Exploiting DBpedia for web search results clustering

Schuhmacher, Michael ; Ponzetto, Simone Paolo

DOI: https://doi.org/10.1145/2509558.2509574
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=1...
Additional URL: http://openreview.net/document/db119ff2-0d44-45ce-...
Document Type: Conference or workshop publication
Year of publication: 2013
Book title: AKBC '13 : Automated Knowledge Base Construction (AKBC) 2013; The 3rd Workshop on Knowledge Extraction at CIKM 2013 in San Francisco, October 27-28, 2013
Page range: 91-96
Date of the conference: 27.10.2013
Publisher: He, Qi
Place of publication: New York, NY
Publishing house: ACM
ISBN: 978-1-4503-2411-3
Publication language: English
Institution: School of Business Informatics and Mathematics > Semantic Web (Juniorprofessur) (Ponzetto 2013-2015)
School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Keywords (English): dbpedia , natural language processing , search result clustering , semantic networks
Abstract: We present a knowledge-rich approach to Web search result clustering which exploits the output of an open-domain entity linker, as well as the types and topical concepts encoded within a wide-coverage ontology. Our results indicate that, thanks to an accurate and compact semantification of the search result snippets, we are able to achieve a competitive performance on a benchmarking dataset for this task.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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