BibSonomy publications for /user/hothoTue Jul 15 18:12:46 CEST 2008An efficient reduction of ranking to classification2007ranking learning toread This paper describes an efficient reduction of the learning problem of ranking to binary classification. The reduction guarantees an average pairwise misranking regret of at most that of the binary classifier regret, improving a recent result of Balcan et al which only guarantees a factor of 2. Moreover, our reduction applies to a broader class of ranking loss functions, admits a simpler proof, and the expected running time complexity of our algorithm in terms of number of calls to a classifier or preference function is improved from $\Omega(n^2)$ to $O(n \log n)$. In addition, when the top $k$ ranked elements only are required ($k \ll n$), as in many applications in information extraction or search engines, the time complexity of our algorithm can be further reduced to $O(k \log k + n)$. Our reduction and algorithm are thus practical for realistic applications where the number of points to rank exceeds several thousands. Much of our results also extend beyond the bipartite case previously studied.Sat Jul 12 12:19:07 CEST 2008WebKDD 2008 Workshop on Web Mining and Web Usage AnalysisAugustTo Appear{Detecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies}2008community clusterig folksonomy detection toread Wed Jul 02 16:57:41 CEST 2008Innovations in Information Technology, 2007. Innovations '07. 4th International Conference on208-212CoolRank: A Social Solution for Ranking Bookmarked Web Resources2007web ranking * folkrank toread folksonomy 2.0 Users tag resources for a variety of reasons and using a variety of conventions. The tags that they provide are stored in social bookmarking services, so these services can provide a rich gateway to a wide and interesting quantity of web resources. The cognitive effort that has gone into making these tags has presumably added value to the description of the resource. In this work we utilize the quantitative value of these tags for ranking bookmarked web resources in social bookmarking services. Our proposed solution is called CoolRank, a simple and intuitive model to rank bookmarked web resources in a social bookmarking service, such as del.icio.us. CoolRank makes use of both quantitative information, based on the number of people who have bookmarked a web resource, and subjective information, based on the words people have used in their tags.Tue Jul 01 15:19:39 CEST 2008Bonn, GermanyProceedings of the 22nd International Conference on Machine Learning - Learning in Web Search (LWS 2005)18--26Learning Word-to-Concept Mappings for Automatic Text Classification2005wordnet concept topic classification tc model text Wed Jun 25 10:36:15 CEST 2008MünchenWeb 2.0 in der Unternehmenspraxis: Grundlagen, Fallstudien und Trends zum Einsatz von Social Software26-38Social Bookmarking2008folksonomy bookmarking myown 2008 social Tue Jun 17 22:31:22 CEST 2008Semantic Web and Web 2.0Web Semantics: Science, Services and Agents on the World Wide Webfeb 138--53Discovering Shared Conceptualizations in Folksonomies62008discovering myown fca tagging formal folksonomy bibsonomy concept analysis 2008 Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.Tue Jun 17 22:30:50 CEST 2008Journal of Applied Ontologyto appearAEON - An Approach to the Automatic Evaluation of Ontologies2008evaluation ontology sw ml automatic 2008 myown Tue Jun 17 21:04:00 CEST 2008ESOECEUR Workshop ProceedingsProceedings of the First International Workshop on Emergent Semantics and Ontology Evolution, ESOE 2007, co-located with ISWC 2007 + ASWC 2007, Busan, Korea, November 12th, 20072922007semantics 2007 myown ontology workshop Sun Jun 15 15:04:05 CEST 2008NatureJan71746-6Free journal-ranking tool enters citation market4512008citation research ranking index rank tools Thu Jun 12 16:55:39 CEST 2008Berlin; New YorkInternational handbooks on information systems--Handbook on ontologies2004survey handbook semantic web ontology Thu Jun 12 08:19:04 CEST 2008Berlin, HeidelbergProceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Koreahttp://data.semanticweb.org/conference/iswc-aswc/2007/proceedingsNovember365--378LNCSWeb search personalization via social bookmarking and tagging48252007bookmarking search tagging social toread iswc In this paper, we present a new approach to web search personalization based on user collaboration and sharing of information about web documents. The proposed personalization technique separates data collection and user profiling from the information system whose contents and indexed documents are being searched for, i.e. the search engines, and uses social bookmarking and tagging to re-rank web search results. It is independent of the search engine being used, so users are free to choose the one they prefer, even if their favorite search engine does not natively support personalization. We show how to design and implement such a system in practice and investigate its feasibility and usefulness with large sets of real-word data and a user study.Wed Jun 04 11:45:49 CEST 2008New York, NY, USAICML '05: Proceedings of the 22nd international conference on Machine learning41--48Multi-way distributional clustering via pairwise interactions2005hierarchical coclustering multiway multi clustering graph Mon Jun 02 15:12:04 CEST 2008Physical Review ESeptemberFast algorithm for detecting community structure in networks692003networks fast algorithm community clustering Thu May 29 17:31:25 CEST 2008Los Alamitos, CA, USAitng388-393Web Page Recommender System based on Folksonomy Mining for ITNG ’06 Submissions002006folksonomy recommender Wed May 21 10:23:17 CEST 2008AI Communications4245 - 262Network Properties of Folksonomies202007properties network folksonomies summerschool kdubiq 2007 sosbuch myown Tue May 20 21:48:14 CEST 2008Alicante, SpainProceedings of the 10th International Conference on Applications of Natural Language to Information Systems (NLDB)JUN227-238Lecture Notes in Computer ScienceText2Onto - A Framework for Ontology Learning and Data-driven Change Discovery35132005nlp learning text2onto ol ontology kaon Tue May 20 17:43:51 CEST 2008Information, Wissenschaft und PraxisOCTsee the special issue for more contributions related to the Semantic Web6-7315-320Ontologies on Demand? - A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text572006survey learning ontology toread Mon May 19 14:43:47 CEST 2008Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008101-113A Comparison of Social Bookmarking with Traditional Search49562008search query log bookmarking myown social 2008 Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system’s data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users. In this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings. Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system.Mon May 19 08:38:59 CEST 2008Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems2008similarity learning ol 2008 analysis myown ontology tag semantic Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.Tue May 13 09:58:22 CEST 2008AI Magazine433-44Statistical Techniques for Natural Language Parsing181997tagging learning model lecture nlp