially improves classification of messages in comparison with the SVM model based only on the standard reply-to network.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {877–880},
numpages = {4},
keywords = {communities, social networks, newsgroups, message classification},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321566,
author = {Fu, Yupeng and Xiang, Rongjing and Liu, Yiqun and Zhang, Min and Ma, Shaoping},
title = {A CDD-Based Formal Model for Expert Finding},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321566},
doi = {10.1145/1321440.1321566},
abstract = {Searching an organization's document repositories for experts is a frequently faced problem in intranet information management. This paper proposes a candidate-centered model which is referred as Candidate Description Document (CDD)-based retrieval model. The expertise evidence about an expert candidate scattered over repositories is mined and aggregated automatically to form a profile called the candidate's CDD, which represents his knowledge. We present the model from its foundations through its logical development and argue in favor of this model for expert finding. We devise and compare the different strategies for exploring a variety of expertise evidence. The experiments on TREC enterprise corpora demonstrate that the CDD-based model achieves significant and consistent improvement on performance through comparative studies with non-CDD methods.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {881–884},
numpages = {4},
keywords = {expertise modeling, knowledge management, expert finding},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321567,
author = {Garakani, Vahid and Izadi, Sayyed Kamyar and Haghjoo, Mostafa and Harizi, Mohammad},
title = {Ntjfsat¬: A Novel Method for Query with Not-Predicates on Xml Data},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321567},
doi = {10.1145/1321440.1321567},
abstract = {Previous researches, in the filed of XML databases, have been done to evaluate XML queries with AND-branches. However, as far as we know, very little work has examined the efficient processing of XML queries with NOT-predicates. Also these methods have to process all of the query nodes in the document when dealing with queries with NOT-branch. In this paper, with some modification in TJFast method, we propose a new manner for answering to various NOT-queries. This method processes nodes efficiently, in a way that in the ideal state, we obtain part of the answer after the process of each node, and we don't have any unreasonable processing of each node.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {885–888},
numpages = {4},
keywords = {tjfast, not-predicates, dewey encoding, xml, twig joins},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321568,
author = {Goldstein, Jade and Ciany, Gary M. and Carbonell, Jaime G.},
title = {Genre Identification and Goal-Focused Summarization},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321568},
doi = {10.1145/1321440.1321568},
abstract = {In this paper, we present a novel technique of first performing document genre identification, then utilizing the genre for producing tailored summaries based on a user's information seeking needs - genre oriented goal-focused summarization - such as a plot or opinion summary of a movie review. We create a test corpus to determine genre classification accuracy for 16 genres, and examine performance on various amounts of training data for machine learning algorithms - Random Forests, SVM light and Na\"{\i}ve Bayes. Results show that Random Forests outperforms SVM light and Na\"{\i}ve Bayes. The genre tag is used to inform a downstream summarization engine. We define types of summaries for 7 genres, create a ground truth corpus and analyze the results of genre oriented goal-focused summarization, showing that this type of user based summarization requires different algorithms than the leading sentence baseline which is known to perform well in the case of news articles.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {889–892},
numpages = {4},
keywords = {genre identification, evaluation, data mining, text classification, text categorization, task-based information retrieval, metadata extraction, summarization, machine learning},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321569,
author = {Hlaoua, Lobna and Boughanem, Mohand and Pinel-Sauvagnat, Karen},
title = {Combination of Evidences in Relevance Feedback for Xml Retrieval},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321569},
doi = {10.1145/1321440.1321569},
abstract = {The main objective in XML Retrieval is to select the relevant elements of XML document instead of the whole document. Many open issues appear when considering Relevance Feedback (RF) in XML documents. They are mainly related to the form of XML documents, which mix content and structure information and to the new information granularity. In this paper, a new flexible method of relevance feedback in XML retrieval using two sources of evidence is described. We propose to use the context criterion to select terms to extend the initial query and to use generative structures to express structural constraints. Both approaches are applied in different combined forms. Experiments are carried out with the INEX evaluation campaign and results show the effectiveness of our approach.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {893–896},
numpages = {4},
keywords = {combined evidences, context, structure, relevance feedback, xml retrieval},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321570,
author = {Houle, Michael Edward and Grira, Nizar},
title = {A Correlation-Based Model for Unsupervised Feature Selection},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321570},
doi = {10.1145/1321440.1321570},
abstract = {We propose a new model for feature evaluation and selection that assesses the propensity of the features to support two-set classification. For each item of the data set, the collection of features induce a ranking (ordered list) of the remaining items. The evaluation criterion favors features that result in the most consistent discrimination between relevant and non-relevant items within these ranked lists. The discrimination boundaries within a single list are determined combinatorially, according to the degree of correlation among the relevant sets of its members. The model makes no special assumptions on the nature of the data. A selection heuristic based on the model is also proposed using sequential forward generation, and an experimental comparison is made with other unsupervised feature selection methods.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {897–900},
numpages = {4},
keywords = {clustering, feature selection},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321571,
author = {Hu, Meishan and Sun, Aixin and Lim, Ee-Peng},
title = {Comments-Oriented Blog Summarization by Sentence Extraction},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321571},
doi = {10.1145/1321440.1321571},
abstract = {Much existing research on blogs focused on posts only, ignoring their comments. Our user study conducted on summarizing blog posts, however, showed that reading comments does change one's understanding about blog posts. In this research, we aim to extract representative sentences from a blog post that best represent the topics discussed among its comments. The proposed solution first derives representative words from comments and then selects sentences containing representative words. The representativeness of words is measured using ReQuT (i.e., Reader, Quotation, and Topic). Evaluated on human labeled sentences, ReQuT together with summation-based sentence selection showed promising results.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {901–904},
numpages = {4},
keywords = {blog, requt, sentence selection, comments},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321572,
author = {Jiang, Fianny Ming-fei and Pei, Jian and Fu, Ada Wai-chee},
title = {Ix-Cubes: Iceberg Cubes for Data Warehousing and Olap on Xml Data},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321572},
doi = {10.1145/1321440.1321572},
abstract = {With increasing amount of data being stored in XML format, OLAP queries over these data become important. OLAP queries have been well studied in the relational database systems. However, the evaluation of OLAP queries over XML data is not a trivial extension of the relational solutions, especially when a schema is not available. In this paper, we introduce the IX-cube (Iceberg XML cube) over XML data to tackle the problem. We extend OLAP operations to XML data. We also develop efficient approaches to IX-Cube computation and OLAP query evaluation using IX-cubes.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {905–908},
numpages = {4},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321573,
author = {Jones, Rosie and Kumar, Ravi and Pang, Bo and Tomkins, Andrew},
title = {"I Know What You Did Last Summer": Query Logs and User Privacy},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321573},
doi = {10.1145/1321440.1321573},
abstract = {We investigate the subtle cues to user identity that may be exploited in attacks on the privacy of users in web search query logs. We study the application of simple classifiers to map a sequence of queries into the gender, age, and location of the user issuing the queries. We then show how these classifiers may be carefully combined at multiple granularities to map a sequence of queries into a set of candidate users that is 300-600 times smaller than random chance would allow. We show that this approach remains accurate even after removing personally identifiable information such as names/numbers or limiting the size of the query log.We also present a new attack in which a real-world acquaintance of a user attempts to identify that user in a large query log, using personal information. We show that combinations of small pieces of information about terms a user would probably search for can be highly effective in identifying the sessions of that user.We conclude that known schemes to release even heavily scrubbed query logs that contain session information have significant privacy risks.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {909–914},
numpages = {6},
keywords = {privacy, query log analysis, k-anonymity},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321574,
author = {Jouini, Khaled and Jomier, Genevi\`{e}ve},
title = {Indexing Multiversion Databases},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321574},
doi = {10.1145/1321440.1321574},
abstract = {An efficient management of multiversion data with branched evolution is crucial for many applications. It requires database designers aware of tradeoffs among index structures and policies. This paper defines a framework and an analysis method for understanding the behavior of different indexing policies. Given data and query characteristics the analysis allows determining the most suitable index structure. The analysis is validated by an experimental study.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {915–918},
numpages = {4},
keywords = {performance analysis, branched and multiversion data, comparison, index structures},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321575,
author = {Jurczyk, Pawel and Agichtein, Eugene},
title = {Discovering Authorities in Question Answer Communities by Using Link Analysis},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321575},
doi = {10.1145/1321440.1321575},
abstract = {Question-Answer portals such as Naver and Yahoo! Answers are quickly becoming rich sources of knowledge on many topics which are not well served by general web search engines. Unfortunately, the quality of the submitted answers is uneven, ranging from excellent detailed answers to snappy and insulting remarks or even advertisements for commercial content. Furthermore, user feedback for many topics is sparse, and can be insufficient to reliably identify good answers from the bad ones. Hence, estimating the authority of users is a crucial task for this emerging domain, with potential applications to answer ranking, spam detection, and incentive mechanism design. We present an analysis of the link structure of a general-purpose question answering community to discover authoritative users, and promising experimental results over a dataset of more than 3 million answers from a popular community QA site. We also describe structural differences between question topics that correlate with the success of link analysis for authority discovery.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {919–922},
numpages = {4},
keywords = {question-answer portals, link analysis},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321576,
author = {Kumaran, Giridhar and Allan, James},
title = {Selective User Interaction},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321576},
doi = {10.1145/1321440.1321576},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {923–926},
numpages = {4},
keywords = {query potential, query expansion, user interaction, query relaxation},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321577,
author = {Liu, Feng and Tian, Fengzhan and Zhu, Qiliang},
title = {Ensembling Bayesian Network Structure Learning on Limited Data},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321577},
doi = {10.1145/1321440.1321577},
abstract = {In recent years, Bagging method has been applied to learn Bayesian networks (BNs), especially on limited datasets. However, the BNs learned using Bagging method from limited datasets can be biased towards complex models. We present an efficient approach to produce more accurate BNs from limited datasets. Based on the Markov condition of BN learning, we proposed a novel sampling method, called Root Nodes based Sampling (RNS), and a BNs fusion method. The experimental results reveal that our ensemble method can achieve more accurate results in terms of accuracy on limited datasets.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {927–930},
numpages = {4},
keywords = {fusion method, ensemble method, bayesian network, sampling method},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321578,
author = {Lv, Jianming and Cheng, Xueqi},
title = {CTO: Concept Tree Based Semantic Overlay for Pure Peer-to-Peer Information Retrieval},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321578},
doi = {10.1145/1321440.1321578},
abstract = {Inspired by how search behavior works in human society, we propose CTO, a self-organized semantic overlay based on concept tree for P2P IR infrastructure, which is efficient for full text search in pure P2P environment without any central control or powerful peer as hub node. Especially, CTO performs very well on searching the unpopular resources shared by a few peers. In our experiment, while searching for the scarce documents shared by the peers, CTO achieves about 80% recall rate when the search covers less than 5% peers in the overlay. The search latency of CTO is also very low, which is controlled in the range about 5~12 hops.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {931–934},
numpages = {4},
keywords = {concept tree, keyword co-occurrences, peer clustering, p2p ir},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321579,
author = {Marin, Mauricio and Gil-Costa, Veronica},
title = {High-Performance Distributed Inverted Files},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321579},
doi = {10.1145/1321440.1321579},
abstract = {We present a general method of parallel query processing that allows scalable performance on distributed inverted files. The method allows the realization of a hybrid that combines the advantages of the document and term partitioned inverted files.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {935–938},
numpages = {4},
keywords = {parallel and distributed computing, inverted files},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321580,
author = {Namata, Galileo Mark and Staats, Brian and Getoor, Lise and Shneiderman, Ben},
title = {A Dual-View Approach to Interactive Network Visualization},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321580},
doi = {10.1145/1321440.1321580},
abstract = {Visualizing network data, from tree structures to arbitrarily connected graphs, is a difficult problem in information visualization. A large part of the problem is that in network data, users not only have to visualize the attributes specific to each data item, but also the links specifying how those items are connected to each other. Past approaches to resolving these difficulties focus on zooming, clustering, filtering and applying various methods of laying out nodes and edges. Such approaches, however, focus only on optimizing a network visualization in a single view, limiting the amount of information that can be shown and explored in parallel. Moreover, past approaches do not allow users to cross reference different subsets or aspects of large, complex networks. In this paper, we propose an approach to these limitations using multiple coordinated views of a given network. To illustrate our approach, we implement a tool called DualNet and evaluate the tool with a case study using an email communication network. We show how using multiple coordinated views improves navigation and provides insight into large networks with multiple node and link properties and types.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {939–942},
numpages = {4},
keywords = {information visualization, interactive graph visualization, social networks, coordinated views},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321581,
author = {Pera, Maria S. and Ng, Yiu-Kai},
title = {Using Word Similarity to Eradicate Junk Emails},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321581},
doi = {10.1145/1321440.1321581},
abstract = {Emails are one of the most commonly used modern communication media these days; however, unsolicited emails obstruct this otherwise fast and convenient technology for information exchange and jeopardize the continuity of this popular communication tool. Waste of valuable resources and time and exposure to offensive content are only a few of the problems that arise as a result of junk emails. In addition, the monetary cost of processing junk emails reaches billions of dollars per year and is absorbed by public users and Internet service providers. Even though there has been extensive work in the past dedicated to eradicate junk emails, none of the existing junk email detection approaches has been highly successful in solving these problems, since spammers have been able to infiltrate existing detection techniques. In this paper, we present a new tool, JunEX, which relies on the content similarity of emails to eradicate junk emails. JunEX compares each incoming email to a core of emails marked as junk by each individual user to identify unwanted emails while reducing the number of legitimate emails treated as junk, which is critical. Conducted experiments on JunEX verify its high accuracy.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {943–946},
numpages = {4},
keywords = {word correlation, similarity detection, junk email},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321582,
author = {Quian\'{e}-Ruiz, Jorge-Arnulfo and Lamarre, Philippe and Cazalens, Sylvie and Valduriez, Patrick},
title = {Satisfaction Balanced Mediation},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321582},
doi = {10.1145/1321440.1321582},
abstract = {We consider a distributed information system that allows autonomous consumers to query autonomous providers. We focus on the problem of query allocation from a new point of view, by considering consumers and providers' satisfaction in addition to query load. We define satisfaction as a long-run notion based on the consumers and providers' preferences. We propose and validate a mediation process, called SBMediation, which is compared to Capacity based query allocation. The experimental results show that SBMediation significantly outperforms Capacity based when confronted to autonomous participants.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {947–950},
numpages = {4},
keywords = {participants' satisfaction, imposition, query allocation, autonomous participants, payment},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321583,
author = {Raghuveer, Aravindan and Jindal, Meera and Mokbel, Mohamed F. and Debnath, Biplob and Du, David},
title = {Towards Efficient Search on Unstructured Data: An Intelligent-Storage Approach},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321583},
doi = {10.1145/1321440.1321583},
abstract = {Applications that create and consume unstructured data have grown both in scale of storage requirements and complexity of search primitives. We consider two such applications: exhaustive search and integration of structured and unstructured data. Current block-based storage systems are either incapable or inefficient to address the challenges bought forth by the above applications. We propose a storage framework to efficiently store and search unstructured and structured data while controlling storage management costs. Experimental results based on our prototype show that the proposed system can provide impressive performance and feature benefits.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {951–954},
numpages = {4},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321584,
author = {Rull, Guillem and Farr\'{e}, Carles and Teniente, Ernest and Urp\'{\i}, Toni},
title = {Computing Explanations for Unlively Queries in Databases},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321584},
doi = {10.1145/1321440.1321584},
abstract = {A query is unlively if it always returns an empty answer. Debugging a database schema requires not only determining unlively queries, but also fixing them. To the best of our knowledge, the existing methods do not provide the designer with an explanation of why a query is not lively. In this paper, we propose a method for computing explanations that is independent of the particular method used to determine liveliness. It provides three levels of search: one explanation, a maximal set of non-overlapping explanations, and all explanations. The first two levels require only a linear number of calls to the underlying method. We also propose a filter to reduce the number of these calls, and experimentally compare our method with the best known method for finding unsatisfiable subsets of constraints.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {955–958},
numpages = {4},
keywords = {query liveliness, database schema validation},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321585,
author = {Sarmento, Luis and Jijkuon, Valentin and de Rijke, Maarten and Oliveira, Eugenio},
title = {"More like These": Growing Entity Classes from Seeds},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321585},
doi = {10.1145/1321440.1321585},
abstract = {We present a corpus-based approach to the class expansion task. For a given set of seed entities we use co-occurrence statistics taken from a text collection to define a membership function that is used to rank candidate entities for inclusion in the set. We describe an evaluation framework that uses data from Wikipedia. The performance of our class extension method improves as the size of the text collection increases.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {959–962},
numpages = {4},
keywords = {list expansion, lexical acquisition},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321586,
author = {Shin, Se Jung and Lee, Won Suk},
title = {An On-Line Interactive Method for Finding Association Rules Data Streams},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321586},
doi = {10.1145/1321440.1321586},
abstract = {In order to trace the changes of association rules over an online data stream efficiently, this paper proposes two different methods of generating all association rules directly over the changing set of currently frequent itemsets. While all of the currently frequent itemsets are monitored by the estDec method, all the association rules of every frequent itemset in the prefix tree of the estDec method are generated. For this purpose, a traversal stack is introduced to efficiently enumerate all association rules. These online methods can avoid the drawbacks of the conventional two-step approach. In an on-line environment, a user may be interested in finding those association rules whose antecedents or consequents are fixed to be a specific itemset. Since generating all the association rules may take too long to produce them timely, two additional methods, namely Assoc-X and Assoc-Y, are introduced. Finally, the proposed methods are compared by a series of experiments to identify their various characteristics.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {963–966},
numpages = {4},
keywords = {frequent itemsets, data streams, data mining, association rules},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321587,
author = {Song, Shaoxu and Chen, Lei},
title = {Probabilistic Correlation-Based Similarity Measure of Unstructured Records},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321587},
doi = {10.1145/1321440.1321587},
abstract = {Computing the similarity between unstructured records is a fundamental function in multiple applications. Approximate string matching and full text retrieval techniques do not show the best performance when applied directly, since the information are limited in unstructured records of short record length. In this paper, we propose a novel probabilistic correlation-based similarity measure. Rather than simply conducting the exact matching tokens of two records, our similarity evaluation enriches the information of records by considering the correlations of tokens. We define the probabilistic correlation between tokens as the probability that these tokens appear in the same records. Then we compute the weight of tokens and discover the correlations of records based on the probabilistic correlations of tokens. Finally, we present extensive experimental results to demonstrate the effectiveness of our approach.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {967–970},
numpages = {4},
keywords = {probabilistic correlation, record similarity},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321588,
author = {Song, Xiaodan and Chi, Yun and Hino, Koji and Tseng, Belle},
title = {Identifying Opinion Leaders in the Blogosphere},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321588},
doi = {10.1145/1321440.1321588},
abstract = {Opinion leaders are those who bring in new information, ideas, and opinions, then disseminate them down to the masses, and thus influence the opinions and decisions of others by a fashion of word of mouth. Opinion leaders capture the most representative opinions in the social network, and consequently are important for understanding the massive and complex blogosphere. In this paper, we propose a novel algorithm called InfluenceRank to identify opinion leaders in the blogosphere. The InfluenceRank algorithm ranks blogs according to not only how important they are as compared to other blogs, but also how novel the information they can contribute to the network. Experimental results indicate that our proposed algorithm is effective in identifying influential opinion leaders.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {971–974},
numpages = {4},
keywords = {network summarization, blog ranking, opinion leader},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321589,
author = {Song, Yangqiu and Zhang, Bin and Yin, Wenjun and Zhang, Changshui and Dong, Jin},
title = {Ranking with Semi-Supervised Distance Metric Learning and Its Application to Housing Potential Estimation},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321589},
doi = {10.1145/1321440.1321589},
abstract = {This paper proposes a semi-supervised distance metric learning algorithm for the ranking problem. Instead of giving the computer what are the important factors that affect the final rank value, we only give several most certainly ranked points which implicitly contain the knowledge of the ranking factors. Then the computer can automatically use the most certain points and plenty of unlabeded data to learn an informative metric for ranking. This metric not only can help to regress an order in the observed data, but also can be used to retrieve the data by querying new test points. Moreover, the lower-rank distance metric can be used to visualize high-dimensional data. We also present an application to the housing potential estimation problem. It is shown that the algorithm is efficient to help consultants to refine their consulting work.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {975–978},
numpages = {4},
keywords = {dimensionality reduction, information retrieval, ranking, metric learning, semi-supervised learning},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321590,
author = {Tan, Songbo and Wu, Gaowei and Tang, Huifeng and Cheng, Xueqi},
title = {A Novel Scheme for Domain-Transfer Problem in the Context of Sentiment Analysis},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321590},
doi = {10.1145/1321440.1321590},
abstract = {In this work, we attempt to tackle domain-transfer problem by combining old-domain labeled examples with new-domain unlabeled ones. The basic idea is to use old-domain-trained classifier to label some informative unlabeled examples in new domain, and retrain the base classifier over these selected examples. The experimental results demonstrate that proposed scheme can significantly boost the accuracy of the base sentiment classifier on new domain.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {979–982},
numpages = {4},
keywords = {opinion mining, information retrieval, sentiment classification},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321591,
author = {Trotman, Andrew and Subramanya, Vikram},
title = {Sigma Encoded Inverted Files},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321591},
doi = {10.1145/1321440.1321591},
abstract = {Compression of term frequency lists and very long document-id lists within an inverted file search engine are examined. Several compression schemes are compared including Elias γ and δ codes, Golomb Encoding, Variable Byte Encoding, and a class of word-based encoding schemes including Simple-9, Relative-10 and Carryover-12. It is shown that these compression methods are not well suited to compressing these kinds of lists of numbers. Of those tested, Carryover-12 is preferred because it is both effective at compression and fast at decompression.A novel technique, Sigma Encoding prior to compression, is proposed and tested. Sigma Encoding utilizes a parameterized dictionary to reduce the number of bits necessary to store an integer. This method shows an about 0.3 bit per integer improvement over Carryover-12 while costing only about 3 extra clock cycles per integer to decompress.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {983–986},
numpages = {4},
keywords = {compression, inverted files},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321592,
author = {Tsegay, Yohannes and Turpin, Andrew and Zobel, Justin},
title = {Dynamic Index Pruning for Effective Caching},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321592},
doi = {10.1145/1321440.1321592},
abstract = {RAM and dynamic pruning schemes to reduce query evaluation times. While only a small portion of lists are processed with dynamic pruning, current systems still store the entire inverted list in cache. In this paper we investigate caching only the pieces of the inverted lists that are actually used to answer a query during dynamic pruning. We examine an LRU cache model, and two recently proposed models. We also introduce a new dynamic pruning scheme for impact-ordered inverted lists.Using two large web collections and corresponding query logs we show that, using an LRU cache, our new pruning scheme reduces the number of disk accesses during query processing time by 7%-15% over the state-of-the-art impact-ordered baseline, without reducing answer quality. Surprisingly, however, we discover that using our new pruning scheme makes little difference to disk traffic when the more sophisticated caching schemes are employed.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {987–990},
numpages = {4},
keywords = {inverted lists, search engines, caching, efficiency},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321593,
author = {Wang, Xuanhui and Fang, Hui and Zhai, ChengXiang},
title = {Improve Retrieval Accuracy for Difficult Queries Using Negative Feedback},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321593},
doi = {10.1145/1321440.1321593},
abstract = {How to improve search accuracy for difficult topics is an under-addressed, yet important research question. In this paper, we consider a scenario when the search results are so poor that none of the top-ranked documents is relevant to a user's query, and propose to exploit negative feedback to improve retrieval accuracy for such difficult queries. Specifically, we propose to learn from a certain number of top-ranked non-relevant documents to rerank the rest unseen documents. We propose several approaches to penalizing the documents that are similar to the known non-relevant documents in the language modeling framework. To evaluate the proposed methods, we adapt standard TREC collections to construct a test collection containing only difficult queries. Experiment results show that the proposed approaches are effective for improving retrieval accuracy of difficult queries.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {991–994},
numpages = {4},
keywords = {difficult queries, negative feedback, language modeling},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321594,
author = {Wang, Yong and Gong, Shaogang},
title = {Translating Topics to Words for Image Annotation},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321594},
doi = {10.1145/1321440.1321594},
abstract = {One of the classic techniques for image annotation is the language translation model. It views an image as a document, i.e., a set of visual words which are obtained by vector quatitizing the image regions generated by unsupervised image segmentation. Annotating images are achieved by translating visual words to textual words, just like translating a document in English to a document in French. In this paper, we also view an image as a document, but we view the annotation processes as two consecutive processes, i.e., document summarization and translation. In the document summarization process, an image document is firstly summarized into its own visual language, which we called visual topics. The translation process translates these visual topics to textual words. Compared to the original translation model, our visual topics learned by the probabilistic latent semantic analysis (PLSA) approach provide an intermediate abstract level of visual description. We show improved annotation performance on the Corel image dataset.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {995–998},
numpages = {4},
keywords = {translation model, probabilistic latent sematic analysis, automatic image annotation},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inbook{10.1145/1321440.1321595,
author = {Wu, Youzheng and Hu, Xinhui and Kashioka, Hideki},
title = {Mining Redundancy in Candidate-Bearing Snippets to Improve Web Question Answering},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321595},
abstract = {Conventional question answering (QA) techniques independently process candidate-bearing snippets to select an exact answer to a question from candidate answers. This paper presents two novel ways of utilizing redundancy in candidate-bearing snippets to help select an exact answer to a question in our Web QA system, i.e., cluster-based language model (CLM-M) and unsupervised SVM classifier (U-SVM) techniques. The comparative experiments demonstrate that the proposed methods significantly outperform the language model-based (LM-M) and supervised SVM-based (S-SVM) techniques that do not utilize this redundancy in the candidate-bearing snippets. Using the CLM-M, the top_1 score is increased from 36.03% (LM-M) to 46.96%; and the top_1 improvement in the U-SVM over the S-SVM is about 23%. Moreover, a cross-model comparison shows that the performance ranking of these models is: U-SVM > CLM-LM > LM-M > S-SVM > R-M (the retrieval-based model).},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {999–1002},
numpages = {4}
}

@inproceedings{10.1145/1321440.1321596,
author = {Xu, Shengliang and Bao, Shenghua and Cao, Yunbo and Yu, Yong},
title = {Using Social Annotations to Improve Language Model for Information Retrieval},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321596},
doi = {10.1145/1321440.1321596},
abstract = {This poster is concerned with the problem of exploring the use of social annotations for improving language models for information retrieval (denoted as LMIR). Two properties of social annotations, namely keyword property and structure property are studied for this aim. The keyword property improves LMIR by concatenating all the annotations of a document to generate a summary of the document. The structure property can boost LMIR further when similarity among annotations and similarity among documents are taken into consideration simultaneously. The two properties of social annotations are leveraged for the use of language modeling with a mixture model named as "Language Annotation Model" (denoted as LAM). Evaluations using del.icio.us data show that LAM outperforms the traditional LMIR approaches significantly.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {1003–1006},
numpages = {4},
keywords = {information retrieval, social annotation, language model},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321597,
author = {Xu, Yu and Papakonstantinou, Yannis},
title = {Efficient LCA Based Keyword Search in Xml Data},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321597},
doi = {10.1145/1321440.1321597},
abstract = {Keyword search in XML documents based on the notion of lowest common ancestors (LCAs) and modifications of it has recently gained research interest [2, 3, 4]. In this paper we propose an efficient algorithm called Indexed Stack to find answers to keyword queries based on XRank's semantics to LCA [2]. The complexity of the Indexed Stack algorithm is O(kd|S1|log|S|) where k is the number of keywords in the query, d is the depth of the tree and |S1 | (|S|) is the occurrence of the least (most) frequent keyword in the query. In comparison, the best worst case complexity of the core algorithms in [2] is O(kd|S|). We analytically and experimentally evaluate the Indexed Stack algorithm and the two core algorithms in [2]. The results show that the Indexed Stack algorithm outperforms in terms of both CPU and I/O costs other algorithms by orders of magnitude when the query contains at least one low frequency keyword along with high frequency keywords.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {1007–1010},
numpages = {4},
keywords = {search, xml, LCA, keyword},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321598,
author = {Yang, Lei and Qi, Lei and Zhao, Yan-Ping and Gao, Bin and Liu, Tie-Yan},
title = {Link Analysis Using Time Series of Web Graphs},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321598},
doi = {10.1145/1321440.1321598},
abstract = {Link analysis is a key technology in contemporary web search engines. Most of the previous work on link analysis only used information from one snapshot of web graph. Since commercial search engines crawl the Web periodically, they will naturally obtain time series data of web graphs. The historical information contained in the series of web graphs can be used to improve the performance of link analysis. In this paper, we argue that page importance should be a dynamic quantity, and propose defining page importance as a function of both PageRank of the current web graph and accumulated historical page importance from previous web graphs. Specifically, a novel algorithm named TemporalRank is designed to compute the proposed page importance. We try to use a kinetic model to interpret this page importance and show that it can be regarded as the solution to an ordinary differential equation. Experiments on link analysis using web graph data in five snapshots show that the proposed algorithm can outperform PageRank in many measures, and can effectively filter out newly appeared link spam websites.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {1011–1014},
numpages = {4},
keywords = {page importance, search engine, pagerank, link analysis, temporal information},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321599,
author = {Zaragoza, Hugo and Rode, Henning and Mika, Peter and Atserias, Jordi and Ciaramita, Massimiliano and Attardi, Giuseppe},
title = {Ranking Very Many Typed Entities on Wikipedia},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321599},
doi = {10.1145/1321440.1321599},
abstract = {We discuss the problem of ranking very many entities of different types. In particular we deal with a heterogeneous set of types, some being very generic and some very specific. We discuss two approaches for this problem: i) exploiting the entity containment graph and ii) using a Web search engine to compute entity relevance. We evaluate these approaches on the real task of ranking Wikipedia entities typed with a state-of-the-art named-entity tagger. Results show that both approaches can greatly increase the performance of methods based only on passage retrieval.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {1015–1018},
numpages = {4},
keywords = {entity retrieval, wikipedia, entity graphs},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321600,
author = {Zhang, Duo and Tang, Jie and Li, Juanzi and Wang, Kehong},
title = {A Constraint-Based Probabilistic Framework for Name Disambiguation},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321600},
doi = {10.1145/1321440.1321600},
abstract = {This paper is concerned with the problem of name disambiguation. By name disambiguation, we mean distinguishing persons with the same name. It is a critical problem in many knowledge management applications. Despite much research work has been conducted, the problem is still not resolved and becomes even more serious, in particular with the popularity of Web 2.0. Previously, name disambiguation was often undertaken in either a supervised or unsupervised fashion. This paper first gives a constraint-based probabilistic model for semi-supervised name disambiguation. Specifically, we focus on investigating the problem in an academic researcher social network (http://arnetminer.org). The framework combines constraints and Euclidean distance learning, and allows the user to refine the disambiguation results. Experimental results on the researcher social network show that the proposed framework significantly outperforms the baseline method using unsupervised hierarchical clustering algorithm.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {1019–1022},
numpages = {4},
keywords = {digital library, name disambiguation, social network analysis, semi-supervised clustering},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321601,
author = {Zhang, Qi and Wang, Wei},
title = {An Efficient Algorithm for Approximate Biased Quantile Computation in Data Streams},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321601},
doi = {10.1145/1321440.1321601},
abstract = {We propose an efficient algorithm for approximate biased quantile computation in large data streams. Our algorithm computes decomposable biased quantile summaries on fixed sized blocks and dynamically maintains the biased quantile summary for the entire stream as the exponential histogram over the block-wise quantile summaries. The algorithm is computationally efficient and achieves an amortized computational cost of O(log(1⁄∈log(∈n))) and a space requirement of O(log3∈n↬∈). Our algorithm does not assume prior knowledge of the stream sizes or the range of data values in the streams. In practice, our algorithm is able to efficiently maintain summaries over large data streams with over tens of millions of observations and achieves significant performance improvement over prior algorithms.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {1023–1026},
numpages = {4},
keywords = {streaming algorithms, biased quantiles},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

@inproceedings{10.1145/1321440.1321602,
author = {Zhou, Xiaohua and Hu, Xiaohua and Zhang, Xiaodan and Shen, Xiajiong},
title = {A Segment-Based Hidden Markov Model for Real-Setting Pinyin-to-Chinese Conversion},
year = {2007},
isbn = {9781595938039},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1321440.1321602},
doi = {10.1145/1321440.1321602},
abstract = {Hidden markov model (HMM) is frequently used for Pinyin-to-Chinese conversion. But it only captures the dependency with the preceding character. Higher order markov models can bring higher accuracy, but are computationally unaffordable to average PC settings. We propose a segment-based hidden markov model (SHMM), which has the same magnitude of complexity as first-order HMM, but generates higher decoding accuracy. SHMM tells a word from a bigram connecting two words, and assigns a reasonable probability to words as a whole. It is more powerful than HMM to decode words containing over two characters. We conduct a comprehensive Pinyin-to-Chinese conversion evaluation on Lancaster corpus. The experiment shows the perfect sentence accuracy is improved from 34.7% (HMM) to 43.3% (SHMM). The one-error sentence accuracy is increased from 72.7% to 78.3%. Furthermore, SHMM can seamlessly integrate with pinyin typing correction, acronym pinyin input, user-defined words, and self-adaptive learning all of which are a must for a commercial Pinyin-to-Chinese conversion product in order to improve the efficiency of pinyin input.},
booktitle = {Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management},
pages = {1027–1030},
numpages = {4},
keywords = {chinese input, segment-based hidden markov model, pinyin},
location = {Lisbon, Portugal},
series = {CIKM '07}
}

license_type: permissive

extension: bib