SemSimp: A Parametric Method for Evaluating the Semantic Similarity of Digital Resources

Antonio De Nicola; Anna Formica; Ida Mele; Francesco Taglino

doi:10.3844/jcssp.2024.841.849

Research Article · Open access · Published 27 May 2024

SemSim^p: A Parametric Method for Evaluating the Semantic Similarity of Digital Resources

Antonio De Nicola¹, Anna Formica², Ida Mele³ & Francesco Taglino³ Show affiliations

Journal of Computer Science, Volume 20, Issue 8, pp. 841–849 · doi.org/10.3844/jcssp.2024.841.849

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Abstract

SemSim^p is a parametric method for evaluating the semantic similarity of digital resources that is based on the notion of information content. It exploits a weighted reference ontology of concepts and requires resources to be semantically annotated, each by means of a set of concepts from the ontology. Specifically, the weights of the concepts can be calculated either by considering the available annotations or only the structure of the ontology. SemSim^p was evaluated against six representative semantic similarity methods proposed in the literature. Experiments were run on a large real-world dataset based on the Association for Computing Machinery (ACM) digital library, including both a statistical analysis and an expert judgment assessment. The main result shows that the SemSim^p annotation frequency configuration, when combined with the geometric average normalization factor, outperforms the other methods.

KeywordsSemantic Similarity Information Content Weighted Reference Ontology Semantic Annotation

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Nicola, A. D., Formica, A., Mele, I. & Taglino, F. (2024). SemSim^p: A Parametric Method for Evaluating the Semantic Similarity of Digital Resources. Journal of Computer Science, 20(8), 841–849. https://doi.org/10.3844/jcssp.2024.841.849

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