Exploiting linked open data to uncover entity types

GAO, Jie and MAZUMDAR, Suvodeep (2015). Exploiting linked open data to uncover entity types. In: GANDON, Fabian, CABRIO, Elena, STANKOVIC, Milan and ZIMMERMAN, Antoine, (eds.) Semantic web evaluation challenges : Second SemWebEval Challenge at ESWC 2015, Portorož, Slovenia, May 31 - June 4, 2015, revised selected papers. Communications in Computer and Information Science (548). Cham, Springer, 51-62.

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Official URL: https://link.springer.com/chapter/10.1007/978-3-31...
Link to published version:: https://doi.org/10.1007/978-3-319-25518-7_5

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.

Item Type: Book Section
Additional Information: Paper originally presented at Second SemWebEval Challenge at ESWC 2015, Portorož, Slovenia, May 31 - June 4, 2015. Book series ISSN: 1865-0929. Semantic Web Evaluation Challenge at Extended Semantic Web Conference
Departments: Faculty of Science, Technology and Arts > Computing
Identification Number: https://doi.org/10.1007/978-3-319-25518-7_5
Depositing User: Suvodeep Mazumdar
Date Deposited: 19 Jan 2018 12:55
Last Modified: 27 Jan 2018 21:25
URI: http://shura.shu.ac.uk/id/eprint/16904

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