Expanding the User’s Query to Enhance Semantic Information Retrieval Using the Reasoning Mechanism Based on Homomorphism Between Semantic Annotations

Semantic search encompasses advanced technological approaches to information discovery and retrieval, employing semantic techniques to extract information from intricately structured data sources.An effective search engine must have the ability of accessing and retrieving information of interest by employing reasoning with conceptual models.However, the structural and semantic information intrinsic in conceptual models is not readily amenable to reasoning and AI-enhanced semantic processing.Therefore, the chosen model should enable understanding the meaning of concepts and the relationships between them.Il should also carefully consider the context of the search, ultimately enhancing the accuracy of the returned results.Among the models that fulfill these objectives, conceptual graphs stand out as particularly interesting.They are built upon a robust theoretical framework that spans multiple domains, including philosophical, psychological, linguistic, and artificial intelligence disciplines.In this paper, we describe a method for semantic search driven by conceptual graph-based representation, and a powerful matching reasoning supported by a projection operation between the semantic annotations associated with the document and the submitted query.