Open AccessDisambiguating the Context of the Concept Terms using Concept Hierarchies
Author(s) -
Raju Dara*,
T Raghunadha Reddy
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l2505.1081219
Subject(s) - computer science , context (archaeology) , object (grammar) , ontology , domain (mathematical analysis) , latent semantic analysis , artificial intelligence , ontology learning , formal concept analysis , natural language processing , information retrieval , semantic web , data science , ontology based data integration , epistemology , mathematics , suggested upper merged ontology , algorithm , paleontology , mathematical analysis , philosophy , biology
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.