Computing the Commonalities of Clusters in Resource Description Framework: Computational Aspects

Simona Colucci, Francesco Maria Donini () and Eugenio Di Sciascio
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Simona Colucci: Dipartimento di Ingegneria Elettrica e dell’Informazione (DEI), Politecnico di Bari, Via Orabona 4, 70125 Bari, Italy
Francesco Maria Donini: Dipartimento di Scienze Umanistiche, della Comunicazione e del Turismo (DISUCOM), Università della Tuscia, Via Santa Maria in Gradi, 4, 01100 Viterbo, Italy
Eugenio Di Sciascio: Dipartimento di Ingegneria Elettrica e dell’Informazione (DEI), Politecnico di Bari, Via Orabona 4, 70125 Bari, Italy

Data, 2024, vol. 9, issue 10, 1-18

Abstract: Clustering is a very common means of analysis of the data present in large datasets, with the aims of understanding and summarizing the data and discovering similarities, among other goals. However, despite the present success of the use of subsymbolic methods for data clustering, a description of the obtained clusters cannot rely on the intricacies of the subsymbolic processing. For clusters of data expressed in a Resource Description Framework ( RDF ), we extend and implement an optimized, previously proposed, logic-based methodology that computes an RDF structure—called a Common Subsumer—describing the commonalities among all resources. We tested our implementation with two open, and very different, RDF datasets: one devoted to public procurement, and the other devoted to drugs in pharmacology. For both datasets, we were able to provide reasonably concise and readable descriptions of clusters with up to 1800 resources. Our analysis shows the viability of our methodology and computation, and paves the way for general cluster explanations to be provided to lay users.

Keywords: Clusterization; Explanation in Artificial Intelligence (XAI); Least Common Subsumer (LCS); Resource Description Framework (RDF) (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
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