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Journal of Intelligent Systems

Editor-in-Chief: Fleyeh, Hasan


CiteScore 2018: 1.03

SCImago Journal Rank (SJR) 2018: 0.188
Source Normalized Impact per Paper (SNIP) 2018: 0.533

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2191-026X
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Volume 28, Issue 4

Issues

Extracting Conceptual Relationships and Inducing Concept Lattices from Unstructured Text

V.S. Anoop
  • Corresponding author
  • Data Engineering Lab, Indian Institute of Information Technology and Management-Kerala (IIITM-K), Thiruvananthapuram, India
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/ S. Asharaf
  • Indian Institute of Information Technology and Management-Kerala (IIITM-K), Thiruvananthapuram, India
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2017-09-26 | DOI: https://doi.org/10.1515/jisys-2017-0225

Abstract

Concept and relationship extraction from unstructured text data plays a key role in meaning aware computing paradigms, which make computers intelligent by helping them learn, interpret, and synthesis information. These concepts and relationships leverage knowledge in the form of ontological structures, which is the backbone of semantic web. This paper proposes a framework that extracts concepts and relationships from unstructured text data and then learns lattices that connect concepts and relationships. The proposed framework uses an off-the-shelf tool for identifying common concepts from a plain text corpus and then implements machine learning algorithms for classifying common relations that connect those concepts. Formal concept analysis is then used for generating concept lattices, which is a proven and principled method of creating formal ontologies that aid machines to learn things. A rigorous and structured experimental evaluation of the proposed method on real-world datasets has been conducted. The results show that the newly proposed framework outperforms state-of-the-art approaches in concept extraction and lattice generation.

Keywords: Formal concept analysis; concept extraction; concept lattices; relation extraction; knowledge discovery

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About the article

Received: 2017-05-16

Published Online: 2017-09-26

Published in Print: 2019-09-25


Citation Information: Journal of Intelligent Systems, Volume 28, Issue 4, Pages 669–681, ISSN (Online) 2191-026X, ISSN (Print) 0334-1860, DOI: https://doi.org/10.1515/jisys-2017-0225.

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