Hybridization of Max.C-Value and Permutation for Refined Concept Extraction

Authors

  • R. Manimala, G. Muthu Lakshmi

Keywords:

Ontology, ontology learning, Ontology Acquisition

Abstract

Ontology is the representation of knowledge with a set of concepts and the relationships between those concepts, for a domain. It describes about the domain.It is mainly used to organize the web data for maximizing the semantic access and to extract the knowledge from different format of data. Intelligence is necessary for the creation and processing of those semantic metadata. Manual ontology construction is labour-intensive, error-prone process, rigid, expensive, time consuming and complex task. Ontology Acquisition or ontology learning includes the automatic extraction of domain’s terms, concepts and the relationships between those concepts from the corpus of text, and encoding them with an ontology language for easy information retrieval. In the automatic ontology learning, Concept extraction is the main step to improve the accuracy .This paper describes the concept extraction using Max.CVPC method. Our proposed work is the Hybridization of Maximum C-Value of Permutation of UMLS Concepts which is further filtered with the threshold to extract the dominant concepts or refined concepts. It improves the precision as well as extracts the dominant concepts to build the appropriate ontology. Here we used Permutation technique instead of n-gram to increase the sub terms and its weightage.The method is analyzed with Genia Corpus which contains 2000 MEDLINE abstracts. The results are compared with the n-gram technique which is also filtered with maximum of C-Value. We evaluate our work with the metrics precision, Recall and F-Measure.

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Published

30.06.2022

How to Cite

R. Manimala. (2022). Hybridization of Max.C-Value and Permutation for Refined Concept Extraction. International Journal of Intelligent Systems and Applications in Engineering, 10(2s), 343–352. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/8007

Issue

Vol. 10 No. 2s (2022)

Section

Research Article

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