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Lexical Syntactic Patterns and Novel Statistical Measures based Bootstrapping Approach for Evolution of Biomedical Ontologies

by B. Sathiya, T. V. Geetha
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 39
Year of Publication: 2020
Authors: B. Sathiya, T. V. Geetha
10.5120/ijca2020919873

B. Sathiya, T. V. Geetha . Lexical Syntactic Patterns and Novel Statistical Measures based Bootstrapping Approach for Evolution of Biomedical Ontologies. International Journal of Computer Applications. 177, 39 ( Feb 2020), 21-27. DOI=10.5120/ijca2020919873

@article{ 10.5120/ijca2020919873,
author = { B. Sathiya, T. V. Geetha },
title = { Lexical Syntactic Patterns and Novel Statistical Measures based Bootstrapping Approach for Evolution of Biomedical Ontologies },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2020 },
volume = { 177 },
number = { 39 },
month = { Feb },
year = { 2020 },
issn = { 0975-8887 },
pages = { 21-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number39/31164-2020919873/ },
doi = { 10.5120/ijca2020919873 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:48:12.778810+05:30
%A B. Sathiya
%A T. V. Geetha
%T Lexical Syntactic Patterns and Novel Statistical Measures based Bootstrapping Approach for Evolution of Biomedical Ontologies
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 39
%P 21-27
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Knowledge extraction and information processing from the proliferating biomedical data is a primary challenge to the researchers in this field. This is tackled by a semantic knowledge representation model with controlled vocabulary termed as ontology. However, the exponential growth of biomedical data makes the ontology outdated soon and hence its evolution process becomes an inevitable one. Even though numerous ontology evolution systems attempted to evolve the ontology automatically in numerous ways, identifying concepts of ontology that need to be evolved and discovery of new components of the concepts such as its related new concepts and relations is not handled automatically. Therefore, the aim of this work is to automatically identify the concepts which need to be evolved and discover the new components for those concepts using the web pages and MEDLINE database. Particularly, a new concept selection measure: CE (Concept to be Evolved) is designed to select the concepts with high possibility to be evolved based on the number of neighbour and depth of it. Next, a lexical syntactic pattern based bootstrapping approach with new statistical scoring measures such as HH-CS (Hyponym Hypernym-Concept Scoring), DR-CS CS (Domain Range-Concept Scoring) and RS (Relation Scoring) is proposed to discover new candidate components from web pages using the set of patterns and precisely select the correct candidate components from the MEDLINE database using the scoring measures. The experimental results on the biomedical ontologies in terms of precision, recall, F-measure and ontology quality metrics prove the effectiveness of the proposed CE measure and bootstrapping approach with new statistical measures in precisely identifying concepts to be evolved and discovering new components.

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Index Terms

Computer Science
Information Sciences

Keywords

Ontology evolution enrichment bootstrapping biomedical ontologies.

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