@article{10.1109/TKDE.2012.115, address = {Los Alamitos, CA, USA}, author = {Zubiaga, Arkaitz and Fresno, Victor and Martinez, Raquel and Garcia-Plaza, Alberto P.}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.115}, interhash = {f2e961e2b99fec0634b0d4fa3e001282}, intrahash = {8a25332bfeb33e2ad8e1e1a062976da2}, issn = {1041-4347}, journal = {IEEE Transactions on Knowledge and Data Engineering}, number = {PrePrints}, publisher = {IEEE Computer Society}, title = {Harnessing Folksonomies to Produce a Social Classification of Resources}, volume = 99, year = 2012 } @misc{haslhofer2013semantic, abstract = {Tags assigned by users to shared content can be ambiguous. As a possible solution, we propose semantic tagging as a collaborative process in which a user selects and associates Web resources drawn from a knowledge context. We applied this general technique in the specific context of online historical maps and allowed users to annotate and tag them. To study the effects of semantic tagging on tag production, the types and categories of obtained tags, and user task load, we conducted an in-lab within-subject experiment with 24 participants who annotated and tagged two distinct maps. We found that the semantic tagging implementation does not affect these parameters, while providing tagging relationships to well-defined concept definitions. Compared to label-based tagging, our technique also gathers positive and negative tagging relationships. We believe that our findings carry implications for designers who want to adopt semantic tagging in other contexts and systems on the Web.}, author = {Haslhofer, Bernhard and Robitza, Werner and Lagoze, Carl and Guimbretiere, Francois}, interhash = {84516aa456894b6d6adf86abd2386656}, intrahash = {a653f1a0a1ac5084e80757ec277b1184}, note = {cite arxiv:1304.1636Comment: 10 pages}, title = {Semantic Tagging on Historical Maps}, url = {http://arxiv.org/abs/1304.1636}, year = 2013 } @inproceedings{Laniado2010, author = {Laniado, David and Mika, Peter}, booktitle = {International Semantic Web Conference (1)}, crossref = {conf/semweb/2010-1}, editor = {Patel-Schneider, Peter F. and Pan, Yue and Hitzler, Pascal and Mika, Peter and Zhang, Lei and Pan, Jeff Z. and Horrocks, Ian and Glimm, Birte}, ee = {http://dx.doi.org/10.1007/978-3-642-17746-0_30}, interhash = {3a63f88e11f958d548fa91fe442e1dcf}, intrahash = {58dace4881efbd12c81ef1cc2e6bf7b9}, isbn = {978-3-642-17745-3}, pages = {470-485}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Making Sense of Twitter.}, url = {http://dblp.uni-trier.de/db/conf/semweb/iswc2010-1.html#LaniadoM10}, volume = 6496, year = 2010 } @inproceedings{rezel2010swefe, abstract = {This paper presents SWE-FE: a suite of methods to extend folksonomies to the worldwide Sensor Web in order to tackle the emergent data rich information poor (DRIP) syndrome afflicting most geospatial applications on the Internet. SWE-FE leverages the geospatial information associated with three key components of such collaborative tagging systems: tags, resources and users. Specifically, SWE-FE provides algorithms for: i) suggesting tags for users during the tag input stage; ii) generating tag maps which provides for serendipitous browsing; and iii) personalized searching within the folksonomy. We implement SWE-FE on the GeoCENS Sensor Web platform as a case study for assessing the efficacy of our methods. We outline the evaluation framework that we are currently employing to carry out this assessment.}, author = {Rezel, R. and Liang, S.}, booktitle = {2010 International Symposium on Collaborative Technologies and Systems (CTS)}, doi = {10.1109/CTS.2010.5478494}, interhash = {9eb696593932c517873232386f8f61bf}, intrahash = {d5b71572c7fea6504a0c0a3d84a9ecf0}, month = may, pages = {349--356}, publisher = {IEEE}, title = {SWE-FE: Extending folksonomies to the Sensor Web}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5478494}, year = 2010 } @article{Kammergruber2010a, abstract = {Digitally supported knowledge work, using tags for content organization, creates inherent challenges. In this paper we show the design of a corporate tagging framework facing these challenges. We describe the implementation of a thesaurus approach as a lightweight alternative to a more sophisticated ontology design. An RDF based architecture with a Web 2.0 style editor enables average users to enrich social tagging data with semantic relations. }, author = {Kammergruber, Walter Christian and Ehms, Karsten}, interhash = {d9907846571f0057629f0202bb4beb7d}, intrahash = {37048201a69ba88c0d56dcbf8d7f758b}, journal = {10th International Conference on Knowledge Management (I-KNOW '10)}, owner = {woidda}, pages = {11-18}, title = {A Corporate Tagging Framework as Integration Service for Knowledge Workers}, volume = 10, year = 2010 } @inproceedings{Kim2008, address = {Berlin, Deutschland}, author = {Kim, Hak Lae and Scerri, Simon and Breslin, John G. and Decker, Stefan and Kim, Hong Gee}, booktitle = {{Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications}}, interhash = {9c5f5af6f47a1a563dbb405c5a58a3cc}, intrahash = {7d3c3c2189394a8686ca9812d58bfe74}, pages = {128--137}, publisher = {{Dublin Core Metadata Initiative}}, title = {{The State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies}}, year = 2008 } @article{citeulike:8506476, abstract = {{Social tagging systems pose new challenges to developers of recommender systems. As observed by recent research, traditional implementations of classic recommender approaches, such as collaborative filtering, are not working well in this new context. To address these challenges, a number of research groups worldwide work on adapting these approaches to the specific nature of social tagging systems. In joining this stream of research, we have developed and evaluated two enhancements of user-based collaborative filtering algorithms to provide recommendations of articles on Cite ULike, a social tagging service for scientific articles. The result obtained after two phases of evaluation suggests that both enhancements are beneficial. Incorporating the number of raters into the algorithms, as we do in our NwCF approach, leads to an improvement of precision, while tag-based BM25 similarity measure, an alternative to Pearson correlation for calculating the similarity between users and their neighbors, increases the coverage of the recommendation process.}}, address = {Los Alamitos, CA, USA}, author = {Santander, Denis P. and Brusilovsky, Peter}, citeulike-article-id = {8506476}, citeulike-linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/WI-IAT.2010.261}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/WI-IAT.2010.261}, doi = {10.1109/WI-IAT.2010.261}, interhash = {dd320da969151c01cf270976c0803274}, intrahash = {2c8764f2fe11ef1ae43fc0a5b51301ae}, isbn = {978-0-7695-4191-4}, journal = {Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on}, pages = {136--142}, posted-at = {2011-01-05 00:19:36}, priority = {0}, publisher = {IEEE Computer Society}, title = {{Improving Collaborative Filtering in Social Tagging Systems for the Recommendation of Scientific Articles}}, url = {http://dx.doi.org/10.1109/WI-IAT.2010.261}, volume = 1, year = 2010 } @inproceedings{conf/wsdm/HeymannPG10, author = {Heymann, Paul and Paepcke, Andreas and Garcia-Molina, Hector}, booktitle = {WSDM}, crossref = {conf/wsdm/2010}, editor = {Davison, Brian D. and Suel, Torsten and Craswell, Nick and Liu, Bing}, ee = {http://doi.acm.org/10.1145/1718487.1718495}, interhash = {d4f72ed57e6b99dbe32e18e218d81ef5}, intrahash = {12579231cd5449f9a40cba9924975f09}, isbn = {978-1-60558-889-6}, pages = {51-60}, publisher = {ACM}, title = {Tagging human knowledge.}, url = {http://dblp.uni-trier.de/db/conf/wsdm/wsdm2010.html#HeymannPG10}, year = 2010 } @inproceedings{Van-Damme2007, address = {Innsbruck}, author = {Damme, C{\'e}line Van and Hepp, Martin and Siorpaes, Katharina}, bdsk-file-1 = {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}, bdsk-url-1 = {http://www.kde.cs.uni-kassel.de/ws/eswc2007/proc/ProceedingsSemnet07.pdf}, booktitle = {Bridging the Gap between Semantic Web and Web 2.0 (SemNet 2007)}, date-added = {2009-08-17 11:40:57 +0200}, date-modified = {2010-01-04 09:30:08 +0100}, interhash = {c8d1bcaa606229417f1c3f0f27c5f0e0}, intrahash = {8d57c1e57c7aba60acb767e3d5b0fa13}, pages = {57--70}, title = {FolksOntology: An Integrated Approach for Turning Folksonomies into Ontologies}, url = {http://www.kde.cs.uni-kassel.de/ws/eswc2007/proc/ProceedingsSemnet07.pdf}, urldate = {28.5.2008}, year = 2007 } @inproceedings{conf/wsdm/HeymannG09, author = {Heymann, Paul and Garcia-Molina, Hector}, booktitle = {WSDM (Late Breaking-Results)}, crossref = {conf/wsdm/2009}, date = {2009-03-10}, editor = {Baeza-Yates, Ricardo A. and Boldi, Paolo and Ribeiro-Neto, Berthier A. and Cambazoglu, Berkant Barla}, ee = {http://www.wsdm2009.org/heymann_2009_tagging.pdf}, interhash = {67ea8530c8f0fd5374d35264213d48aa}, intrahash = {bbea77e3d3ce24dce7be0b3385889186}, isbn = {978-1-60558-390-7}, publisher = {ACM}, title = {Contrasting Controlled Vocabulary and Tagging: Experts Choose the Right Names to Label the Wrong Things.}, url = {http://dblp.uni-trier.de/db/conf/wsdm/wsdm2009.html#HeymannG09}, year = 2009 } @inproceedings{1255198, abstract = {Social bookmarking is an emerging type of a Web service that helps users share, classify, and discover interesting resources. In this paper, we explore the concept of an enhanced search, in which data from social bookmarking systems is exploited for enhancing search in the Web. We propose combining the widely used link-based ranking metric with the one derived using social bookmarking data. First, this increases the precision of a standard link-based search by incorporating popularity estimates from aggregated data of bookmarking users. Second, it provides an opportunity for extending the search capabilities of existing search engines. Individual contributions of bookmarking users as well as the general statistics of their activities are used here for a new kind of a complex search where contextual, temporal or sentiment-related information is used. We investigate the usefulness of social bookmarking systems for the purpose of enhancing Web search through a series of experiments done on datasets obtained from social bookmarking systems. Next, we show the prototype system that implements the proposed approach and we present some preliminary results.}, address = {New York, NY, USA}, author = {Yanbe, Yusuke and Jatowt, Adam and Nakamura, Satoshi and Tanaka, Katsumi}, booktitle = {JCDL '07: Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries}, doi = {http://doi.acm.org/10.1145/1255175.1255198}, interhash = {13ebfc0942b5908890c3caaa7046fe50}, intrahash = {d896ae22bc7b52edefbfb9cdb373cf83}, isbn = {978-1-59593-644-8}, location = {Vancouver, BC, Canada}, pages = {107--116}, publisher = {ACM}, title = {Can social bookmarking enhance search in the web?}, url = {http://portal.acm.org/citation.cfm?id=1255198}, year = 2007 } @inproceedings{1661779, abstract = {A folksonomy refers to a collection of user-defined tags with which users describe contents published on the Web. With the flourish of Web 2.0, folksonomies have become an important mean to develop the Semantic Web. Because tags in folksonomies are authored freely, there is a need to understand the structure and semantics of these tags in various applications. In this paper, we propose a learning approach to create an ontology that captures the hierarchical semantic structure of folksonomies. Our experimental results on two different genres of real world data sets show that our method can effectively learn the ontology structure from the folksonomies.}, address = {San Francisco, CA, USA}, author = {Tang, Jie and fung Leung, Ho and Luo, Qiong and Chen, Dewei and Gong, Jibin}, booktitle = {IJCAI'09: Proceedings of the 21st international jont conference on Artifical intelligence}, interhash = {17f95a6ba585888cf45443926d8b7e98}, intrahash = {7b335f08a288a79eb70eff89f1ec7630}, location = {Pasadena, California, USA}, pages = {2089--2094}, publisher = {Morgan Kaufmann Publishers Inc.}, title = {Towards ontology learning from folksonomies}, url = {http://ijcai.org/papers09/Papers/IJCAI09-344.pdf}, year = 2009 } @incollection{citeulike:6386729, abstract = {Collaborative tagging can help users organize, share and retrieve information in an easy and quick way. For the collaborative tagging information implies user's important personal preference information, it can be used to recommend personalized items to users. This paper proposes a novel tag-based collaborative filtering approach for recommending personalized items to users of online communities that are equipped with tagging facilities. Based on the distinctive three dimensional relationships among users, tags and items, a new similarity measure method is proposed to generate the neighborhood of users with similar tagging behavior instead of similar implicit ratings. The promising experiment result shows that by using the tagging information the proposed approach outperforms the standard user and item based collaborative filtering approaches.}, address = {Berlin, Heidelberg}, author = {Liang, Huizhi and Xu, Yue and Li, Yuefeng and Nayak, Richi}, booktitle = {Rough Sets and Knowledge Technology }, chapter = 84, citeulike-article-id = {6386729}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-02962-2\_84}, citeulike-linkout-1 = {http://www.springerlink.com/content/f66k11352q386379}, doi = {10.1007/978-3-642-02962-2\_84}, editor = {Wen, Peng and Li, Yuefeng and Polkowski, Lech and Yao, Yiyu and Tsumoto, Shusaku and Wang, Guoyin}, interhash = {80e8a1d0263296925609dbd5b72b7d48}, intrahash = {bf98d7c1fee5f2f188f529701e70199f}, isbn = {978-3-642-02961-5}, pages = {666--673}, posted-at = {2009-12-15 15:06:20}, priority = {2}, publisher = {Springer Berlin Heidelberg}, title = {Tag Based Collaborative Filtering for Recommender Systems}, url = {http://dx.doi.org/10.1007/978-3-642-02962-2\_84}, volume = 5589, year = 2009 } @inproceedings{taggingsem08, abstract = {At present tagging is experimenting a great diffusion as the most adopted way to collaboratively classify resources over the Web. In this paper, after a detailed analysis of the attempts made to improve the organization and structure of tagging systems as well as the usefulness of this kind of social data, we propose and evaluate the Tag Disambiguation Algorithm, mining del.icio.us data. It allows to easily semantify the tags of the users of a tagging service: it automatically finds out for each tag the related concept of Wikipedia in order to describe Web resources through senses. On the basis of a set of evaluation tests, we analyze all the advantages of our sense-based way of tagging, proposing new methods to keep the set of users tags more consistent or to classify the tagged resources on the basis of Wikipedia categories, YAGO classes or Wordnet synsets. We discuss also how our semanitified social tagging data are strongly linked to DBPedia and the datasets of the Linked Data community. }, author = {Tesconi, Maurizio and Ronzano, Francesco and Marchetti, Andrea and Minutoli, Salvatore}, crossref = {CEUR-WS.org/Vol-405}, interhash = {0c1c96b41a0af8512c20a7d41504640f}, intrahash = {348a962fe13e0b605ffc53d592464c24}, title = {Semantify del.icio.us: Automatically Turn your Tags into Senses}, url = {http://CEUR-WS.org/Vol-405/paper8.pdf}, year = 2008 } @inproceedings{1502661, abstract = {While recommender systems tell users what items they might like, explanations of recommendations reveal why they might like them. Explanations provide many benefits, from improving user satisfaction to helping users make better decisions. This paper introduces tagsplanations, which are explanations based on community tags. Tagsplanations have two key components: tag relevance, the degree to which a tag describes an item, and tag preference, the user's sentiment toward a tag. We develop novel algorithms for estimating tag relevance and tag preference, and we conduct a user study exploring the roles of tag relevance and tag preference in promoting effective tagsplanations. We also examine which types of tags are most useful for tagsplanations.}, address = {New York, NY, USA}, author = {Vig, Jesse and Sen, Shilad and Riedl, John}, booktitle = {IUI '09: Proceedingsc of the 13th international conference on Intelligent user interfaces}, doi = {http://doi.acm.org/10.1145/1502650.1502661}, interhash = {a6d866cf13c75130c1969c9e40606fd1}, intrahash = {1e74fa227a24f49d8f6b17a02ea96db5}, isbn = {978-1-60558-168-2}, location = {Sanibel Island, Florida, USA}, pages = {47--56}, publisher = {ACM}, title = {Tagsplanations: explaining recommendations using tags}, url = {http://portal.acm.org/citation.cfm?id=1502650.1502661}, year = 2008 } @inproceedings{heymann2008social, abstract = {In this paper, we look at the "social tag prediction" problem. Given a set of objects, and a set of tags applied to those objects by users, can we predict whether a given tag could/should be applied to a particular object? We investigated this question using one of the largest crawls of the social bookmarking system del.icio.us gathered to date. For URLs in del.icio.us, we predicted tags based on page text, anchor text, surrounding hosts, and other tags applied to the URL. We found an entropy-based metric which captures the generality of a particular tag and informs an analysis of how well that tag can be predicted. We also found that tag-based association rules can produce very high-precision predictions as well as giving deeper understanding into the relationships between tags. Our results have implications for both the study of tagging systems as potential information retrieval tools, and for the design of such systems.}, address = {New York, NY, USA}, author = {Heymann, Paul and Ramage, Daniel and Garcia-Molina, Hector}, booktitle = {SIGIR '08: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval}, doi = {http://doi.acm.org/10.1145/1390334.1390425}, interhash = {bb9455c80cc9bd8cf95c951a1318dabc}, intrahash = {0e6023e192f539fe4fce9894b1fbca5a}, isbn = {978-1-60558-164-4}, location = {Singapore, Singapore}, pages = {531--538}, publisher = {ACM}, title = {Social tag prediction}, url = {http://portal.acm.org/citation.cfm?id=1390334.1390425}, year = 2008 } @inproceedings{1390424, address = {New York, NY, USA}, author = {Schenkel, Ralf and Crecelius, Tom and Kacimi, Mouna and Michel, Sebastian and Neumann, Thomas and Parreira, Josiane X. and Weikum, Gerhard}, booktitle = {SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval}, doi = {http://doi.acm.org/10.1145/1390334.1390424}, interhash = {17f7d73a408830fa3a66504e49f7e481}, intrahash = {1c57b638bb3bee94300d883259774493}, isbn = {978-1-60558-164-4}, location = {Singapore, Singapore}, pages = {523--530}, publisher = {ACM}, title = {Efficient top-k querying over social-tagging networks}, url = {http://portal.acm.org/citation.cfm?id=1390424}, year = 2008 } @inproceedings{HaHe06, abstract = {Tagging-based systems enable users to categorize web resources by means of tags (freely chosen keywords), in order to re-finding these resources later. Tagging is implicitly also a social indexing process, since users share their tags and resources, constructing a social tag index, so-called folksonomy. At the same time of tagging-based system, has been popularised an interface model for visual information retrieval known as Tag-Cloud. In this model, the most frequently used tags are displayed in alphabetical order. This paper presents a novel approach to Tag-Cloud�s tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. The results suggest that presented approach reduces the semantic density of tag set, and improves the visual consistency of Tag-Cloud layout.}, author = {Hassan-Montero, Y. and Herrero-Solana, V.}, booktitle = {InScit2006: International Conference on Multidisciplinary Information Sciences and Technologies}, file = {HaHe06.pdf:folksonomies\\HaHe06.pdf:PDF}, interhash = {4458142370e3c6a4fe656af2f822a0dc}, intrahash = {06f68f9fe46dc6d0f646d932e428dec9}, misc = {comment = {proposes using k-clustering and some sort of semantic sorting to refactor tag cloud layout to improve browsing. Not clear on how they actually do it.}, priority = {0}, citeulike-article-id = {2045619}}, owner = {michael}, timestamp = {2008.01.14}, title = {Improving Tag-Clouds as Visual Information Retrieval Interfaces}, url = {http://nosolousabilidad.com/hassan/improving_tagclouds.pdf}, year = 2006 } @inproceedings{1458098, address = {New York, NY, USA}, author = {Song, Yang and Zhang, Lu and Giles, C. Lee}, booktitle = {CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge mining}, doi = {http://doi.acm.org/10.1145/1458082.1458098}, interhash = {5c03bc1e658b6d44f053944418bdaec3}, intrahash = {d330a3537b4a14fbd40661424ec8e465}, isbn = {978-1-59593-991-3}, location = {Napa Valley, California, USA}, pages = {93--102}, publisher = {ACM}, title = {A sparse gaussian processes classification framework for fast tag suggestions}, url = {http://portal.acm.org/citation.cfm?id=1458098}, year = 2008 } @inproceedings{1316677, address = {New York, NY, USA}, author = {Farooq, Umer and Kannampallil, Thomas G. and Song, Yang and Ganoe, Craig H. and Carroll, John M. and Giles, Lee}, booktitle = {GROUP '07: Proceedings of the 2007 international ACM conference on Conference on supporting group work}, doi = {http://doi.acm.org/10.1145/1316624.1316677}, interhash = {66928ca91bf0d777b848fe6f7a55de20}, intrahash = {5d0b61727d81aed019ba4297090108ca}, isbn = {978-1-59593-845-9}, location = {Sanibel Island, Florida, USA}, pages = {351--360}, publisher = {ACM}, title = {Evaluating tagging behavior in social bookmarking systems: metrics and design heuristics}, url = {http://portal.acm.org/citation.cfm?id=1316677&coll=Portal&dl=GUIDE&CFID=9767993&CFTOKEN=86305662}, year = 2007 }