@article {10.3844/jcssp.2024.841.849,
article_type = {journal},
title = {SemSimp: A Parametric Method for Evaluating the Semantic Similarity of Digital Resources},
author = {Nicola, Antonio De and Formica, Anna and Mele, Ida and Taglino, Francesco},
volume = {20},
number = {8},
year = {2024},
month = {May},
pages = {841-849},
doi = {10.3844/jcssp.2024.841.849},
url = {https://thescipub.com/abstract/jcssp.2024.841.849},
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.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}