Alexander Maedche

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. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. 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. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open.

History of cello and speaking tone of voice[edit]

European conference on principles of data mining and knowledge discovery …, 2002 Proceedings of the 12th international conference on software engineering and …, 2000

Profile Mining in CVS-Logs and Face-to-Face Contacts for Recommending Software Developers Recommender Systems are well known applications for increasing the level of relevant content over the noise that continuously grows as more and more content becomes available online. STS are open and inherently social; features that have been proven to encourage participation. It would therefore be of tremendous help for system developers and users to know which personal data are needed for spam detection and which can be ignored.

In: SocialCom 2012 – Proceedings of the 2012 IEEE Fourth International Conference on Social Computing , Amsterdam, NL, September 3-6, 2012. Conceptual Knowledge Processing is based on the mathematical theory of Formal Concept Analysis which has become a successful theory for data analysis during the last two decades. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy.

Public Transportation to Gerd Münstermann Steuerberater inside Hannover

To bridge the gap between folksonomies and the Semantic Web, we apply association rule mining to extract relations and present a deeper analysis of statistical measures which can be used to extract tag relations. Based on the analysis we made on a large folksonomy dataset, we present the application of data mining algorithms on three different tasks, namely spam detection, ranking and recommendation. The application and adaptation of known data mining algorithms to folksonomies with the goal to support the users of such systems and to extract valuable information with a special focus on the Semantic Web is the main target of this paper. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.}, This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.

Proceedings of the 18th international conference on World wide web, 641-650, 2009 European Conference on Principles of Data Mining and Knowledge Discovery …, 2007 Unveiling the hidden bride: deep annotation for mapping and migrating legacy data to the semantic web R Volz, S Handschuh, S Staab, L Stojanovic, N Stojanovic Proceedings of the 12th international conference on World Wide Web, 431-438, 2003 Proceedings of the 16th international conference on World Wide Web, 845-854, 2007

Ontology engineering beyond the modeling of concepts and relations S Staab, A Maedche A conceptual architecture for semantic web enabled web services C Bussler, D Fensel, A Maedche IJCAI-2003, Acapulco, Mexico, August 2003 (see long version at ontology workshop and revised version at K-Cap-2003). Observing and Recommending from a Social Web with Biases , Technical Report, 37 pages, University of Southampton, March 2016.

Extended Semantic Web Conference (ESWC2010), Demo Paper, Heraklion, Greece, May 30-June Conference on Advanced Information Systems Engineering , London, June 20-24, 2011.

This approach is complemented by presenting two approaches to extract conceptualizations from folksonomies. As these systems are easy to use, they attract huge masses of users. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics.

booktitle = WWW9 — Proceedings of the 9th International World Wide Web Conference, Amsterdam, The Netherlands, title = Semantic Web Mining and the Representation, Analysis, and Evolution of Web Space, Semantic Web Mining and the Representation, Analysis, and Evolution of Web Space.

gerd handschuh

The more personal data users reveal, the more difficult it becomes to control its disclosure in the web. With the increased popularity of Web 2.0 services in the last years data privacy has become a major concern for users. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags.

Assessing the degree of semantic relatedness between words is an important task with a variety of semantic applications, such as ontology learning for the Semantic Web, semantic search or query expansion. abstract = In this paper we describe our post-evaluation results for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). In this paper we describe our post-evaluation results for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2).

gerd handschuh

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