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% Encoding: UTF-8

@Conference{lee_yong_2007a,
  author        = {Lee, S.-S. and Yong, H.-S.},
  title         = {Component based approach to handle synonym and polysemy in folksonomy},
  year          = {2007},
  pages         = {200-205},
  note          = {cited By},
  abstract      = {Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. In this paper, we first discuss current limitations of tagging services, second, we give an overview of existing approaches. We then describe the system on component architecture that we have implemented to minimize ambiguity due to no synonym and polysemy control in folksonomy. Finally, we give experimental results. © 2007 IEEE.},
  affiliation   = {Department of Computer Science and Engineering, Ewha Womans University, Seoul, South Korea},
  art_number    = {4385081},
  document_type = {Conference Paper},
  doi           = {10.1109/CIT.2007.4385081},
  journal       = {CIT 2007: 7th IEEE International Conference on Computer and Information Technology},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-38049005933&doi=10.1109%2fCIT.2007.4385081&partnerID=40&md5=cfb156cf373edec0466acdd6936c7729},
}

@Conference{yeung_etal_2007,
  author        = {Yeung, C.-M.A. and Gibbins, N. and Shadbolt, N.},
  title         = {Tag meaning disambiguation through analysis of tripartite structure of folksonomies},
  year          = {2007},
  pages         = {3-6},
  note          = {cited By},
  abstract      = {Collaborative tagging systems are becoming very popular recently. Web users use freely-chosen tags to describe shared resources, resulting in a folksonomy. One problem offolksonomies is that tags which appear in the same form may carry multiple meanings and represent different concepts. As this kind of tags are ambiguous, the precisions in both description and retrieval of the shared resources are reduced. We attempt to develop effective methods to disambiguate tags by studying the tripartite structure of folksonomies. This paper describes the network analysis techniques that we employ to discover clusters of nodes in networks and the algorithm for tag disambiguation. Experiments show that the method is very effective in performing the task. © 2007 IEEE.},
  affiliation   = {Intelligence, Agents and Multimedia Group, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, United Kingdom},
  art_number    = {4427527},
  document_type = {Conference Paper},
  doi           = {10.1109/WIIATW.2007.4427527},
  journal       = {Proceedings - 2007 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2007},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-48149087100&doi=10.1109%2fWIIATW.2007.4427527&partnerID=40&md5=b987817c9be81de9d83696122225dc64},
}

@Conference{lee_yong_2007b,
  author        = {Lee, S.-S. and Yong, H.-S.},
  title         = {TagPlus: A retrieval system using synonym tag in folksonomy},
  booktitle     = {2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07)},
  year          = {2007},
  pages         = {294-298},
  month         = {April},
  note          = {cited By},
  abstract      = {Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs, videos and other content. In ubiquitous computing environment, users access data through various kinds of mobile terminals. Therefore users want more accurate materials because of expensive communication cost or the useless results due to abuse of tags. In this paper, we first describe current limitation of tagging services. We then describe the system (TagPlus) we implemented to minimize ambiguity due to no synonym control. Finally, we give experimental results. © 2007 IEEE.},
  affiliation   = {Ewha Womans University, South Korea},
  art_number    = {4197289},
  document_type = {Conference Paper},
  doi           = {10.1109/MUE.2007.201},
  journal       = {Proceedings - 2007 International Conference on Multimedia and Ubiquitous Engineering, MUE 2007},
  keywords      = {groupware;information analysis;information retrieval systems;ubiquitous computing;TagPlus;collaborative tagging;folksonomy;retrieval system;synonym tag;ubiquitous computing;Collaboration;Communication system control;Content based retrieval;Costs;Mobile communication;Mobile computing;Tagging;Ubiquitous computing;Videos;Vocabulary},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-37349096927&doi=10.1109%2fMUE.2007.201&partnerID=40&md5=7b7a331af2dc61ae86d9620e16d69446},
}

@InProceedings{limpens_etal_2008,
  author    = {F. Limpens and F. Gandon and M. Buffa},
  title     = {Bridging ontologies and folksonomies to leverage knowledge sharing on the social Web: A brief survey},
  booktitle = {2008 23rd IEEE/ACM International Conference on Automated Software Engineering - Workshops},
  year      = {2008},
  pages     = {13-18},
  month     = {Sept},
  abstract  = {Social tagging systems have recently became very popular as a means to classify large sets of resources shared among on-line communities over the social Web. However, the folksonomies resulting from the use of these systems revealed limitations : tags are ambiguous and their spelling may vary, and folksonomies are difficult to exploit in order to retrieve or exchange information. This article compares the recent attempts to overcome these limitations and to support the use of folksonomies with formal languages and ontologies from the Semantic Web.},
  doi       = {10.1109/ASEW.2008.4686305},
  issn      = {2151-0830},
  keywords  = {formal languages;ontologies (artificial intelligence);semantic Web;social networking (online);folksonomies;formal languages;leverage knowledge sharing;ontologies;semantic Web;social Web;social tagging systems;Collaborative software;Collaborative work;Costs;Formal languages;Information analysis;Information retrieval;Knowledge representation;Ontologies;Semantic Web;Tagging},
}

@Conference{kangpyo_etal_2008,
  author          = {Kangpyo, L. and Hyunwoo, K. and Chungsu, J. and Kim, H.-J.},
  title           = {FolksoViz: A subsumption-based folksonomy visualization using wikipedia texts},
  year            = {2008},
  pages           = {1093-1094},
  note            = {cited By},
  abstract        = {In this paper, targeting del.icio.us tag data, we propose a method, FolksoViz, for deriving subsumption relationships between tags by using Wikipedia texts, and visualizing a folksonomy. To fulfill this method, we propose a statistical model for deriving subsumption relationships based on the frequency of each tag on the Wikipedia texts, as well as the TSD (Tag Sense Disambiguation) method for mapping each tag to a corresponding Wikipedia text. The derived subsumption pairs are visualized effectively on the screen. The experiment shows that the FolksoViz manages to find the correct subsumption pairs with high accuracy.},
  affiliation     = {School of Computer Science and Engineering, Seoul National University, Seoul, South Korea},
  author_keywords = {Collaborative tagging; Folksonomy; Subsumption; Visualization; Web 2.0; Wikipedia},
  document_type   = {Conference Paper},
  doi             = {10.1145/1367497.1367672},
  journal         = {Proceeding of the 17th International Conference on World Wide Web 2008, WWW'08},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-57349160346&doi=10.1145%2f1367497.1367672&partnerID=40&md5=86e9235ccdf094ed0726cbfacc488bc1},
}

@Conference{clements_etal_2008,
  author          = {Clements, M. and De Vries, A.P. and Reinders, M.J.T.},
  title           = {Detecting synonyms in social tagging systems to improve content retrieval},
  year            = {2008},
  pages           = {739-740},
  note            = {cited By},
  abstract        = {Collaborative tagging used in online social content systems is naturally characterized by many synonyms, causing low precision retrieval. We propose a mechanism based on user preference profiles to identify synonyms that can be used to retrieve more relevant documents by expanding the user's query. Using a popular online book catalog we discuss the effectiveness of our method over usual similarity based expansion methods.},
  affiliation     = {Faculty of EEMCS, Delft University of Technology, Netherlands; CWI, Amsterdam, Netherlands},
  author_keywords = {Content retrieval; Social media; Synonymy; Tagging},
  document_type   = {Conference Paper},
  doi             = {10.1145/1390334.1390479},
  journal         = {ACM SIGIR 2008 - 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Proceedings},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-57349198712&doi=10.1145%2f1390334.1390479&partnerID=40&md5=87e4dd476d25cc300680aa9975be00f2},
}

@Article{cattuto_etal_2008a,
  author        = {Cattuto, C. and Benz, D. and Hotho, A. and Stumme, G.},
  title         = {Semantic grounding of tag relatedness in social bookmarking systems},
  journal       = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year          = {2008},
  volume        = {5318 LNCS},
  pages         = {615-631},
  note          = {cited By},
  abstract      = {Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Even though most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptions on the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity in terms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures of tag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding is provided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measures of semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of the investigated similarity measures and indicates which ones are better suited in the context of a given semantic application. © 2008 Springer Berlin Heidelberg.},
  affiliation   = {Complex Networks Lagrange Laboratory, Institute for Scientific Interchange (ISI) Foundation, 10133 Torino, Italy; Knowledge and Data Engineering Group, University of Kassel, 34121 Kassel, Germany},
  document_type = {Conference Paper},
  doi           = {10.1007/978-3-540-88564-1-39},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-57349175019&doi=10.1007%2f978-3-540-88564-1-39&partnerID=40&md5=08d29d65a4162b8b51226788651ab625},
}

@Conference{gemmell_etal_2008,
  author        = {Gemmell, J. and Shepitsen, A. and Mobasher, B. and Burke, R.},
  title         = {Personalization in folksonomies based on tag clustering},
  year          = {2008},
  volume        = {WS-08-06},
  pages         = {37-48},
  note          = {cited By},
  abstract      = {Collaborative tagging systems, sometimes referred to as "folksonomies," enable Internet users to annotate or search for resources using custom labels instead of being restricted by pre-defined navigational or conceptual hierarchies. However, the flexibility of tagging brings with it certain costs. Because users are free to apply any tag to any resource, tagging systems contain large numbers of redundant, ambiguous, and idiosyncratic tags which can render resource discovery difficult. Data mining techniques such as clustering can be used to ameliorate this problem by reducing noise in the data and identifying trends. In particular, discovered patterns can be used to tailor the system's output to a user based on the user's tagging behavior. In this paper, we propose a method to personalize a user's experience within a folksonomy using clustering. A personalized view can overcome ambiguity and idiosyncratic tag assignment, presenting users with tags and resources that correspond more closely to their intent. Specifically, we examine unsupervised clustering methods for extracting commonalities between tags, and use the discovered clusters as intermediaries between a user's profile and resources in order to tailor the results of search to the user's interests. We validate this approach through extensive evaluation of proposed personalization algorithm and the underlying clustering techniques using data from a real collaborative tagging Web site. Copyright © 2008, Association for the Advancement of Artificial Intelligence.},
  affiliation   = {Center for Web Intelligence, School of Computing, DePaul University Chicago, IL, United States},
  document_type = {Conference Paper},
  journal       = {AAAI Workshop - Technical Report},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-66149185437&partnerID=40&md5=ec5ee16ce2ff45564dd28e2eb6a93681},
}

@Conference{nauman_etal_2008,
  author          = {Nauman, M. and Khan, S. and Amin, M. and Hussain, F.},
  title           = {Resolving lexical ambiguities in folksonomy based search systems through common sense and personalization},
  year            = {2008},
  volume          = {334},
  pages           = {2-13},
  note            = {cited By},
  abstract        = {Information on Web2.0, generated by users of web based services, is both difficult to organize and organic in nature. Content categorization and search in such situation offers challenging scenarios. The primary means of content categorization in such social services is folk-sonomy or collaborative tagging. During search in folksonomy, several issues arise due to lexical ambiguities in the way users choose tags to represent content. These are issues of different words representing the same concept, same words representing different concepts and variances in level of expertise of users. Past techniques to address these issues have worked on lexical analysis of term and have thus had only moderate levels of success. We have developed a model in which machine common sense and personalization is used to address these issues. In this paper, we explain our approach in detail, describe a prototype developed for the purpose of demonstrating feasibility of our approach and discuss an effectiveness study conducted to measure the success of our model. The results of the study are analyzed and future directions along this path of research are presented.},
  affiliation     = {Research Group, Security Engineering, Institute of Management Sciences, Pakistan; City University of Science and Information Technology, Peshawar, Pakistan},
  author_keywords = {Common Sense; Folksonomy; Search; Web2.0},
  document_type   = {Conference Paper},
  journal         = {CEUR Workshop Proceedings},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884998408&partnerID=40&md5=ed6463bc96aa4d230b0359b974eab62d},
}

@InProceedings{nguyen_cao_2008,
  author    = {H. T. Nguyen and T. H. Cao},
  title     = {Named entity disambiguation on an ontology enriched by Wikipedia},
  booktitle = {2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies},
  year      = {2008},
  pages     = {247-254},
  month     = {July},
  abstract  = {Currently, for named entity disambiguation, the short-age of training data is a problem. This paper presents a novel method that overcomes this problem by automatically generating an annotated corpus based on a specific ontology. Then the corpus was enriched with new and informative features extracted from Wikipedia data. Moreover, rather than pursuing rule-based methods as in literature, we employ a machine learning model to not only disambiguate but also identify named entities. In addition, our method explores in details the use of a range of features extracted from texts, a given ontology, and Wikipedia data for disambiguation. This paper also systematically analyzes impacts of the features on disambiguation accuracy by varying their combinations for representing named entities. Empirical evaluation shows that, while the ontology provides basic features of named entities, Wikipedia is a fertile source for additional features to construct accurate and robust named entity disambiguation systems.},
  doi       = {10.1109/RIVF.2008.4586363},
  keywords  = {classification;learning (artificial intelligence);ontologies (artificial intelligence);text analysis;Wikipedia;annotated corpus;machine learning;named entity disambiguation;ontology;Cities and towns;Data mining;Feature extraction;Knowledge based systems;Machine learning;Ontologies;Robustness;Social network services;Training data;Wikipedia;annotation;entity disambiguation;knowledge base;named entity;ontology},
}

@InProceedings{chukmol_etal_2008,
  author        = {Chukmol, U. and Benharkat, A.-N. and Amghar, Y.},
  title         = {Enhancing web service discovery by using collaborative tagging system},
  booktitle     = {2008 4th International Conference on Next Generation Web Services Practices},
  year          = {2008},
  pages         = {54-59},
  month         = {Oct},
  note          = {cited By},
  abstract      = {Rapid growth of published Web services makes their discoveries more and more difficult. Many approaches are proposed to solve this discovery issue. Most of them are interested only Web service's description itself and neglect the user-centric aspect of the discovery process. User's involvement may be seen as contributing information, after using or testing a service, which marks the personal opinion on service's functionality, quality or invocation cost. This kind of contribution is perceived as a participative aspect inside the Web 2.0 environment and can enhance the Web service discovery process. We illustrate in this research work, a collaborative tagging-based environment for Web service discovery, allowing users to tag or annotate a Web service using keyword or free-text. Our system proposes consequently two types of query to search tagged Web services: keywordbased and free-text. We put in place an advanced mode in the discovery by keyword which offers different ways to combine keywords together inside a query to make it more flexible and accurate. In both types of look-up (by keyword and by free-text), synonym relation between terms is processed in order to enhance the similarity computing between a query and the tagged web services. The Web services found after a discovery process are ranked according to their tags' weight, in the case of discovery by keywords. © 2008 IEEE.},
  affiliation   = {INSA de Lyon, LIRIS Laboratory - CNRS, Blaise PASCAL Building, 7 Avenue Jean CAPELLE, 69621 Villeurbanne Cedex, France},
  art_number    = {4700381},
  document_type = {Conference Paper},
  doi           = {10.1109/NWeSP.2008.29},
  journal       = {Proceedings - International Conference on Next Generation Web Services Practices, NWeSP 2008},
  keywords      = {Web services;groupware;query formulation;Web 2.0 environment;Web service discovery;collaborative tagging system;free-text query;keyword based query;query construction;tagged Web service search;Collaborative work;Cost function;International collaboration;Laboratories;Logic;Ontologies;Tagging;Testing;Uniform resource locators;Web services;Web service discovery;collaborative tagging},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-67549101206&doi=10.1109%2fNWeSP.2008.29&partnerID=40&md5=c801f44f99886ed8ce23b179a2c7ae5f},
}

@Conference{yeung_etal_2008b,
  author        = {Yeung, C.-M.A. and Gibbins, N. and Shadbolt, N.},
  title         = {Collective user behaviour and tag contextualisation in folksonomies},
  booktitle     = {2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology},
  year          = {2008},
  volume        = {3},
  pages         = {659-662},
  month         = {Dec},
  note          = {cited By},
  abstract      = {Collaborative tagging systems have emerged in recent years to become popular tools for organising information on the Web. While collaborative tagging offers many advantages, they also suffer from several limitations, with a major one being the existence of ambiguous tags. To understand what an ambiguous tag is intended to mean, we need to know the contexts in which it is used. Instead of using common large scale clustering techniques on folksonomies, we believe tags can be better contextualised by the social contexts in which they are used. We propose a method to reveal semantics of ambiguous tags by studying the collective user behaviour in a tagging system. In this paper we describe our proposal and some results of our preliminary experiments. We also discuss the significance of the work and how it can be evaluated. © 2008 IEEE.},
  affiliation   = {Intelligence, Agents, Multimedia Group, School of Electronics, Computer Science University of Southampton, Southampton, SO 17 1BJ, United Kingdom},
  art_number    = {4740865},
  document_type = {Conference Paper},
  doi           = {10.1109/WIIAT.2008.265},
  journal       = {Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008},
  keywords      = {Internet;groupware;information analysis;social networking (online);Internet;ambiguous tag;collaborative tagging system;collective user behaviour;folksonomy;social network;tag contextualisation;Collaborative tools;Computer science;Gold;Intelligent agent;International collaboration;Large-scale systems;Multimedia systems;Proposals;Tagging;Vocabulary;collaborative tagging;contextualisation;folksonomy;user behaviour},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-62949180237&doi=10.1109%2fWIIAT.2008.265&partnerID=40&md5=6114f9fb413ea445b7787bfd2e579597},
}

@Conference{giannakidou_etal_2008,
  author        = {Giannakidou, E. and Kompatsiaris, I. and Vakali, A.},
  title         = {SEMSOC: Semantic, social and content-based clustering in multimedia Collaborative Tagging Systems},
  booktitle     = {2008 IEEE International Conference on Semantic Computing},
  year          = {2008},
  pages         = {128-135},
  month         = {Aug},
  note          = {cited By},
  abstract      = {A huge amount of data and metadata emerges from Web 2.0 applications which have transformed the Web to a mass social interaction and collaboration medium. Collaborative Tagging Systems is a typical, popular and promising Web 2.0 application and despite its adoption it faces some serious limitations that restrict their usability. These limitations (no structure on tags, tags validation, spamming and redundancy) are more evident in the case of multimedia content due to its challenging automatic annotation and retrieval requirements. In this paper, we present an approach for social data clustering which combines jointly semantic, social and content-based information. We propose an unsupervised model for efficient and scalable mining on multimedia social-related data, which leads to the extraction of rich and trustworthy semantics and the improvement of retrieval in a social tagging system. Experimental results demonstrate the efficiency of the proposed approach. © 2008 IEEE.},
  affiliation   = {Aristotle University, Thessaloniki, Greece; Informatics and Telematics Institute, CERTH},
  art_number    = {4597183},
  document_type = {Conference Paper},
  doi           = {10.1109/ICSC.2008.73},
  journal       = {Proceedings - IEEE International Conference on Semantic Computing 2008, ICSC 2008},
  keywords      = {Internet;data analysis;groupware;multimedia computing;semantic Web;SEMSOC;Web 2.0;collaboration medium;content-based clustering;mass social interaction;metadata;multimedia collaborative tagging systems;multimedia content;multimedia social-related data;semantic clustering;social clustering;social data clustering;Content based retrieval;Data mining;Informatics;Information retrieval;International collaboration;Multimedia computing;Multimedia systems;Tagging;Telematics;Usability;clustering;multimedia;semantic;social;tags},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-52349101385&doi=10.1109%2fICSC.2008.73&partnerID=40&md5=c363e8152f89863214c13822ac348a68},
}

@InProceedings{limpens_etal_2009,
  author    = {F. Limpens and F. Gandon and M. Buffa},
  title     = {Collaborative Semantic Structuring of Folksonomies},
  booktitle = {2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology},
  year      = {2009},
  volume    = {1},
  pages     = {132-135},
  month     = {Sept},
  abstract  = {The advent of tagging and folksonomies for organizing shared resources on the social Web brought promising opportunities to help communities of users capture their knowledge. However, the lack of semantics, or the spelling variations between tags lowers the potentials for browsing and exploring these data. To overcome these limitations, we propose exploiting the interactions between the users and the systems to validate or correct semantic analysis automatically applied to the tags. This process is based upon our model of the assistance of folksonomies enrichment which supports conflictual points of view. Several strategies can then be applied to propose novel browsing facilities to users.},
  doi       = {10.1109/WI-IAT.2009.26},
  keywords  = {Collaboration;Collaborative work;Conferences;Intelligent agent;Intelligent structures;Navigation;Ontologies;Power system modeling;Resource description framework;Tagging;Folksonomy;Knowledge Engineering;Ontology},
}

@Conference{gemmell_etal_2009,
  author          = {Gemmell, J. and Ramezani, M. and Schimoler, T. and Christiansen, L. and Mobasher, B.},
  title           = {The impact of ambiguity and redundancy on tag recommendation in folksonomies},
  year            = {2009},
  pages           = {45-52},
  note            = {cited By},
  abstract        = {Collaborative tagging applications have become a popular tool allowing Internet users to manage online resources with tags. Most collaborative tagging applications permit unsupervised tagging resulting in tag ambiguity in which a single tag has many different meanings and tag redundancy in which several tags have the same meaning. Common metrics for evaluating tag recommenders may overestimate the utility of ambiguous tags or ignore the appropriateness of redundant tags. Ambiguity and redundancy may even burden the user with additional effort by requiring them to clarify an annotation or forcing them to distinguish between highly related items. In this paper we demonstrate that ambiguity and redundancy impede the evaluation and performance of tag recommenders. Five tag recommendation strategies based on popularity, collaborative filtering and link analysis are explored. We use a cluster-based approach to define ambiguity and redundancy and provide extensive evaluation on three real world datasets. Copyright 2009 ACM.},
  affiliation     = {Center for Web Intelligence, School of Computing, DePaul University, Chicago, IL, United States},
  author_keywords = {Ambiguity; Folksonomies; Recommender system; Redundancy},
  document_type   = {Conference Paper},
  doi             = {10.1145/1639714.1639724},
  journal         = {RecSys'09 - Proceedings of the 3rd ACM Conference on Recommender Systems},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-72249112534&doi=10.1145%2f1639714.1639724&partnerID=40&md5=14b2704f6a3e9bc1c609cd739371214a},
}

@Conference{yeung_etal_2009,
  author          = {Yeung, C.-M.A. and Gibbins, N. and Shadbolt, N.},
  title           = {Contextualising tags in collaborative tagging systems},
  year            = {2009},
  pages           = {251-260},
  note            = {cited By},
  abstract        = {Collaborative tagging systems are now popular tools for organising and sharing information on the Web. While collaborative tagging offers many advantages over the use of controlled vocabularies, they also suffer from problems such as the existence of polysemous tags. We investigate how the different contexts in which individual tags are used can be revealed automatically without consulting any external resources. We consider several different network representations of tags and documents, and apply a graph clustering algorithm on these networks to obtain groups of tags or documents corresponding to the different meanings of an ambiguous tag. Our experiments show that networks which explicitly take the social context into account are more likely to give a better picture of the semantics of a tag. Copyright 2009 ACM.},
  affiliation     = {School of Electronics and Computer Science, University of Southampton, Intelligence, Agents, Multimedia Group, Southampton, SO17 1BJ, United Kingdom},
  author_keywords = {Collaborative tagging; Context; Folksonomy; Semantics},
  document_type   = {Conference Paper},
  doi             = {10.1145/1557914.1557958},
  journal         = {Proceedings of the 20th ACM Conference on Hypertext and Hypermedia, HT'09},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-70450206270&doi=10.1145%2f1557914.1557958&partnerID=40&md5=15b5931193e56a7d8046b49ca2cb6f24},
}

@InProceedings{lee_etal_2009,
  author    = {K. Lee and H. Kim and H. Shin and H. J. Kim},
  title     = {Tag Sense Disambiguation for Clarifying the Vocabulary of Social Tags},
  booktitle = {2009 International Conference on Computational Science and Engineering},
  year      = {2009},
  volume    = {4},
  pages     = {729-734},
  month     = {Aug},
  abstract  = {Tagging is one of the most popular services in Web 2.0. As a special form of tagging, social tagging is done collaboratively by many users, which forms a so-called folksonomy. As tagging has become widespread on the Web, the tag vocabulary is now very informal, uncontrolled, and personalized. For this reason, many tags are unfamiliar and ambiguous to users so that they fail to understand the meaning of each tag. In this paper, we propose a tag sense disambiguating method, called tag sense disambiguation (TSD), which works in the social tagging environment. TSD can be applied to the vocabulary of social tags, thereby enabling users to understand the meaning of each tag through Wikipedia. To find the correct mappings from del.icio.us tags to Wikipedia articles, we define the local neighbor tags, the global neighbor tags, and finally the neighbor tags that would be the useful key-words for disambiguating the sense of each tag based on the tag co-occurrences. The automatically built mappings are reasonable in most cases. The experiment shows that TSD can find the correct mappings with high accuracy.},
  doi       = {10.1109/CSE.2009.454},
  keywords  = {Internet;classification;vocabulary;Web 2.0;Wikipedia;folksonomy;global neighbor tags;local neighbor tags;social tagging;tag sense disambiguation;tag vocabulary;Collaboration;Computer science;Dictionaries;Frequency;Ontologies;Tagging;Thesauri;Uniform resource locators;Vocabulary;Wikipedia;Wikipedia;folksonomy;social tagging;vocabulary;word sense disambiguation},
}

@InProceedings{dattolo_etal_2010,
  author    = {A. Dattolo and F. Ferrara and C. Tasso},
  title     = {The role of tags for recommendation: A survey},
  booktitle = {3rd International Conference on Human System Interaction},
  year      = {2010},
  pages     = {548-555},
  month     = {May},
  abstract  = {Social tagging is an innovative and powerful mechanism introduced with Web 2.0: it shifts the task of classifying resources from a reduced set of knowledge engineers to the wide set of Web users. Users of social tagging systems define personal classifications which can be used by other peers for browsing available resources. However, due to the absence of rules for managing the tagging process, and to the lack of predefined schemas or structures for inserting metadata and relationships among tags, current user generated classifications dop not produce sound taxonomies. This is a strong limitation which prevents an effective and informed resource sharing. For this reason researchers are modeling innovative recommender systems capable to better support tagging, browsing, and searching for new resources. This paper is a survey which discusses the role of tags in recommender systems: starting from social tagging systems, we analyze various techniques for suggesting content and we introduce the approaches exploited for proposing tags for classifying resources, considering both personalized and not-personalized recommendation.},
  doi       = {10.1109/HSI.2010.5514515},
  issn      = {2158-2246},
  keywords  = {Internet;meta data;pattern classification;personal computing;recommender systems;user interfaces;Web 2.0;informed resource sharing;metadata;personal classification;recommender system;social tagging;Decision support systems;Helium;Knowledge engineering;Mercury (metals);Power engineering and energy;Recommender systems;Resource management;Tagging;Taxonomy;Tag;personalization;recommender system;social tagging},
}

@Article{_2010,
  title    = {Modeling Social Annotations via Latent Reason Identification},
  journal  = {IEEE Intelligent Systems},
  year     = {2010},
  volume   = {PP},
  number   = {99},
  pages    = {1-1},
  issn     = {1541-1672},
  abstract = {Social annotations are easy to adopt and widely used, but easiness leads to problems like ambiguity and noise. We present a probabilistic model that explains social tags by modeling the latent reason of each tag, namely the Tag Allocation Model (TAM), which disambiguates tags and identiﬁes the noise. We evaluate TAM ’s predictability on unseen data, and also test the model’s performance in two real world tasks: tag recommendation and tag relation discovery. More applications of TAM could be explored.},
  keywords = {Biological system modeling;Computational modeling;Concrete;Intelligent systems;Noise;Probabilistic logic;Resource management;B.3.3.b Simulation;Biological system modeling;C.3.h Ubiquitous computing;Computational modeling;Concrete;H.3.0.a Web Search;I.2.12 Intelligent Web Services and Semantic Web;I.2.7.i Web text analysis;Intelligent Control;Intelligent systems;Noise;Probabilistic logic;Resource management;Social Computing},
  url      = {http://ieeexplore.ieee.org/document/5678585/},
}

@Article{astrain_etal_2010,
  author   = {J. J. Astrain and F. Echarte and A. Cordoba and J. Villadangos},
  title    = {Clustering Method for Social Network Annotations},
  journal  = {IEEE Latin America Transactions},
  year     = {2010},
  volume   = {8},
  number   = {1},
  pages    = {88-93},
  month    = {March},
  issn     = {1548-0992},
  abstract = {Folksonomies are a widely used tool of collaboratively creating and managing tags to annotate and categorize Internet resources (Web 2.0). The process of annotation and tag management by users of social networks is extremely easy and simple; however, it involves serious problems of navigation and search unlike what happens with taxonomies, thesauri and ontologies. The use of fuzzy similarity measures allows the correct identification of syntactic variations when tag lengths are greater or equal than five symbols, been inadequate for smaller length tags. This article presents a method that combines both fuzzy similarity and cosine measures in order to provide a proper classification of tags even with smaller tag lengths. This method allows the proper classification of the 95% of the syntactic variations of tags analyzed in the experiments.},
  doi      = {10.1109/TLA.2010.5453951},
  keywords = {Internet;document handling;fuzzy set theory;pattern clustering;social networking (online);Internet resources;Web 2.0;clustering method;cosine measure;fuzzy similarity measure;social network annotation;syntactic variation;tag management;Clustering methods;Collaborative tools;Internet;Length measurement;Navigation;Ontologies;Resource management;Social network services;Taxonomy;Thesauri;folksonomies;fuzzy similarity;social network;social tagging;tag annotation;tag clustering},
}

@InProceedings{huang_etal_2012a,
  author    = {Huang, Eric H. and Socher, Richard and Manning, Christopher D. and Ng, Andrew Y.},
  title     = {Improving Word Representations via Global Context and Multiple Word Prototypes},
  booktitle = {Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1},
  year      = {2012},
  series    = {ACL '12},
  pages     = {873--882},
  address   = {Stroudsburg, PA, USA},
  publisher = {Association for Computational Linguistics},
  acmid     = {2390645},
  keywords  = {rank2},
  location  = {Jeju Island, Korea},
  numpages  = {10},
  url       = {http://dl.acm.org/citation.cfm?id=2390524.2390645},
}

@Conference{magableh_etal_2010,
  author          = {Magableh, M. and Cau, A. and Zedan, H. and Ward, M.},
  title           = {Towards a multilingual semantic folksonomy},
  year            = {2010},
  pages           = {178-182},
  note            = {cited By},
  abstract        = {The content of collaborative tagging systems (so-called folksonomies) is generated, consumed, and annotated by the endusers. Users annotate and categorise their data using free-keywords, so-called tags. Consequently, several linguistic problems come to the surface in folksonomies such as; synonyms, polysemy, multilinguality, and others which produce ambiguous and inconsistent classification of data. Therefore, relevant results are not retrieved in the user's query. In this paper, we suggest a novel approach to enhance the "social vocabulary" presented in folksonomies with the "controlled vocabulary" presented in Semantic Web ontologies. Therefore, our proposed approach uses the online WordNet lexical ontology in addition to the EuroWordNet multilingual lexical resource. Our approach tries to employ the ontological relations presented in WordNet in the folksonomy, it focuses only on the problems of synonyms, tag relations, and multilinguality.},
  affiliation     = {Software Technology Research Laboratory (STRL), Faculty of Technology, De Montfort University, The Gateway, Leicester LE1 9BH, United Kingdom},
  author_keywords = {Collaborative tagging system; EuroWordNet; Folksonomy; Semantic web; Social web; WordNet ontology},
  document_type   = {Conference Paper},
  journal         = {Proc. of the IADIS Int. Conf. Collaborative Technologies 2010, Proc. of the IADIS Int. Conf. Web Based Communities 2010, Part of the MCCSIS 2010},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79955138190&partnerID=40&md5=62493359ef3740fd0c6a60d6e4ddb0c1},
}

@Book{dattolo_etal_2009,
  title         = {Towards disambiguating social tagging systems},
  year          = {2009},
  author        = {Dattolo, A. and Duca, S. and Tomasi, F. and Vitali, F.},
  volume        = {1},
  note          = {cited By},
  abstract      = {Social tagging to annotate resources represents one of the innovative aspects introduced with Web 2.0 and the new challenges of the (semantic) Web 3.0. Social tagging, also known as user-generated keywords or folksonomies, implies that keywords, from an arbitrarily large and uncontrolled vocabulary, are used by a large community of readers to describe resources. Despite undeniable success and usefulness of social tagging systems, they also suffer from some drawbacks: the proliferation of social tags, coming as they are from an unrestricted vocabulary leads to ambiguity when determining their intended meaning; the lack of predefined schemas or structures for inserting metadata leads to confusions as to their roles and justification; and the flatness of the structure of the keywords and lack of relationships among them imply difficulties in relating different keywords when they describe the same or similar concepts. So in order to increase precision, in the searches and classifications made possible by folksonomies, some experiences and results from formal classification and subjecting systems are considered, in order to help solve, if not to prevent altogether, the ambiguities that are intrinsic in such systems. Some successful and not so successful approaches as proposed in the scientific literature are discussed, and a few more are introduced here to further help dealing with special cases. In particular, we believe that adding depth and structure to the terms used in folksonomies could help in word sense disambiguation, as well as correctly identifying and classifying proper names, metaphors, and slang words when used as social tags. © 2010, IGI Global.},
  affiliation   = {Department of Mathematics and Computer Science, University of Udine, Italy; University of Bologna, Italy},
  document_type = {Book Chapter},
  doi           = {10.4018/978-1-60566-384-5.ch020},
  journal       = {Handbook of Research on Web 2.0, 3.0, and X.0: Technologies, Business, and Social Applications},
  pages         = {349-369},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79959298753&doi=10.4018%2f978-1-60566-384-5.ch020&partnerID=40&md5=6142d5700cc9041cf79bca4cf0980626},
}

@Conference{voon_etal_2010,
  author          = {Voon, W.P. and Mustapha, N. and Sulaiman, M.N.},
  title           = {A tag recommendation approach based on social semantic web},
  year            = {2010},
  pages           = {909-912},
  note            = {cited By},
  abstract        = {Collaborative tagging applications or folksonomies allow internet users to annotate resources with personalized tags. However, freedom afforded users come at a cost: an uncontrolled vocabulary can result in tag ambiguity hindering navigation. Ambiguity can mislead users as they search for relevant resources. Recommenders that avoid ambiguous tags may be penalized by standard utility metrics for not promoting such tags. To provide effectiveness of tag recommendation in term of accuracy and coverage, we propose a new method for tag recommendation approach based on social semantic web to overcome ambiguity and presenting users tags and resources that correspond more closely. This method is a combination of semantic content analysis methods and social network analysis methods. Copyright 2010 ACM.},
  affiliation     = {Faculty of Computer Science and Information Technology, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia},
  author_keywords = {Ambiguity; Folksonomy; Recommender system; Semantic web; Social semantic web; Tag},
  document_type   = {Conference Paper},
  doi             = {10.1145/1967486.1967645},
  journal         = {iiWAS2010 - 12th International Conference on Information Integration and Web-Based Applications and Services},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79956009869&doi=10.1145%2f1967486.1967645&partnerID=40&md5=c6657266825f53dc1d5c746ed8b2eee1},
}

@InProceedings{fan_etal_2010,
  author    = {J. Fan and Y. Shen and N. Zhou and Y. Gao},
  title     = {Harvesting large-scale weakly-tagged image databases from the web},
  booktitle = {2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
  year      = {2010},
  pages     = {802-809},
  month     = {June},
  abstract  = {To leverage large-scale weakly-tagged images for computer vision tasks (such as object detection and scene recognition), a novel cross-modal tag cleansing and junk image filtering algorithm is developed for cleansing the weakly-tagged images and their social tags (i.e., removing irrelevant images and finding the most relevant tags for each image) by integrating both the visual similarity contexts between the images and the semantic similarity contexts between their tags. Our algorithm can address the issues of spams, polysemes and synonyms more effectively and determine the relevance between the images and their social tags more precisely, thus it can allow us to create large amounts of training images with more reliable labels by harvesting from large-scale weakly-tagged images, which can further be used to achieve more effective classifier training for many computer vision tasks.},
  doi       = {10.1109/CVPR.2010.5540135},
  issn      = {1063-6919},
  keywords  = {Internet;computer vision;visual databases;Web;computer vision;cross modal tag cleansing;image filtering algorithm;large scale weakly tagged image database;Collaboration;Computer vision;Image databases;Image recognition;Internet;Large scale integration;Large-scale systems;Layout;Object detection;Tagging},
}

@InProceedings{si_sun_2010,
  author    = {X. Si and M. Sun},
  title     = {Tag Allocation Model: Model Noisy Social Annotations by Reason Finding},
  booktitle = {2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology},
  year      = {2010},
  volume    = {1},
  pages     = {413-416},
  month     = {Aug},
  abstract  = {We propose the Tag Allocation Model (TAM) to model social annotation data. TAM is a probabilistic generative model, its key feature is finding the latent reason for each tag. A latent reason can be any discrete features of the document (such as words) or a global noise variable. Inferring the reason for each tag helps TAM reduce the ambiguity of a document with multiple tags. By introducing noise as a reason, TAM can handle noise tags naturally. We perform experiments on three real world data sets. The results show that TAM outperforms state-of-the-art approaches in both held-out perplexity and tag recommendation accuracy.},
  doi       = {10.1109/WI-IAT.2010.85},
  keywords  = {inference mechanisms;information retrieval;portals;probability;TAM;global noise variable;noisy social annotation data model;probabilistic generative model;real world data sets;reason finding;tag allocation model;probabilistic model;recommendation;social annotation;tagging},
}

@Article{clements_etal_2010a,
  author   = {M. Clements and A.P. de Vries and M.J.T. Reinders},
  title    = {The influence of personalization on tag query length in social media search},
  journal  = {Information Processing \& Management},
  year     = {2010},
  volume   = {46},
  number   = {4},
  pages    = {403 - 412},
  issn     = {0306-4573},
  note     = {Semantic Annotations in Information Retrieval},
  doi      = {http://dx.doi.org/10.1016/j.ipm.2009.03.006},
  keywords = {Social media, Personalized search, Random walk, Collaborative tagging, Query length},
  url      = {http://www.sciencedirect.com/science/article/pii/S0306457309000247},
}

@Conference{tang_etal_2010,
  author          = {Tang, R. and Zuo, J. and Xu, K. and Zheng, J. and Wang, Y.},
  title           = {An intelligent semantic-based tag cleaner for folksonomies},
  booktitle       = {2010 International Conference on Intelligent Computing and Integrated Systems},
  year            = {2010},
  pages           = {773-776},
  month           = {Oct},
  note            = {cited By},
  abstract        = {The collaborative tagging provided by folksonomy systems is an un-controlled process for users, and the personal and arbitrary tag assignments lead to great tag noises. To solve the problem, authors make contributions as follows: (a) demonstrate that tags assigned to web resources are highly noisy due to the diverse un-controlled present styles of tags; (b) present a two-stage method to clean syntactic and semantic tag noises by taking semantic as the relevance measurement for tags; (c) conduct extensive experiments using dataset collected from del.icio.us. The ratio of the noise tags discovered by our method is up to 40%, and the experiment results show that the proposed method in either semantic approach is highly effective. © 2010 IEEE.},
  affiliation     = {School of Computer Science, Sichuan University, Chengdu, China; Department of Computer Science and Technology, Chengdu University of Information Technology, Chengdu, China},
  art_number      = {5657118},
  author_keywords = {Folksonomy; Pre-processing; Semantic evaluation; Tag noise},
  document_type   = {Conference Paper},
  doi             = {10.1109/ICISS.2010.5657118},
  journal         = {Proceedings - 2010 International Conference on Intelligent Computing and Integrated Systems, ICISS2010},
  keywords        = {identification technology;noise;semantic Web;arbitrary tag assignments;collaborative tagging;folksonomies;intelligent semantic-based tag cleaner;tag noises;web resources;Frequency measurement;Noise;Transforms;folksonomy;pre-processing;semantic evaluation;tag noise},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78651422443&doi=10.1109%2fICISS.2010.5657118&partnerID=40&md5=65924ab8f83be1d048f30d269fc8a68f},
}

@InProceedings{sun_etal_2011c,
  author    = {X. Sun and J. Kaur and L. Possamai and F. Menczer},
  title     = {Detecting Ambiguous Author Names in Crowdsourced Scholarly Data},
  booktitle = {2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing},
  year      = {2011},
  pages     = {568-571},
  month     = {Oct},
  abstract  = {The name ambiguity problem is a challenge in many areas, especially in the field of bibliographic digital libraries. For example, in services that use citation data to compute the impact of authors, ambiguous names lead to biased measures. The problem is amplified where names are collected from heterogeneous sources, including crowd sourced annotations. This is the case in the Scholaro meter system, which cross-correlates author names in user queries with those retrieved from bibliographic data. The uncontrolled nature of user-generated annotations is very valuable, but creates the need to detect ambiguous names. In this paper, we propose an approach to detect ambiguous names at query time, which makes it applicable in the context of a social computing application. We explore two kinds of heuristic features based on citations and crowd sourced topics. Our approach can detect ambiguous author names in crowd sourced scholarly data with an accuracy of 75%.},
  doi       = {10.1109/PASSAT/SocialCom.2011.43},
  keywords  = {citation analysis;digital libraries;Scholaro meter system;ambiguous author name;bibliographic digital libraries;citation data;crowd sourced annotation;crowd sourced topic;crowdsourced scholarly data;heuristic features;name ambiguity problem;social computing;user-generated annotation;Accuracy;Collaboration;Computer science;Educational institutions;Feature extraction;Indexes;Libraries;ambiguous name detection;citation analysis;crowdsourcing;discipline annotations;scholarly data;social tagging},
}

@InProceedings{majid_etal_2011,
  author    = {A. Majid and S. Khusro and A. Rauf},
  title     = {Semantics in social tagging systems: A review},
  booktitle = {International Conference on Computer Networks and Information Technology},
  year      = {2011},
  pages     = {191-203},
  month     = {July},
  abstract  = {Web 2.0 brought many collaborative and novel applications which transformed the web as medium and resulted in exponential growth of the web. Tagging systems are one of these killer applications. As tags represent the link between objective information and users' cognitive information, they have ambiguity problem reducing precision. Hence search and retrieval pose a challenge on folksonomy systems having unstructured, non-hierarchical organization with unsupervised vocabulary. We present a brief survey of different approaches related to resolving ambiguity and bringing structure and semantics in the social tagging model.},
  doi       = {10.1109/ICCNIT.2011.6020929},
  issn      = {2223-6317},
  keywords  = {information retrieval;social networking (online);Web 2.0;folksonomy system;social tagging system semantics;user cognitive information;Electronic publishing;Encyclopedias;Internet;Navigation;Semantics;Thesauri},
}

@Article{yi_2011,
  author          = {Yi, K.},
  title           = {An empirical study on the automatic resolution of semantic ambiguity in social tags},
  journal         = {Proceedings of the ASIST Annual Meeting},
  year            = {2011},
  volume          = {48},
  note            = {cited By},
  abstract        = {Due to the popularity of collaborative tagging services and systems, the role of social tags in information organization and retrieval has become increasingly critical within the tagging system. Consequently, the resolution of semantic ambiguity of tag sense (i.e., meaning) plays an important role in the enhancement of information organization and retrieval in collaborative tagging applications. Our approach to tackle the task of automatic resolution of semantic ambiguity is based on the hypothesis that given a target tag, some of the co-occurring social tags that were selectively assigned by the same or by different people to the same resource can serve as a useful dataset. Four different methods (i.e., two LSA-based, LIN-based, and Cooccurrence method) for tag sense disambiguation are proposed and their results are compared, aiming to automatically rank the senses associated with a given target tag. The experimental results with a Delicious dataset indicate that LSA-w and LIN-based methods produce more stable performances, and suggest the same two methods for the Tag Sense Disambiguation task, particularly in the situation that the Boolean effect is more likely to arise.},
  affiliation     = {School of Library and Information Science, 331 Little Fine Arts Library, University of Kentucky, Lexington, KY 40509, United States},
  author_keywords = {Latent semantic analysis; Meanings of tag; Tag sense disambiguation},
  document_type   = {Conference Paper},
  doi             = {10.1002/meet.2011.14504801175},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861431075&doi=10.1002%2fmeet.2011.14504801175&partnerID=40&md5=c1dd51b49fa91c414708d4346d45c949},
}

@Article{kim_etal_2012b,
  author          = {Kim, H.-N. and Roczniak, A. and Lévy, P. and El Saddik, A.},
  title           = {Social media filtering based on collaborative tagging in semantic space},
  journal         = {Multimedia Tools and Applications},
  year            = {2012},
  volume          = {56},
  number          = {1},
  pages           = {63-89},
  note            = {cited By},
  abstract        = {We propose a semantic collaborative filtering method to enhance recommendation quality derived from user-generated tags. Social tagging is employed as an approach in order to grasp and filter users' preferences for items. In addition, we explore several advantages of semantic tagging for ambiguity, synonymy, and semantic interoperability, which are notable challenges in information filtering. The proposed approach first determines semantically similar users using social tagging and subsequently discovers semantically relevant items for each user. Experimental results show that our method offers significant advantages both in terms of improving the recommendation quality and in dealing with ambiguity, synonymy, and interoperability issues. © 2010 Springer Science+Business Media, LLC.},
  affiliation     = {Collective Intelligence Lab, University of Ottawa, Ottawa, ON, Canada; Collective Intelligence Lab, Canada Research Department of Collective Intelligence, University of Ottawa, Ottawa, ON, Canada; Multimedia Communication Research Lab, University of Ottawa, Ottawa, ON, Canada},
  author_keywords = {Folksonomy; IEML; Semantic collaborative filtering; Semantic tagging; Social recommender system; Social tagging},
  document_type   = {Article},
  doi             = {10.1007/s11042-010-0557-4},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857361281&doi=10.1007%2fs11042-010-0557-4&partnerID=40&md5=09a64b82800e3eb404b1442fb2ec1899},
}

@Article{vandic_etal_2012,
  author   = {Damir Vandic and Jan-Willem van Dam and Flavius Frasincar},
  title    = {A semantic-based approach for searching and browsing tag spaces},
  journal  = {Decision Support Systems},
  year     = {2012},
  volume   = {54},
  number   = {1},
  pages    = {644 - 654},
  issn     = {0167-9236},
  doi      = {http://dx.doi.org/10.1016/j.dss.2012.08.010},
  keywords = {Tag spaces, Semantic clustering, Syntactic variations, Flickr},
  url      = {http://www.sciencedirect.com/science/article/pii/S0167923612002254},
}

@InProceedings{mas_2012,
  author    = {M. D. Mas},
  title     = {Intelligent Interface Architectures for Folksonomy Driven Structure Network},
  booktitle = {2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems},
  year      = {2012},
  pages     = {519-525},
  month     = {July},
  abstract  = {The folksonomy is the result of free personal information or assignment of tags to an object (determined by the URI) in order to find them. The practice of tagging is done in a collective environment. Folksonomies are self constructed, based on co-occurrence of definitions, rather than a hierarchical structure of the data. The downside of this was that a few sites and applications are able to successfully exploit the sharing of bookmarks. The need for tools that are able to resolve the ambiguity of the definitions is becoming urgent as the need of simple instruments for their visualization, editing and exploitation in web applications still hinders their diffusion and wide adoption. An intelligent interactive interface design for folksonomies should consider the contextual design and inquiry based on a concurrent interaction for a perceptual user interfaces. To represent folksonomies a new concept structure called " Folksodriven" is used in this paper. While it is presented the Folks driven Structure Network (FSN) to resolve the ambiguity of definitions of folksonomy tags suggestions for the user. On this base a Human-Computer Interactive (HCI) systems is developed for the visualization, navigation, updating and maintenance of folksonomies Knowledge Bases - the FSN - through the web. System functionalities as well as its internal architecture will be introduced.},
  doi       = {10.1109/CISIS.2012.158},
  keywords  = {data visualisation;groupware;human computer interaction;information retrieval;interactive systems;semantic Web;user interfaces;Folksodriven Structure Network;Folksodriven structure;HCI system;URI;Web application;bookmark sharing;collaborative environment;collective environment;concurrent interaction;contextual design;definition ambiguity;definition cooccurrence;editing;folksonomy driven structure network;folksonomy knowledge base;folksonomy tags;free personal information;human-computer interactive system;inquiry;intelligent interactive interface design;intelligent interface architecture;knowledge base maintenance;knowledge base navigation;knowledge base update;knowledge base visualization;perceptual user interface;semantic Web;tag assignment;Information architecture;Labeling;Maintenance engineering;Marine vehicles;Navigation;Ontologies;Visualization;Folksonomy;Human Computer Interactive systems (HCI);Information Architecture;Interface design;Network;Ontology;Semantic Web},
}

@Article{huang_etal_2012,
  author   = {Shiu-Li Huang and Sheng-Cheng Lin and Yung-Chun Chan},
  title    = {Investigating effectiveness and user acceptance of semantic social tagging for knowledge sharing},
  journal  = {Information Processing \& Management},
  year     = {2012},
  volume   = {48},
  number   = {4},
  pages    = {599 - 617},
  issn     = {0306-4573},
  doi      = {http://dx.doi.org/10.1016/j.ipm.2011.07.004},
  keywords = {Knowledge management system, Knowledge map, Ontology, Social tagging, Technology acceptance},
  url      = {http://www.sciencedirect.com/science/article/pii/S0306457311000744},
}

@Article{tourne_godoy_2012,
  author          = {Tourné, N. and Godoy, D.},
  title           = {Evaluating tag filtering techniques for web resource classification in folksonomies},
  journal         = {Expert Systems with Applications},
  year            = {2012},
  volume          = {39},
  number          = {10},
  pages           = {9723-9729},
  issn            = {0957-4174},
  note            = {cited By},
  abstract        = {Social or collaborative tagging systems emerged as a novel classification scheme on the Web based on the collective knowledge of people. In sites such as Del.icio.us, Technorati or Flickr, users annotate a variety of resources, including Web pages, blogs, pictures, videos or bibliographic references; using freely chosen textual labels or tags. Underlying collaborative tagging systems are ternary data structures known as folksonomies relating resources and users through tags, this information facilitate accessing and browsing massive repositories of resources. Collective annotations provided by people in the form of tags can also be exploited to organize resources on-line in a more formal classification scheme such as the ones provided by hierarchies or directories, alleviating the task of manual classification commonly required by systems like directories on the Web. In this paper we present an empirical study carried out to determine the value of tags in resource classification. Furthermore, the use of several filtering and pre-processing operations to reduce the ambiguity and noise in tags are analyzed to determine whether they allow to increase the quality of resource classification. © 2012 Elsevier Ltd. All rights reserved.},
  affiliation     = {Faculty of Exact Sciences, UNICEN University, Campus Universitario, 7000 Tandil, Bs. As., Argentina; ISISTAN Research Institute, UNICEN University, Campus Universitario, 7000 Tandil, Bs. As., Argentina; CONICET, Bs. As., Argentina},
  author_keywords = {Folksonomies; Social tagging systems; Web resource classification},
  document_type   = {Article},
  doi             = {10.1016/j.eswa.2012.02.088},
  keywords        = {Web resource classification, Social tagging systems, Folksonomies},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84859210884&doi=10.1016%2fj.eswa.2012.02.088&partnerID=40&md5=de76e2a4e94625c9fd866fd2d2b65e74},
}

@Article{chen_etal_2014,
  author          = {Chen, J. and Feng, S. and Liu, J.},
  title           = {Topic sense induction from social tags based on non-negative matrix factorization},
  journal         = {Information Sciences},
  year            = {2014},
  volume          = {280},
  pages           = {16-25},
  issn            = {0020-0255},
  note            = {cited By},
  abstract        = {Social tagging, also noted as collaborative tagging or folksonomy, is an important way for users themselves to describe resources on the Web. The tags that the web users adopt to describe the resources are called social tags, and they have been widely used and studied. However, for the absence of a central controlled vocabulary, the semantics of the social tags are ambiguous due to constant changes of either the users' interests or the informal definitions, which makes it hard to directly make use of these social tags in the web applications. In this paper, we propose a non-negative matrix factorization (NMF) based method to automatically induce topic senses from social tags, which can then be used for the tag disambiguation. A novel automatic evaluation method is also proposed to evaluate our method. The experiment results show that the proposed topic sense induction method can help to provide precise resources search and recommendation, which is one of the key functionalities in social tagging systems. © 2014 Elsevier Inc. All rights reserved.},
  affiliation     = {School of Computer, Wuhan University, Wuhan 430072, China; School of Information Engineering, Nanjing University of Finance and Economic, China},
  author_keywords = {Disambiguation; Non-negative matrix factorization; Social tag; Topic sense induction},
  document_type   = {Article},
  doi             = {10.1016/j.ins.2014.04.048},
  keywords        = {Topic sense induction, Social tag, Disambiguation, Non-negative matrix factorization},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902482576&doi=10.1016%2fj.ins.2014.04.048&partnerID=40&md5=ede8f5ea3ab3804b500edf407c2cd77c},
}

@InProceedings{legesse_etal_2016,
  author    = {M. Legesse and G. Gianini and D. Teferi},
  title     = {Selecting Feature-Words in Tag Sense Disambiguation Based on Their Shapley Value},
  booktitle = {2016 12th International Conference on Signal-Image Technology Internet-Based Systems (SITIS)},
  year      = {2016},
  pages     = {236-240},
  month     = {Nov},
  abstract  = {In tag-word disambiguation, a word is assigned to a specific context chosen among the different ones to which it is related. Relatedness to a context is often defined based on the co-occurrence of the target word with other words (context words) in sentences of a specific corpus. The overall disambiguation process can be thought as a classification process, where the context words play the role of features for the target. A problem with this approach is that the large number of possible context words can reduce the classification performance, both in terms of computational effort and in terms of quality of the outcome. Feature selection can improve the process in both regards, by reducing the overall feature space to a manageable size with high information content. In this work we propose to use, in disambiguation, a feature selection approach based on the Shapley Value (SV) - a Coalitional Game Theory related metrics, measuring the importance of a component within a coalition. By including in the feature set only the words with the highest Shapley Value, we obtain remarkable quality and performance improvements. The problem of the exponential complexity in the exact SV computation is avoided by an approximate computation based on sampling. We demonstrate the effectiveness of this method and of the sampling approach results, by using both a synthetic language corpus and a real world linguistic corpus.},
  doi       = {10.1109/SITIS.2016.45},
  keywords  = {feature selection;game theory;linguistics;pattern classification;set theory;word processing;SV computation;Shapley value;classification performance reduction;coalitional game theory related metrics;exponential complexity;feature selection;feature-word selection;linguistic corpus;synthetic language corpus;tag sense disambiguation;tag-word disambiguation;Context;Feature extraction;Games;Histograms;Measurement;Probability distribution;Tagging;Feature selection;Shapley Value;dimensional reduction;disambiguation;semantic relatedness;tagging},
}

@InProceedings{tygel_etal_2016,
  author    = {A. Tygel and S. Auer and J. Debattista and F. Orlandi and M. L. M. Campos},
  title     = {Towards Cleaning-Up Open Data Portals: A Metadata Reconciliation Approach},
  booktitle = {2016 IEEE Tenth International Conference on Semantic Computing (ICSC)},
  year      = {2016},
  pages     = {71-78},
  month     = {Feb},
  abstract  = {This paper presents an approach for metadata reconciliation, curation and linking for Open Governamental Data Portals (ODPs). ODPs have been lately the standard solution for governments willing to put their public data available for the society. Portal managers use several types of metadata to organize the datasets, one of the most important ones being the tags. However, the tagging process is subject to many problems, such as synonyms, ambiguity or incoherence, among others. As our empiric analysis of ODPs shows, these issues are currently prevalent in most ODPs and effectively hinders the reuse of Open Data. In order to address these problems, we develop and implement an approach for tag reconciliation in Open Data Portals, encompassing local actions related to individual portals, and global actions for adding a semantic metadata layer above individual portals. The local part aims to enhance the quality of tags in a single portal, and the global part is meant to interlink ODPs by establishing relations between tags.},
  doi       = {10.1109/ICSC.2016.54},
  keywords  = {data handling;government data processing;information retrieval;meta data;portals;metadata curation;metadata linking;metadata reconciliation approach;open governamental data portal;public data;semantic metadata layer;tag reconciliation;tagging process;Measurement;Metadata;Organizations;Portals;Semantics;Standards organizations;Tagging;open data portal;semantic web;tag},
}

@InProceedings{beyer_pinzger_2016,
  author    = {S. Beyer and M. Pinzger},
  title     = {Grouping Android Tag Synonyms on Stack Overflow},
  booktitle = {2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)},
  year      = {2016},
  pages     = {430-440},
  month     = {May},
  abstract  = {On Stack Overflow, more than 38,000 diverse tags are used to classify posts. The Stack Overflow community provides tag synonyms to reduce the number of tags that have the same or similar meaning. In our previous research, we used those synonym pairs to derive a number of strategies to create tag synonyms automatically. In this work, we continue this line of research and present an approach to group tag synonyms to meaningful topics. We represent our synonyms as directed, weighted graphs, and investigate several graph community detection algorithms to build meaningful groups of tags, also called tag communities. We apply our approach to the tags obtained from Android-related Stack Overflow posts and evaluate the resulting tag communities quantitatively with various community metrics. In addition, we evaluate our approach qualitatively through a manual inspection and comparison of a random sample of tag communities. Our results show that we can cluster the Android tags to 2,481 meaningful tag communities. We also show how these tag communities can be used to derive trends of topics of Android-related questions on Stack Overflow.},
  doi       = {10.1109/MSR.2016.051},
  keywords  = {Android (operating system);directed graphs;pattern clustering;Android tag synonym grouping;Stack Overflow;directed-weighted graphs;graph community detection algorithm;qualitative analysis;tag community metrics;Androids;Detection algorithms;Humanoid robots;Market research;Software engineering;Tagging;Terminology;Android;Clustering;Stack Overflow;Tags, rank1},
}

@Article{mika_etal_2008,
  author     = {Mika, Peter and Ciaramita, Massimiliano and Zaragoza, Hugo and Atserias, Jordi},
  title      = {Learning to Tag and Tagging to Learn: A Case Study on Wikipedia},
  journal    = {IEEE Intelligent Systems},
  year       = {2008},
  volume     = {23},
  number     = {5},
  pages      = {26--33},
  month      = sep,
  issn       = {1541-1672},
  acmid      = {1449594},
  address    = {Piscataway, NJ, USA},
  doi        = {10.1109/MIS.2008.85},
  issue_date = {September 2008},
  keywords   = {Wikipedia, named entity recognition, natural language processing, natural language processing, named entity recognition, Wikipedia},
  numpages   = {8},
  publisher  = {IEEE Educational Activities Department},
  url        = {http://dx.doi.org/10.1109/MIS.2008.85},
}

@Conference{choy_lui_2007,
  author        = {Choy, S.-O. and Lui, A.K.},
  title         = {Web information retrieval in collaborative tagging systems},
  booktitle     = {2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06)},
  year          = {2007},
  pages         = {352-355},
  month         = {Dec},
  note          = {cited By},
  abstract      = {Collaborative tagging on the web has been quickly gaining ground as a new paradigm for web information retrieval, discovering and filtering. There are a number of successful deployments of collaborative tagging systems that effectively recruits the activity of human users into collecting and annotating vast amounts of web resources. They lead to an emergent categorization of web resources in terms of tags, and create a different kind of web directory. However, the current ways of exploration in the tagging space are limited, which cannot get the most out of the real value of it. This paper presents our methodology, observations, and experimental results in the way we propose how to improve the user experience in exploring information captured by collaborative tagging systems. © 2006 IEEE.},
  affiliation   = {School of Science and Technology, Open University of Hong Kong, Hong Kong SAR, Hong Kong},
  art_number    = {4061393},
  document_type = {Conference Paper},
  doi           = {10.1109/WI.2006.191},
  journal       = {Proceedings - 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings), WI'06},
  keywords      = {Internet;information retrieval;Web directory;Web information filtering;Web information retrieval;Web resource;collaborative tagging system;Collaboration;Humans;Information filtering;Information filters;Information retrieval;Navigation;Organizing;Recruitment;Tagging;Uniform resource locators},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-42549089028&doi=10.1109%2fWI.2006.191&partnerID=40&md5=830305139f6641d667f28bd0b49d6874},
}

@InProceedings{begelman_2006,
  author    = {Grigory Begelman},
  title     = {Automated Tag Clustering: Improving search and exploration in the tag space},
  booktitle = {In Proc. of the Collaborative Web Tagging Workshop at WWW’06},
  year      = {2006},
}

@InProceedings{lee_etal_2008,
  author    = {Lee, Sang Su and Won, Dongwoo and McLeod, Dennis},
  title     = {Tag-geotag Correlation in Social Networks},
  booktitle = {Proceedings of the 2008 ACM Workshop on Search in Social Media},
  year      = {2008},
  series    = {SSM '08},
  pages     = {59--66},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {1458595},
  doi       = {10.1145/1458583.1458595},
  isbn      = {978-1-60558-258-0},
  keywords  = {clustering, geotagging, power law for correlation among tagging and geotagging, tagging},
  location  = {Napa Valley, California, USA},
  numpages  = {8},
  url       = {http://doi.acm.org/10.1145/1458583.1458595},
}

@InProceedings{sanchez-zamora_llamas-nistal_2009,
  author    = {F. Sánchez-Zamora and M. Llamas-Nistal},
  title     = {Visualizing tags as a network of relatedness},
  booktitle = {2009 39th IEEE Frontiers in Education Conference},
  year      = {2009},
  pages     = {1-6},
  month     = {Oct},
  abstract  = {We present a layout algorithm for grouping the user's tags within a folksonomy, aimed at easing the visualization and exploration of a large set of tags and their complex relationships. We introduce the possibility of (i) labeling linking tags to construct a network of tags and (ii) defining the concrete meaning of a tag by choosing the right definition extracted from Wikimedia projects or by writing down a particular definition. This provides the context for disambiguating the semantic of tags and it can be used to refine searches and/or suggestions. Moreover, this leads to alleviate some of the problems regarding the use of a natural vocabulary in a faceted classification. We provide some definitions and show examples of the so-called second generation of tag-clouds. We conclude with some implications that make use of the layout algorithm for visualizing a set of more than 400 tags.},
  doi       = {10.1109/FIE.2009.5350530},
  issn      = {0190-5848},
  keywords  = {information retrieval;pattern classification;Wikimedia projects;faceted classification;folksonomy;natural vocabulary;tag clouds second generation;tags concrete meaning;tags definition extraction;tags exploration;tags network construction;users tags visualization;Concrete;Electronic learning;Information retrieval;Joining processes;Labeling;Tagging;Uniform resource locators;Visualization;Vocabulary;Writing;Faceted Classification;Folksonomy;Knowledge Visualization;Tag-cloud},
}

@Conference{martinez_etal_2009,
  author        = {Martínez, E. and Celma, O. and Sordo, M. and de Jong, B. and Serra, X.},
  title         = {Extending the folksonomies of freesound.org using content-based audio analysis},
  year          = {2009},
  pages         = {65-70},
  note          = {cited By},
  abstract      = {This paper presents an in-depth study of the social tagging mechanisms used in Freesound.org, an online community where users share and browse audio files by means of tags and content-based audio similarity search. We performed two analyses of the sound collection. The first one is related with how the users tag the sounds, and we could detect some well-known problems that occur in collaborative tagging systems (i.e. polysemy, synonymy, and the scarcity of the existing annotations). Moreover, we show that more than 10% of the collection were scarcely annotated with only one or two tags per sound, thus frustrating the retrieval task. In this sense, the second analysis focuses on enhancing the semantic annotations of these sounds, by means of content- based audio similarity (autotagging). In order to "autotag" the sounds, we use a k-NN classifier that selects the available tags from the most similar sounds. Human assessment is performed in order to evaluate the perceived quality of the candidate tags. The results show that, in 77% of the sounds used, the annotations have been correctly extended with the proposed tags derived from audio similarity.},
  affiliation   = {Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain},
  document_type = {Conference Paper},
  journal       = {Proceedings of the 6th Sound and Music Computing Conference, SMC 2009},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905181598&partnerID=40&md5=4249693b53914bc706f2f4bd969d4cbb},
}

@InBook{schmitz_etal_2006,
  pages     = {261--270},
  title     = {Mining Association Rules in Folksonomies},
  publisher = {Springer Berlin Heidelberg},
  year      = {2006},
  author    = {Schmitz, Christoph and Hotho, Andreas and J{\"a}schke, Robert and Stumme, Gerd},
  editor    = {Batagelj, Vladimir and Bock, Hans-Hermann and Ferligoj, Anu{\v{s}}ka and {\v{Z}}iberna, Ale{\v{s}}},
  address   = {Berlin, Heidelberg},
  isbn      = {978-3-540-34416-2},
  abstract  = {Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.},
  booktitle = {Data Science and Classification},
  doi       = {10.1007/3-540-34416-0_28},
  url       = {https://doi.org/10.1007/3-540-34416-0_28},
}

@InProceedings{huang_zhou_2010,
  author    = {X. Huang and Y. Zhou},
  title     = {An Asymmetric Similarity Measure for Tag Clustering on Flickr},
  booktitle = {2010 12th International Asia-Pacific Web Conference},
  year      = {2010},
  pages     = {171-177},
  month     = {April},
  abstract  = {Web 2.0 tools and environments have made tagging, the act of assigning keywords to on-line objects, a popular way to annotate shared resources. The success of now-prominent tagging systems makes tagging "the natural way for people to classify objects as well as an attractive way to discover new material". One of the most challenging problems is to harvest the semantics from these systems, which can support a number of applications, including tag clustering and tag recommendation. We conduct detailed studies on different types of tag relations and tag similarity measures, and propose a scalable measure that we name Reliability Factor Similarity Measure (RFSM). We compare it with two other measures having similar scalability by integrating them into hierarchical clustering methods and performing tag clustering on a subset of Flickr data. The results suggest that RFSM outperforms those two measures when it is applies for tag clustering purpose. We also present an alternative way of utilizing discovered tag relations to set up tag refining rules in order to deal with some noise in the initial tag sets, which can in turn improve the precision of tag relations.},
  doi       = {10.1109/APWeb.2010.65},
  keywords  = {Web sites;identification technology;pattern clustering;Flickr;Web 2.0 tools;asymmetric similarity measure;hierarchical clustering method;keyword assigning;reliability factor similarity measure;shared resource annotation;tag clustering;tag recommendation;tag relations;tag similarity measures;Australia;Clustering algorithms;Clustering methods;Conducting materials;Costs;Information technology;Performance evaluation;Scalability;Tagging;Vocabulary;Clustering;Folksonomy;Reliability Factor Similarity Measure;Tag;Web 2.0, rank2},
}

@InProceedings{beyer_pinzger_2015,
  author    = {S. Beyer and M. Pinzger},
  title     = {Synonym Suggestion for Tags on Stack Overflow},
  booktitle = {2015 IEEE 23rd International Conference on Program Comprehension},
  year      = {2015},
  pages     = {94-103},
  month     = {May},
  abstract  = {The amount of diverse tags used to classify posts on Stack Overflow increased in the last years to more than 38,000 tags. Many of these tags have the same or similar meaning. Stack Overflow provides an approach to reduce the amount of tags by allowing privileged users to manually create synonyms. However, currently exist only 2,765 synonym-pairs on Stack Overflow that is quite low compared to the total number of tags. To comprehend how synonym-pairs are built, we manually analyzed the tags and how the synonyms could be created automatically. Based on our findings, we then present TSST, a tag synonym suggestion tool, that outputs a ranked list of possible synonyms for each input tag. We first evaluated TSST with the 2,765 approved synonym-pairs of Stack Overflow. For 88.4% of the tags TSST finds the correct synonyms, for 72.2% the correct synonym is within the top 10 suggestions. In addition, we applied TSST to 10 randomly selected Android related tags and evaluated the suggested synonyms with 20 Android app developers in an online survey. Overall, in 80% of their ratings, developers found an adequate synonym suggested by TSST.},
  doi       = {10.1109/ICPC.2015.18},
  issn      = {1092-8138},
  keywords  = {Internet;question answering (information retrieval);Android app;Stack Overflow;Web 2.0;social bookmarking;synonym-pairs;tag synonym suggestion tool;Accuracy;Androids;Databases;Humanoid robots;Java;Radiation detectors;Terminology;Stack Overflow;synonyms;tags, rank1},
}

@InProceedings{beyer_pinzger_2016,
  author    = {S. Beyer and M. Pinzger},
  title     = {Grouping Android Tag Synonyms on Stack Overflow},
  booktitle = {2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)},
  year      = {2016},
  pages     = {430-440},
  month     = {May},
  abstract  = {On Stack Overflow, more than 38,000 diverse tags are used to classify posts. The Stack Overflow community provides tag synonyms to reduce the number of tags that have the same or similar meaning. In our previous research, we used those synonym pairs to derive a number of strategies to create tag synonyms automatically. In this work, we continue this line of research and present an approach to group tag synonyms to meaningful topics. We represent our synonyms as directed, weighted graphs, and investigate several graph community detection algorithms to build meaningful groups of tags, also called tag communities. We apply our approach to the tags obtained from Android-related Stack Overflow posts and evaluate the resulting tag communities quantitatively with various community metrics. In addition, we evaluate our approach qualitatively through a manual inspection and comparison of a random sample of tag communities. Our results show that we can cluster the Android tags to 2,481 meaningful tag communities. We also show how these tag communities can be used to derive trends of topics of Android-related questions on Stack Overflow.},
  doi       = {10.1109/MSR.2016.051},
  keywords  = {Android (operating system);directed graphs;pattern clustering;Android tag synonym grouping;Stack Overflow;directed-weighted graphs;graph community detection algorithm;qualitative analysis;tag community metrics;Androids;Detection algorithms;Humanoid robots;Market research;Software engineering;Tagging;Terminology;Android;Clustering;Stack Overflow;Tags, rank1},
  url       = {http://ieeexplore.ieee.org/document/7832922/},
}

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license: MIT

repository: queirozfcom/auto-tagger

repo_name: queirozfcom/auto-tagger


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@ARTICLE{test1,
     NOTE = {Having the similar title and author is helpful for debugging since authors are
                  processed differently from other fields. Here is a formula: $\tan(\alpha)$,
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     AUTHOR = {Kàrè \"u\"a Lêáé},
     TITLE = {Kàrè \"u\"a Lêáé},
     JOURNAL = {Latin-1 accents},
     YEAR = {2020},
}


text:
@article{ma2019combinatorial,
 author = {Ma, Qiang and Ge, Suwen and He, Danyang and Thaker, Darshan and Drori, Iddo},
 journal = {AAAI Workshop on Deep Learning  AAAI Workshop
on Deep Learning on Graphs: Methodologies and Applications},
 title = {Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning},
 year = {2019}
}

repository: darshanthaker/academic-kickstart

repo_name: darshanthaker/academic-kickstart

gha_license_id: MIT


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repository: FlamingTempura/bibtex-tidy

repo_name: FlamingTempura/bibtex-tidy

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text:
@article{parmesan_beyond_2013,
 abstract = {There is increasing pressure from policymakers for ecologists to generate more detailed ‘attribution’ analyses aimed at quantitatively estimating relative contributions of different driving forces, including anthropogenic climate change (ACC), to observed biological changes. Here, we argue that this approach is not productive for ecological studies. Global meta-analyses of diverse species, regions and ecosystems have already given us ‘very high conﬁdence’ [sensu Intergovernmental Panel on Climate Change (IPCC)] that ACC has impacted wild species in a general sense. Further, for well-studied species or systems, synthesis of experiments and models with long-term observations has given us similarly high conﬁdence that they have been impacted by regional climate change (regardless of its cause). However, the role of greenhouse gases in driving these impacts has not been estimated quantitatively. Should this be an ecological research priority? We argue that development of quantitative ecological models for this purpose faces several impediments, particularly the existence of strong, non-additive interactions among different external factors. However, even with current understanding of impacts of global warming, there are myriad climate change adaptation options already developed in the literature that could be, and in fact are being, implemented now.},
 author = {Parmesan, Camille and Burrows, Michael T. and Duarte, Carlos M. and Poloczanska, Elvira S. and Richardson, Anthony J. and Schoeman, David S. and Singer, Michael C.},
 doi = {10.1111/ele.12098},
 editor = {Regan, Helen},
 file = {Parmesan et al. - 2013 - Beyond climate change attribution in conservation .pdf:/Users/ric325/Zotero/storage/R2A22G5Z/Parmesan et al. - 2013 - Beyond climate change attribution in conservation .pdf:application/pdf},
 issn = {1461023X},
 journal = {Ecology Letters},
 language = {en},
 month = {May},
 pages = {58--71},
 title = {Beyond climate change attribution in conservation and ecological research},
 url = {http://doi.wiley.com/10.1111/ele.12098},
 urldate = {2019-10-24},
 volume = {16},
 year = {2013}
}


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repository: kennethmitra/FingerprintTimeClock

repo_name: kennethmitra/FingerprintTimeClock


text:
@article{sasson_increasing_2013,
  title = {Increasing cardiopulmonary resuscitation provision in communities with low bystander cardiopulmonary resuscitation rates: a science advisory from the American Heart Association for healthcare providers, policymakers, public health departments, and community leaders},
  volume = {127},
  issn = {1524-4539},
  shorttitle = {Increasing cardiopulmonary resuscitation provision in communities with low bystander cardiopulmonary resuscitation rates},
  doi = {10.1161/CIR.0b013e318288b4dd},
  language = {eng},
  number = {12},
  journal = {Circulation},
  author = {Sasson, Comilla and Meischke, Hendrika and Abella, Benjamin S and Berg, Robert A and Bobrow, Bentley J and Chan, Paul S and Root, Elisabeth Dowling and Heisler, Michele and Levy, Jerrold H and Link, Mark and Masoudi, Frederick and Ong, Marcus and Sayre, Michael R and Rumsfeld, John S and Rea, Thomas D and {American Heart Association Council on Quality of Care and Outcomes Research} and {Emergency Cardiovascular Care Committee} and {Council on Cardiopulmonary, Critical Care, Perioperative and Resuscitation} and {Council on Clinical Cardiology} and {Council on Cardiovascular Surgery and Anesthesia}},
  month = {mar},
  year = {2013},
  note = {{PMID:} 23439512},
  keywords = {Administrative Personnel, American Heart Association, Cardiopulmonary Resuscitation, Community Health Services, Health Personnel, Heart Arrest, Humans, Leadership, Public Health, United States},
  pages = {1342--1350}
}