Computing the Commonalities of Clusters in Resource Description Framework: Computational Aspects
Author(s)
Date Issued
2024
Type
article
Volume
9
Issue
121
Journal
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.