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, May 31 - June 4, 2015, Portorož, Slovenia. 548, 548 . Springer International Publishing , pp. 51-62. ISBN 978-3-319-25517-0
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
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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. | ||||
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Institution: | The University of Sheffield | ||||
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) | ||||
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Depositing User: | Symplectic Sheffield | ||||
Date Deposited: | 26 Sep 2016 11:03 | ||||
Last Modified: | 01 Nov 2016 05:57 | ||||
Published Version: | http://dx.doi.org/10.1007/978-3-319-25518-7_5 | ||||
Status: | Published | ||||
Publisher: | Springer International Publishing | ||||
Series Name: | 548 | ||||
Refereed: | Yes | ||||
Identification Number: | https://doi.org/10.1007/978-3-319-25518-7_5 |