IWD towards Semantic similarity measure in ontology. Issue 7 (2nd October 2020)

• Record Type: Journal Article
• Authors: Rathee, Preeti; Malik, Sanjay Kumar
• Is Part Of: Journal of information & optimization sciences. Volume 41:Issue 7(2020)
• Page Start: 1561
• Page End: 1577
• Publication Date: 2020-10-02
• Subjects: 68P20; Ontology -- Semantic similarity -- IWD -- Semantic distance -- Similarity measure; Electronic data processing -- Periodicals; Information science -- Periodicals; Mathematical optimization -- Periodicals; 519.6
• DOI: 10.1080/02522667.2020.1802129
• Languages: English
• ISSNs: 0252-2667

Abstract

Abstract: Ontology plays a key role in semantic web to represent various concepts and their relations to make knowledge machine understandable. In order to provide a knowledge sharing environment, concept mapping in ontologies is required. In this regard, semantic similarity in the field of information retrieval is quite significant which refers to relatedness in ontology concepts. This semantic similarity may be computed in several ways. In this paper, a new method, based on optimization algorithm, IWD (Intelligent Water Drop), is proposed to computer similarity in ontology concepts. It is used to measure weighted path in concepts of ontology which is further used to find the semantic distance. Semantic similarity is computed on the basis of this semantic distance. Further, with the aid of a case study, a comparative analysis has been presented for the proposed method and other existing similarity methods for semantic similarity.