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Article

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

by
Simona Colucci
1,†,
Francesco Maria Donini
2,*,† and
Eugenio Di Sciascio
1
1
Dipartimento di Ingegneria Elettrica e dell’Informazione (DEI), Politecnico di Bari, Via Orabona 4, 70125 Bari, Italy
2
Dipartimento di Scienze Umanistiche, della Comunicazione e del Turismo (DISUCOM), Università della Tuscia, Via Santa Maria in Gradi, 4, 01100 Viterbo, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Data 2024, 9(10), 121; https://doi.org/10.3390/data9100121
Submission received: 10 August 2024 / Revised: 3 October 2024 / Accepted: 18 October 2024 / Published: 20 October 2024
(This article belongs to the Section Information Systems and Data Management)

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) Clusterization; Explanation in Artificial Intelligence (XAI); Least Common Subsumer (LCS); Resource Description Framework (RDF)

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MDPI and ACS Style

Colucci, S.; Donini, F.M.; Di Sciascio, E. Computing the Commonalities of Clusters in Resource Description Framework: Computational Aspects. Data 2024, 9, 121. https://doi.org/10.3390/data9100121

AMA Style

Colucci S, Donini FM, Di Sciascio E. Computing the Commonalities of Clusters in Resource Description Framework: Computational Aspects. Data. 2024; 9(10):121. https://doi.org/10.3390/data9100121

Chicago/Turabian Style

Colucci, Simona, Francesco Maria Donini, and Eugenio Di Sciascio. 2024. "Computing the Commonalities of Clusters in Resource Description Framework: Computational Aspects" Data 9, no. 10: 121. https://doi.org/10.3390/data9100121

APA Style

Colucci, S., Donini, F. M., & Di Sciascio, E. (2024). Computing the Commonalities of Clusters in Resource Description Framework: Computational Aspects. Data, 9(10), 121. https://doi.org/10.3390/data9100121

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