Project KG-bot

Knowledge Graph chatbot – toward Large Language Model based Interaction with Metabolomics Knowledge Graphs

About the project

The KGBot project (Knowledge Graph chatBot) aims to enhance an AI-powered chemistry chatbot prototype designed to improve the accessibility and usability of metabolomics knowledge graphs (KGs). By leveraging mass spectrometry data, the chatbot employs a natural language interface to generate queries (using the SPARQL language), allowing chemists to intuitively explore complex metabolomics the knowledge graph (represented in RDF). Key objectives include broadening the chatbot’s compatibility with various large language models (LLMs) and KGs, integrating dynamic tools for data extraction and visualization, and enabling extended dialogical interactions to support iterative queries. The project also seeks to enrich user interactions by providing features such as result visualization, hypothesis generation, and analysis recommendations. Building on the interdisciplinary expertise of the project partners, this initiative fosters transdisciplinary collaboration and aims to deliver scalable solutions applicable across multiple domains. Anticipated outcomes include enhanced access to scientific data and the development of a robust open-source framework to support future academic and industrial applications. Funding will be directed towards supporting postdoctoral researchers and student contributions to the project.