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FCA-Based Ontology Learning from Unstructured Textual Data

  • Simin JabbariEmail author
  • Kilian Stoffel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11308)

Abstract

Ontologies have been frequently used for representing domain knowledge. They have lots of applications in semantic knowledge extraction. However, learning ontologies especially from unstructured data is a difficult yet an interesting challenge. In this paper, we introduce a pipeline for learning ontology from a text corpus in a semi-automated fashion using Natural Language Processing (NLP) and Formal Concept Analysis (FCA). We apply our proposed method on a small given corpus that consists of some news documents in IT and pharmaceutical domain. We then discuss the potential applications of the proposed model and ideas on how to improve it even further.

Keywords

Ontology engineering Semantic knowledge extraction Formal concept analysis Natural language processing Concept lattice 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of NeuchâtelNeuchâtelSwitzerland
  2. 2.Diagnostics Data Science LabF. Hoffmann-La Roche Ltd.BaselSwitzerland

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