Effective and Reliable Domain-Specific Knowledge Question Answering

Large language models (LLMs) serve as powerful knowledge bases, capable of embedding knowledge from text into model parameters. However, the widespread issue of hallucination and opaque black-box processes restrict their broader application in professional sectors that place high demands on safety and predictability. Conversely, knowledge graphs provide inherent superiority in the authenticity and interpretability in question answering sessions. However, building a knowledge graph in specialized fields and conduct question answering over the knowledge graph are both nontrivial problems. This paper introduces an efficient knowledge graph question answering (KGQA) approach tailored to customized domain-specific knowledge graphs. Our “extract-then-bind” KGQA method leverages the in-context learning capabilities of the LLM to extract mention and relation proposals from the query, which are then matched with nodes and edges in the knowledge graph. Experimental results demonstrate the effectiveness of our approach in a question answering task using automotive user manuals. Notably, the knowledge graph was created through an automated process. By combining this innovative KGQA technique with its corresponding knowledge graph construction method, this paper proposes an effective and reliable system for addressing domain-specific knowledge question answering. This integrated solution guarantees authenticity and traceability in question answering while significantly reducing the need for manual labour.