Integrating Open and Closed Information Extraction: Challenges and First Steps

Dutta, Arnab ; Niepert, Mathias ; Meilicke, Christian ; Ponzetto, Simone Paolo

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Document Type: Conference or workshop publication
Year of publication: 2013
Book title: NLP-DBPEDIA 2013 : Proceedings of the NLP & DBpedia workshop co-located with the 12th International Semantic Web Conference (ISWC 2013) Sydney, Australia, October 22, 2013
The title of a journal, publication series: CEUR Workshop Proceedings
Volume: 1064
Page range: 50-61
Date of the conference: October 22, 2013
Publisher: Hellmann, Sebastian
Place of publication: Aachen, Germany
Publishing house: RWTH Aachen
ISSN: 1613-0073
Publication language: English
Institution: School of Business Informatics and Mathematics > Praktische Informatik II (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Keywords (English): Information extraction , Entity Linking , Ontologies
Abstract: Over the past years, state-of-the-art information extraction (IE) systems such as NELL and ReVerb have achieved impres sive results by producing very large knowledge resources at web scale with minimal supervision. However, these resources lack the schema information, exhibit a high degree of ambiguity, and are diffcult even for humans to interpret. Working with such resources becomes easier if there is a structured information base to which the resources can be linked. In this paper, we introduce the integration of open information extraction projects with Wikipedia-based IE projects that maintain a logical schema, as an important challenge for the NLP, semantic web, and machine learning communities. We describe the problem, present a gold-standard benchmark, and take the first steps towards a data-driven solution to the problem. This is especially promising, since NELL and ReVerb typically achieve a very large coverage, but still still lack a full-fledged clean ontological structure which, on the other hand, could be provided by large-scale ontologies like DBpedia or YAGO.
Additional information: Online-Ressource

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