Ontology matching through OntoGPT for O3PO, DABGEO, and OEOontologies

Whenbuildingacausalgraphfromtextualsources, such asmedia reports, a key task is to provide an accurate semantic understanding of the graph nodes and to link them to existing ontologies with at least two purposes: (i) expand the knowledge with already created ontologies and (ii) guarantee accurate and different levels of abstraction of the extracted concepts. This article describes how weused OntoGPT, a tool for matching raw text to ontology concepts initially designed for the medical domain, to match concepts from media events to relevant ontologies. In particular, we developed a set of scripts to generate custom YAML templates and Python code to facilitate the extraction of relevant concepts from ontologies and link them to causality graphs. The article discusses the operation of OntoGPT and the template generation process and addresses the tool’s limitations encountered during the abovementioned process. Given the interest in developing a foresight tool that addresses strategic foresight needs in the green energy domain, three ontologies related to energy and the oil industry were considered.