SOA architecture was created to systematise issues relating to the interoperability of M2M services, focusing on issues such as security and privacy. With the advent of generative AI, there is a different way to perform the operations for which Semantic Web Services were created, in a much simpler way, but losing control over the level of security and privacy. In this paper, we seek to propose a combined vision of the two approaches, identifying how generative AI can be used to solve specific, rather than general, problems. To this end, we attempt to analyse how an LLM could be used by a software agent to align different types of XML parameter data in WSDL descriptions.
OWL-S Grounding Parameters Matching by Means of LLM: Preliminary Investigation
Redavid D.;Bernasconi E.;Ferilli S.
2025-01-01
Abstract
SOA architecture was created to systematise issues relating to the interoperability of M2M services, focusing on issues such as security and privacy. With the advent of generative AI, there is a different way to perform the operations for which Semantic Web Services were created, in a much simpler way, but losing control over the level of security and privacy. In this paper, we seek to propose a combined vision of the two approaches, identifying how generative AI can be used to solve specific, rather than general, problems. To this end, we attempt to analyse how an LLM could be used by a software agent to align different types of XML parameter data in WSDL descriptions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
