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DWS at the 2016 Open Knowledge Extraction Challenge: A Hearst-Like Pattern-Based Approach to Hypernym Extraction and Class Induction

  • Stefano FaralliEmail author
  • Simone Paolo Ponzetto
Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (CCIS, volume 641)

Abstract

In this paper we present a system for the 2016 edition of the Open Knowledge Extraction (OKE) Challenge. The OKE challenge promotes research in automatic extraction of structured content from textual data and its representation and publication as Linked Data. The proposed system addresses the second task of the challenge, namely “Class Induction and entity typing for Vocabulary and Knowledge Base enrichment” and combines state-of-the-art lexically-based Natural Language Processing (NLP) techniques with lexical and semantic knowledge bases to first extract hypernyms from definitional sentences and second select the most suitable class of the extracted hypernyms from those available in the DOLCE foundational ontology.

Keywords

Linked Open Data Hearst patterns Hypernym extraction Class induction 
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Notes

Acknowledgement

This work was partially funded by the Junior-professor funding programme of the Ministry of Science, Research and the Arts of the state of Baden-Württemberg, Germany (project “Deep semantic models for high-end NLP applications”).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Research Group Data and Web ScienceUniversity of MannheimMannheimGermany

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