Semantic similarity measurement: an intrinsic information content model. (26th January 2021)

• Record Type: Journal Article
• Authors: Adhikari, Abhijit; Dutta, Biswanath; Dutta, Animesh; Mondal, Deepjyoti
• Is Part Of: International journal of metadata, semantics and ontologies. Volume 14:Number 3(2020)
• Page Start: 218
• Page End: 233
• Publication Date: 2021-01-26
• Subjects: semantic similarity -- knowledge-based systems -- ontology -- intrinsic information content -- natural language processing; Metadata -- Periodicals; Semantic Web -- Periodicals; Ontologies (Information retrieval) -- Periodicals; Data structures (Computer science) -- Periodicals; Information theory -- Periodicals; 005.74
• Languages: English
• ISSNs: 1744-2621

Abstract

Ontology dependent Semantic Similarity (SS) measurement has emerged as a new research paradigm in finding the semantic strength between any two entities. In this regard, as observed, the information theoretic intrinsic approach yields better accuracy in correlation with human cognition. The precision of such a technique highly depends on how accurately we calculate Information Content (IC) of concepts and its compatibility with a SS model. In this work, we develop an intrinsic IC model to facilitate better SS measurement. The proposed model has been evaluated using three vocabularies, namely SNOMED CT, MeSH and WordNet against a set of benchmark data sets. We compare the results with the state-of-the-art IC models. The results show that the proposed intrinsic IC model yields a high correlation with human assessment. The article also evaluates the compatibility of the proposed IC model and the other existing IC models in combination with a set of state-of-the-art SS models.