Gao, J. and Mazumdar, S. (2016) Exploiting linked open data to uncover entity types. In: Gandon, F., Cabrio, E., Stankovic, M. and Zimmermann, A., (eds.) Semantic Web Evaluation Challenges: Second SemWebEval Challenge at ESWC 2015, Portorož, Slovenia, May 31 - June 4, 2015, Revised Selected Papers. Second SemWebEval Challenge at ESWC 2015, 31 May - 04 Jun 2015, Portorož, Slovenia. Communications in Computer and Information Science, CCIS 548 . Springer International Publishing , pp. 51-62. ISBN 9783319255170
Abstract
Extracting structured information from text plays a crucial role in automatic knowledge acquisition and is at the core of any knowledge representation and reasoning system. Traditional methods rely on hand-crafted rules and are restricted by the performance of various linguistic pre-processing tools. More recent approaches rely on supervised learning of relations trained on labelled examples, which can be manually created or sometimes automatically generated (referred as distant supervision). We propose a supervised method for entity typing and alignment. We argue that a rich feature space can improve extraction accuracy and we propose to exploit Linked Open Data (LOD) for feature enrichment. Our approach is tested on task-2 of the Open Knowledge Extraction challenge, including automatic entity typing and alignment. Our approach demonstrate that by combining evidences derived from LOD (e.g. DBpedia) and conventional lexical resources (e.g. WordNet) (i) improves the accuracy of the supervised induction method and (ii) enables easy matching with the Dolce+DnS Ultra Lite ontology classes.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2015 Springer International Publishing Switzerland. This is an author produced version of a paper subsequently published in Communications in Computer and Information Science. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC) EP/J019488/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 26 Sep 2016 11:03 |
Last Modified: | 26 Jun 2024 15:47 |
Status: | Published |
Publisher: | Springer International Publishing |
Series Name: | Communications in Computer and Information Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-25518-7_5 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:96572 |