Hybrid Method for Semantic Similarity Computation Using Weighted Components in Ontology

Abstract

In this paper, the researchers propose an approach to measure the semantic similarity between two concepts in an ontology like WordNet and DBpedia. Some earlier semantic similarity approaches proposed concentrated on the ontology structure between concepts and some concentrated only on the information content of concepts. This paper proposes a semantic similarity approach with path length, information content, and semantic depth (i.e., PLICD) to combine both path length as well as information content-based approaches. This proposed approach uses weighted shortest path length and information content calculated using semantic depth and hyponyms of the concepts to measure semantic similarity between two concepts. Through experimentations performed on WordNet and DBpedia, the researchers note that the PLICD semantic similarity approach has delivered a statistically meaningful enhancement as compared to the other semantic similarity approaches concerning accuracy and F score.