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Semantifying triples from open information extraction systems


Dutta, Arnab ; Meilicke, Christian ; Stuckenschmidt, Heiner


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DOI: https://doi.org/10.3233/978-1-61499-421-3-111
URL: https://madoc.bib.uni-mannheim.de/36881
Additional URL: http://ebooks.iospress.nl/publication/37200
URN: urn:nbn:de:bsz:180-madoc-368811
Document Type: Conference or workshop publication
Year of publication: 2014
Book title: STAIRS 2014 : Proceedings of the 7th European Starting AI Researcher Symposium
The title of a journal, publication series: Frontiers in Artificial Intelligence and Applications
Volume: 264
Page range: 111-120
Conference title: STAIRS 2014
Location of the conference venue: Prague, Czech Republic
Date of the conference: 18.-19.8.2014
Publisher: Endriss, Ulle
Place of publication: Clifton, Va. [u.a.]
Publishing house: IOS Press
ISBN: 978-1-61499-420-6 , 978-1-61499-421-3
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Abstract: The last few years have witnessed some remarkable success of the stateof- the art unsupervised knowledge extraction systems like NELL and REVERB. These systems are gifted with typically web-scale coverage but are often plagued with ambiguity due to lack of proper schema or unique identifiers for the instances. This classifies them apart from extraction systems like DBPEDIA, YAGO or FREEBASE which have precise information content but have smaller coverage. In this work we bring together the former to enrich the later with high precision novel facts and present a statistical approach to discover new knowledge. In particular, we semantify NELL triples using DBPEDIA.




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ORCID: Dutta, Arnab, Meilicke, Christian ORCID: https://orcid.org/0000-0002-0198-5396 and Stuckenschmidt, Heiner ORCID: https://orcid.org/0000-0002-0209-3859

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