Description and Evaluation of Semantic Similarity Measures Approaches
This work provides a comparative analysis for researchers and practitioners in Semantic Web and NLP to choose effective semantic similarity measures, but it is incremental as it focuses on evaluation rather than introducing new methods.
The paper reviews and evaluates existing semantic similarity measures for concepts/terms using structured knowledge sources, comparing them on two standard benchmarks to aid researchers and practitioners in selecting appropriate methods.
In recent years, semantic similarity measure has a great interest in Semantic Web and Natural Language Processing (NLP). Several similarity measures have been developed, being given the existence of a structured knowledge representation offered by ontologies and corpus which enable semantic interpretation of terms. Semantic similarity measures compute the similarity between concepts/terms included in knowledge sources in order to perform estimations. This paper discusses the existing semantic similarity methods based on structure, information content and feature approaches. Additionally, we present a critical evaluation of several categories of semantic similarity approaches based on two standard benchmarks. The aim of this paper is to give an efficient evaluation of all these measures which help researcher and practitioners to select the measure that best fit for their requirements.