Towards a FAIR Knowledge Graph-Based Conversational Agent for Exploring Agroforestry Research

This paper presents a framework for improving access to agroforestry research through a FAIR knowledge graph integrated with a conversational agent. The proposed system aggregates metadata and research outputs from institutional repositories of CIFOR and World Agroforestry (ICRAF), transforming scientific information into semantically enriched and machine-readable knowledge structures. The framework combines knowledge graph technologies, AGROVOC-based semantic annotation, large language models, and graph retrieval-augmented generation to support natural language querying and query-focused summarization. By linking diverse research resources and enabling intuitive access through a conversational interface, the approach aims to enhance the discoverability, accessibility, and reuse of agroforestry knowledge, particularly for researchers and stakeholders in Africa, Asia, and Latin America. The system contributes to advancing FAIR data principles and supports more effective exploration of scientific evidence in agriculture and agroforestry research.

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