Disambiguating the context of the concept terms using concept hierarchies

0Citations
Citations of this article
2Readers
Mendeley users who have this article in their library.
Get full text

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.

Cite

CITATION STYLE

APA

Dara, R., & Raghunadha Reddy, T. (2019). Disambiguating the context of the concept terms using concept hierarchies. International Journal of Innovative Technology and Exploring Engineering, 8(12), 90–95. https://doi.org/10.35940/ijitee.L2505.1081219

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free