Semantic Web Evaluation Challenge

Semantic Web Evaluation Challenges pp 51-62

Exploiting Linked Open Data to Uncover Entity Types

Conference paper

DOI: 10.1007/978-3-319-25518-7_5

Part of the Communications in Computer and Information Science book series (CCIS, volume 548)
Cite this paper as:
Gao J., Mazumdar S. (2015) Exploiting Linked Open Data to Uncover Entity Types. In: Gandon F., Cabrio E., Stankovic M., Zimmermann A. (eds) Semantic Web Evaluation Challenges. Communications in Computer and Information Science, vol 548. Springer, Cham


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.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.OAK Group, Department of Computer ScienceUniversity of SheffieldSheffieldUK

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