Automated ontology construction for unstructured text documents

  • Authors:

  • Chang-Shing Lee;Yuan-Fang Kao;Yau-Hwang Kuo;Mei-Hui Wang

  • Affiliations:

  • Department of Computer Science and Information Engineering, National University of Tainan, 33, Sec. 2, Shu-Lin St., Tainan 700, Taiwan;CREDIT Research Center, National Cheng Kung University, Tainan, Taiwan;CREDIT Research Center, National Cheng Kung University, Tainan, Taiwan;Department of Computer Science and Information Engineering, National University of Tainan, 33, Sec. 2, Shu-Lin St., Tainan 700, Taiwan

  • Venue:
  • Data & Knowledge Engineering
  • Year:
  • 2007

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Abstract

Ontology is playing an increasingly important role in knowledge management and the Semantic Web. This study presents a novel episode-based ontology construction mechanism to extract domain ontology from unstructured text documents. Additionally, fuzzy numbers for conceptual similarity computing are presented for concept clustering and taxonomic relation definitions. Moreover, concept attributes and operations can be extracted from episodes to construct a domain ontology, while non-taxonomic relations can be generated from episodes. The fuzzy inference mechanism is also applied to obtain new instances for ontology learning. Experimental results show that the proposed approach can effectively construct a Chinese domain ontology from unstructured text documents.