Semantic Web-based Interoperability for Intelligent Agents with PSyKE

Modern distributed systems require communicating agents to agree on a shared, formal semantics for the data they exchange and operate upon. The Semantic Web offers tools to encode semantics in the form of ontologies, where data is represented in the form knowledge graphs (KG). Applying such tools to intelligent agents equipped with machine learning (ML) capabilities is of particular interest, as it may enable a higher degree of interoperability among heterogeneous agents. Indeed, inputs and outputs of ML models can be formalised through ontologies, while the data they operate upon can be represented as KG.
In this paper we explore the combination of Semantic Web tools with knowledge extraction-that is, a research line aimed at extracting intelligible rules mimicking the behaviour of ML predictors, with the purpose of explaining their behaviour. Along this line, we study whether and to what extent ontologies and KG can be exploited as both the source and the outcome of a rule extraction procedure. In other words, we investigate the extraction of semantic rules out of sub-symbolic predictors trained upon data as KG-possibly adhering to some ontology. In doing so, we extend our PSyKE framework for rule extraction with Semantic Web support. In practice, we make PSyKE able to (i) train ML predictors out of OWL ontologies and RDF knowledge graphs, and (ii) extract semantic knowledge out of them, in the form of SWRL rules. A discussion among the major benefits and issues of our approach is provided, along with a description of the overall workflow.

parole chiave   Explainable AI; Knowledge extraction;Semantic Web;Intelligent agents;PSyKE

presentazione di riferimento

 Semantic Web-based Interoperability for Intelligent Agents with PSyKE (EXTRAAMAS 2022@AAMAS 2022, 09/05/2022) — Federico Sabbatini (Federico Sabbatini, Giovanni Ciatto, Andrea Omicini)

evento origine

 EXTRAAMAS 2022@AAMAS 2022

rivista o collana

 Lecture Notes in Computer Science (LNCS)

progetto finanziatore

 EXPECTATION — Personalized Explainable Artificial Intelligence for decentralized agents with heterogeneous knowledge (01/04/2021–31/03/2024)

pubblicazione di riferimento per presentazione

 Semantic Web-based Interoperability for Intelligent Agents with PSyKE (EXTRAAMAS 2022@AAMAS 2022, 09/05/2022) — Federico Sabbatini (Federico Sabbatini, Giovanni Ciatto, Andrea Omicini)