Semantic vs. LLM-based approach: A case study of KOnPoTe vs. Claude for ontology population from French advertisements

Abstract

Automatic ontology population is the process of identifying, extracting, and integrating relevant information from diverse sources to instantiate the classes and properties specified in an ontology, thereby creating a Knowledge Graph (KG) for a particular domain. In this study, we evaluate two approaches for ontology population from text: KOnPoTe, a semantic technique that employs textual and domain knowledge analysis, and a generative AI method leveraging Claude, a Large Language Model (LLM). We conduct comparative experiments on three French advertisement domains: real estate, boats, and restaurants to assess the performance of these techniques. Our analysis highlights the respective strengths and limitations of the semantic approach and the LLM-based one in the context of the ontology population process.