SciGraphChat: Retrieval-Augmented Generation over Scientific Knowledge Graphs
Abstract
Effective retrieval and information synthesis of scholarly knowledge remain challenging because of fragmented publication practices, LLM hallucinations, and the limitations of keyword-based search. We present SciGraphChat, an operational system deployed as an AI assistant within the NFDIxCS consortium, enabling discovery and exploration of scholarly knowledge represented in research knowledge graphs (KGs) through an integrated conversational and graph interface. The system builds upon a schema-agnostic, training-free retrieval approach for KG question answering, supporting source-aware and semantically informed access to structured scholarly data. By coupling KG-based Retrieval-Augmented Generation with a dialogue-oriented interface, SciGraphChat accompanies responses with retrieved RDF subgraphs and links to underlying resources. Unlike conventional chat-based assistants, the system discovers the retrieved graph evidence and enables users to interactively inspect and navigate the underlying knowledge structure through a dedicated graph panel. This paper presents the design and deployment of such a scholarly knowledge discovery system, along with practical design recommendations for integrating Semantic Web technologies and generative AI within research infrastructures. We evaluate the system with NFDIxCS stakeholders and external users via the System Usability Scale (SUS) and the User Experience Questionnaire–Short (UEQ-S), complemented by qualitative feedback. Results show…
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