# Description and Evaluation of Semantic Similarity Measures Approaches

arXiv: https://arxiv.org/abs/1310.8059 · HTML: https://mlsanity.com/paper/1310.8059/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2013.

## Built on by (most cited first, 4 of 4)

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- [Understanding Stigmatizing Language Lexicons: A Comparative Analysis in Clinical Contexts](https://mlsanity.com/paper/2509.07462/) (2025)

## Abstract (arXiv)

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.