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FCA-Based Ontology Learning from Unstructured Textual Data

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Mining Intelligence and Knowledge Exploration (MIKE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

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Authors and Affiliations

  1. University of Neuchâtel, Neuchâtel, Switzerland

    Simin Jabbari & Kilian Stoffel

  2. Diagnostics Data Science Lab, F. Hoffmann-La Roche Ltd., Basel, Switzerland

    Simin Jabbari

Authors
  1. Simin Jabbari
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  2. Kilian Stoffel
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Corresponding author

Correspondence to Simin Jabbari .

Editor information

Editors and Affiliations

  1. Technical University of Cluj-Napoca, Cluj-Napoca, Romania

    Adrian Groza

  2. Indian Institute of Information Technology, Sri City, India

    Rajendra Prasath

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Jabbari, S., Stoffel, K. (2018). FCA-Based Ontology Learning from Unstructured Textual Data. In: Groza, A., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2018. Lecture Notes in Computer Science(), vol 11308. Springer, Cham. https://doi.org/10.1007/978-3-030-05918-7_1

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  • DOI: https://doi.org/10.1007/978-3-030-05918-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05917-0

  • Online ISBN: 978-3-030-05918-7

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