Helping SSH Researchers to Explore and Exploit Knowledge Graphs through Artificial Intelligence: the GRAPHIA Project

Abstract Many research institutions worldwide have invested over the years in the creation of knowledge graphs (KGs) with deep semantic descriptions of their data. Despite the semantic richness of this information, its accessibility and usefulness for researchers remain limited due to the complexity of their ontologies and the specialized query languages required to retrieve information (e.g., SPARQL) [1]. Helping researchers in social sciences and humanities (SSH) to more easily access information contained in KGs is one of the aims of the GRAPHIA project, a recent EU-funded project, which aims not only to create a comprehensive KG for SSH, but also to exploit large language models (LLMs) as a means to access and analyse the information contained in them. In GRAPHIA, we will expand the current implementations of AI done in the context of GoTriple, a massive repository holding more than 19 million SSH publications. The first AI implementation is the GoTriple ChatBot [2], a RAG system (Retrieval Augmented Generation) fed with the full text of selected documents to answer user prompts. As a complete RAG indexation of GoTriple turned out to be too costly, a new implementation allows users to create notebooks starting from GoTriple documents, and then pass them to the RAG system for AI-assisted analysis. Finally, GRAPHIA partners’ experiments with AI include the usage of LLMs to convert natural language questions into structured queries against KGs, and the usage of the Model Context Protocol (MCP) to empower AI-assisted interactions with massive KGs. References [1] M. A. Pellegrino, V. Scarano, and C. Spagnuolo, ‘Move cultural heritage knowledge graphs in everyone’s pocket’, Semantic Web, vol. 14, no. 2, pp. 323–359, 2023, doi: 10.3233/SW-223117. [2] A. Bertozzi, M. L. Abbamonte, A. Abaza, L. De Santis, ‘From Words to Search: GoTriple's AI ChatBot for Efficient Research Engagement’. Zenodo, 2024. doi: 10.5281/zenodo.10977163. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the Agency. Neither the European Union nor the granting authority can be held responsible for them.