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ISSN: 1549-3636 (Print)
ISSN: 1552-6607 (Online)
Research Article Open Access

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

Antonio De Nicola1, Anna Formica2, Ida Mele3 and Francesco Taglino3
  • 1 Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Casaccia Research Centre, Via Anguillarese 301, Rome, Italy
  • 2 Institute of Systems Analysis and Informatics (IASI) “Antonio Ruberti”, National Research Council, Via dei Taurini 19, Rome, Italy
  • 3 Institute of Systems Analysis and Informatics (IASI) “Antonio Ruberti”, National Research Council, Via dei Taurini 19, Rome, Italy

Abstract

SemSimp 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. SemSimp 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 SemSimp annotation frequency configuration, when combined with the geometric average normalization factor, outperforms the other methods.

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Journal of Computer Science
Volume 20 No. 8, 2024, 841-849

DOI: https://doi.org/10.3844/jcssp.2024.841.849

Submitted On: 12 January 2024 Published On: 27 May 2024

How to Cite: Nicola, A. D., Formica, A., Mele, I. & Taglino, F. (2024). SemSimp: 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

Open AccessCopyright: © 2024 Antonio De Nicola, Anna Formica, Ida Mele and Francesco Taglino. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Keywords

  • Semantic Similarity
  • Information Content
  • Weighted Reference Ontology
  • Semantic Annotation