Disambiguating the Context of the Concept Terms using Concept Hierarchies
Raju Dara1, T. Raghunadha Reddy2

1Dr. Raju Dara*, Department of CSE, Vignana Bharathi Institute of Technology, Hyderabad, Telangana, India.
2Dr. T Raghunadha Reddy, Dept of Information Technology, Vardhaman College of Engineering, Shamshabad, Telangana, India. 

Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 90-95 | Volume-8 Issue-12, October 2019. | Retrieval Number: L25051081219/2019©BEIESP | DOI: 10.35940/ijitee.L2505.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Latent Semantic Analysis (LSA) makes the machine clearly conceptualize the terms of the document by learning the context in which these terms are written. However, LSA suffers from the limitation of input data matrix size in terms of number of terms and number of documents of the considered dataset. When the size of the dataset is huge, LSA becomes inefficient towards learning the correct context and thereby is unable to produce the intended concepts by the machine. To overcome this problem, Context Disambiguation (ConDis) ontology is engineered for a domain which has the capability of evolving itself based on automatic learning of concepts and relations from the ever scaling documents over the web. The concept hierarchies from general to specific concepts combined with corresponding object relations specify the particular context for a term. These object relations based concept hierarchies clearly help disambiguate the context of the concept terms in an effective manner.
Keywords: LSA, Term, Context, ConDis Ontology, Concept, Ontology Evolution, Concept Hierarchies.
Scope of the Article: Advance Concept