Article
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Explaining Clusters in RDF Datasets: Computational Aspects
Version 1
: Received: 16 August 2024 / Approved: 16 August 2024 / Online: 16 August 2024 (17:09:52 CEST)
How to cite: Donini, F. M.; Colucci, S.; Di Sciascio, E. Explaining Clusters in RDF Datasets: Computational Aspects. Preprints 2024, 2024081258. https://doi.org/10.20944/preprints202408.1258.v1 Donini, F. M.; Colucci, S.; Di Sciascio, E. Explaining Clusters in RDF Datasets: Computational Aspects. Preprints 2024, 2024081258. https://doi.org/10.20944/preprints202408.1258.v1
Abstract
Clustering is a very common analysis of data present in large datasets, with the aims of
understanding and summarizing data, and discovering similarities, among others. However, despite
the present success of subsymbolic methods for data clustering, the description of the obtained
clusters cannot rely on the intricacies of the subsymbolic processing. For clusters of data expressed
in the Resource Description Framework (RDF) we extend and implement an optimized previously
proposed logic-based methodology which computed 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 up to 1800 resources. Our analysis shows the viability of our methodology
and computation, and paves the way for general cluster explanations to lay users.
Keywords
Clusterization; Explanation in Artificial Intelligence (XAI); Least Common Subumer (LCS); RDF
Subject
Computer Science and Mathematics, Information Systems
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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