Evaluating the similarity of RDF resources is nowadays a thoroughly investigated research problem, with reference to a variety of contexts. In fact, several tools are available for the comparison of pairs and/or groups of resources in a knowledge graph, mostly based on machine learning techniques. Unfortunately such tools, though extensively tested and fully scalable, return non-explainable (often numerical) similarity results also when comparing RDF resources, treating them according to their vector embeddings. and making no use of the semantic information carried by RDF triples. In this work, we propose a tool able to compute the commonalities of compared resource and explain them through a text in English, produced by a Natural Language Generation approach. The proposed approach is logic-based and is grounded on the computation of the Least Common Subsumer (re)defined in RDF. The feasibility of the tool is demonstrated with reference to the similarity of Twitter accounts.

A Human-readable Explanation for the Similarity of RDF Resources

Colucci S.;Donini F. M.;Di Sciascio E.
2022-01-01

Abstract

Evaluating the similarity of RDF resources is nowadays a thoroughly investigated research problem, with reference to a variety of contexts. In fact, several tools are available for the comparison of pairs and/or groups of resources in a knowledge graph, mostly based on machine learning techniques. Unfortunately such tools, though extensively tested and fully scalable, return non-explainable (often numerical) similarity results also when comparing RDF resources, treating them according to their vector embeddings. and making no use of the semantic information carried by RDF triples. In this work, we propose a tool able to compute the commonalities of compared resource and explain them through a text in English, produced by a Natural Language Generation approach. The proposed approach is logic-based and is grounded on the computation of the Least Common Subsumer (re)defined in RDF. The feasibility of the tool is demonstrated with reference to the similarity of Twitter accounts.
2022
3rd Italian Workshop on Explainable Artificial Intelligence, XAI.it 2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/248880
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