Exploiting DBpedia for web search results clustering

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.

BASE: Schuhmacher, Michael ; Ponzetto, Simone Paolo

Google Scholar: Schuhmacher, Michael ; Ponzetto, Simone Paolo

ORCID: Schuhmacher, Michael and Ponzetto, Simone Paolo

Hits per month over past year

You have found an error? Please let us know about your desired correction here: E-Mail

Show item