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%0 Conference Proceedings
%A Benz, Dominik; Grobelnik, Marko; Hotho, Andreas; Jäschke, Robert; Mladenic, Dunja; Servedio, Vito D. P.; Sizov, Sergej & Szomszor, Martin
%D 2008
%T Analyzing Tag Semantics Across Collaborative Tagging Systems
%E Alani, Harith; Staab, Steffen & Stumme, Gerd
%B Proceedings of the Dagstuhl Seminar on Social Web Communities
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%F benz2008analyzing
%K 2008, dagstuhl, iin2009, itegpub, myown, tag_semantics, tagorapub, ol_web2.0, widely_related
%X The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance.
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%U http://www.kde.cs.uni-kassel.de/pub/pdf/benz2008analyzing.pdf
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%0 Conference Proceedings
%A Baeza-Yates, Ricardo & Tiberi, Alessandro
%D 2007
%T Extracting semantic relations from query logs
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%B KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
%C New York, NY, USA
%I ACM
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%2 Extracting semantic relations from query logs
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%F baezayates2007extracting
%K analysis, iin2009, kdd2007, ol_web2.0, ontology_learning, query_log, toread, toread_dbe, widely_related
%X In this paper we study a large query log of more than twenty million queries with the goal of extracting the semantic relations that are implicitly captured in the actions of users submitting queries and clicking answers. Previous query log analyses were mostly done with just the queries and not the actions that followed after them. We first propose a novel way to represent queries in a vector space based on a graph derived from the query-click bipartite graph. We then analyze the graph produced by our query log, showing that it is less sparse than previous results suggested, and that almost all the measures of these graphs follow power laws, shedding some light on the searching user behavior as well as on the distribution of topics that people want in the Web. The representation we introduce allows to infer interesting semantic relationships between queries. Second, we provide an experimental analysis on the quality of these relations, showing that most of them are relevant. Finally we sketch an application that detects multitopical URLs.
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%U http://portal.acm.org/citation.cfm?id=1281192.1281204
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%A Mori, Junichiro; Tsujishita, Takumi; Matsuo, Yutaka & Ishizuka, Mitsuru
%D 2006
%T Extracting Relations in Social Networks from the Web Using Similarity Between Collective Contexts
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%B International Semantic Web Conference
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%F mori2006extracting
%K extract, folksonomy, iin2009, relations, resources, social-networks, toread, ol_web2.0, toread_dbe, data_socialnetworks, methods_relations
%X Social networks have recently garnered considerable interest. With the intention of utilizing social networks for the Semantic Web, several studies have examined automatic extraction of social networks. However, most methods have addressed extraction of the strength of relations. Our goal is extracting the underlying relations between entities that are embedded in social networks. To this end, we propose a method that automatically extracts labels that describe relations among entities. Fundamentally, the method clusters similar entity pairs according to their collective contexts in Web documents. The descriptive labels for relations are obtained from results of clustering. The proposed method is entirely unsupervised and is easily incorporated into existing social network extraction methods. Our method also contributes to ontology population by elucidating relations between instances in social networks. Our experiments conducted on entities in political social networks achieved clustering with high precision and recall. We extracted appropriate relation labels to represent the entities.
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