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Automatic Ontology Learning from Multiple Knowledge Sources of Text

Automatic Ontology Learning from Multiple Knowledge Sources of Text

B Sathiya, T.V. Geetha
Copyright: © 2018 |Volume: 14 |Issue: 2 |Pages: 21
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781522542797|DOI: 10.4018/IJIIT.2018040101
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MLA

Sathiya, B, and T.V. Geetha. "Automatic Ontology Learning from Multiple Knowledge Sources of Text." IJIIT vol.14, no.2 2018: pp.1-21. http://doi.org/10.4018/IJIIT.2018040101

APA

Sathiya, B. & Geetha, T. (2018). Automatic Ontology Learning from Multiple Knowledge Sources of Text. International Journal of Intelligent Information Technologies (IJIIT), 14(2), 1-21. http://doi.org/10.4018/IJIIT.2018040101

Chicago

Sathiya, B, and T.V. Geetha. "Automatic Ontology Learning from Multiple Knowledge Sources of Text," International Journal of Intelligent Information Technologies (IJIIT) 14, no.2: 1-21. http://doi.org/10.4018/IJIIT.2018040101

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Abstract

The prime textual sources used for ontology learning are a domain corpus and dynamic large text from web pages. The first source is limited and possibly outdated, while the second is uncertain. To overcome these shortcomings, a novel ontology learning methodology is proposed to utilize the different sources of text such as a corpus, web pages and the massive probabilistic knowledge base, Probase, for an effective automated construction of ontology. Specifically, to discover taxonomical relations among the concept of the ontology, a new web page based two-level semantic query formation methodology using the lexical syntactic patterns (LSP) and a novel scoring measure: Fitness built on Probase are proposed. Also, a syntactic and statistical measure called COS (Co-occurrence Strength) scoring, and Domain and Range-NTRD (Non-Taxonomical Relation Discovery) algorithms are proposed to accurately identify non-taxonomical relations(NTR) among concepts, using evidence from the corpus and web pages.

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