Benz, D. & Hotho, A.: Position Paper: Ontology Learning from Folksonomies.. In: Hinneburg, A. (Hrsg.):
LWA 2007: Lernen - Wissen - Adaption, Halle, September 2007, Workshop Proceedings (LWA). Martin-Luther-University Halle-Wittenberg, 2007, S. 109-112
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Berendt, B.; Hotho, A.; Mladenic, D. & Semeraro, G. (Hrsg.):
From Web to Social Web: Discovering and Deploying User and Content Profiles . Springer, 2007 (LNCS 4736)
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This book constitutes the refereed proceedings of the Workshop on Web Mining, WebMine 2006, held in Berlin, Germany, September 18th, 2006. Topics included are data mining based on analysis of bloggers and tagging, web mining, XML mining and further techniques of knowledge discovery. The book is especially valuable for those interested in the aspects of the Social Web (Web 2.0) and its inherent dynamic and diversity of user-generated content.
Cattuto, C.; Schmitz, C.; Baldassarri, A.; Servedio, V. D. P.; Loreto, V.; Hotho, A.; Grahl, M. & Stumme, G.: Network Properties of Folksonomies. In:
AI Communications 20 (2007), Nr. 4, S. 245 - 262
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Proceedings of the First International Workshop on Emergent Semantics and Ontology Evolution, ESOE 2007, co-located with ISWC 2007 + ASWC 2007, Busan, Korea, November 12th, 2007. CEUR Workshop Proceedings , 2007
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Dourisboure, Y.; Geraci, F. & Pellegrini, M.: Extraction and Classification of Dense Communities in the WebAuthors.
Proc of the wwww. 2007
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The World Wide Web (WWW) is rapidly becoming important for society as a medium for sharing data, information and services, and there is a growing interest in tools for understanding collective behaviors and emerging phenomena in the WWW. In this paper we focus on the problem of searching and classifying communities in the web. Loosely speaking a community is a group of pages related to a common interest. More formally communities have been associated in the computer science literature with the existence of a locally dense sub-graph of the web-graph (where web pages are nodes and hyper-links are arcs of the web-graph). The core of our contribution is a new scalable algorithm for finding relatively dense subgraphs in massive graphs. We apply our algorithm on web-graphs built on three publicly available large crawls of the web (with raw sizes up to 120M nodes and 1G arcs). The effectiveness of our algorithm in finding dense subgraphs is demonstrated experimentally by embedding artificial communities in the web-graph and counting how many of these are blindly found. Effectiveness increases with the size and density of the communities: it is close to 100%for communities of a thirty nodes or more (even at low density). It is still about 80%even for communities of twenty nodes with density over $50 of the arcs present. At the lower extremes the algorithm catches 35%of dense communities made of ten nodes. We complete our Community Watch system by clustering the communities found in the web-graph into homogeneous groups by topic and labelling each group by representative keywords.
Getoor, B. T. L. (Hrsg.):
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) . 2007
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Grahl, M.; Hotho, A. & Stumme, G.: Conceptual Clustering of Social Bookmark Sites. In: Hinneburg, A. (Hrsg.):
Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007). Martin-Luther-Universität Halle-Wittenberg, 2007, S. 50-54
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Grahl, M.; Hotho, A. & Stumme, G.: Conceptual Clustering of Social Bookmarking Sites.
7th International Conference on Knowledge Management (I-KNOW '07). Graz, Austria: Know-Center, 2007, S. 356-364
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Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of the system, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.
Hotho, A. & Stumme, G.: Mining the World Wide Web. In:
Künstliche Intelligenz (2007), Nr. 3, S. 5-8
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Jäschke, R.; Hotho, A.; Schmitz, C. & Stumme, G.: Analysis of the Publication Sharing Behaviour in BibSonomy.
Proceedings of the 15th International Conference on Conceptual Structures. Sheffield, England: 2007 (LNCS 4604)
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BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.
Jäschke, R.; Grahl, M.; Hotho, A.; Krause, B.; Schmitz, C. & Stumme, G.: Organizing Publications and Bookmarks in BibSonomy. In: Alani, H.; Noy, N.; Stumme, G.; Mika, P.; Sure, Y. & Vrandecic, D. (Hrsg.):
Workshop on Social and Collaborative Construction of Structured Knowledge (CKC 2007) at WWW 2007. Banff, Canada: 2007
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Jäschke, R.; Marinho, L.; Hotho, A.; Schmidt-Thieme, L. & Stumme, G.: Tag Recommendations in Folksonomies. In: Hinneburg, A. (Hrsg.):
Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007). Martin-Luther-Universität Halle-Wittenberg, 2007, S. 13-20
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Jäschke, R.; Marinho, L. B.; Hotho, A.; Schmidt-Thieme, L. & Stumme, G.: Tag Recommendations in Folksonomies. In: Kok, J. N.; Koronacki, J.; de Mántaras, R. L.; Matwin, S.; Mladenic, D. & Skowron, A. (Hrsg.):
Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings. Springer, 2007 (Lecture Notes in Computer Science 4702), S. 506-514
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Schmitz, C.; Grahl, M.; Hotho, A.; Stumme, G.; Catutto, C.; Baldassarri, A.; Loreto, V. & Servedio, V. D. P.: Network Properties of Folksonomies.
Proc. WWW2007 Workshop ``Tagging and Metadata for Social Information Organization''. Banff: 2007
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Specia, L. & Motta, E.: Integrating Folksonomies with the Semantic Web.
Proc. of the European Semantic Web Conference (ESWC2007). Berlin Heidelberg, Germany: Springer-Verlag, 2007 (LNCS 4519), S. 624-639
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Völker, J.; Vrandecic, D.; Sure, Y. & Hotho, A.: Learning Disjointness. In: Franconi, E.; Kifer, M. & May, W. (Hrsg.):
Proceedings of the European Semantic Web Conference, ESWC2007. Springer-Verlag, 2007 (Lecture Notes in Computer Science 4519)
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