K-GRASP: Tacit Knowledge Externalisation with Language Models and Knowledge Graphs
TL;DR
This work proposes an architecture called K-GRASP (Knowledge Graph-based Retrieval-Augmented Structured Prompting), which combines the representational power of knowledge graphs (KG) with the probabilistic reasoning capabilities of Large Language Models (LLMs) to address the externalisation of tacit knowledge.
Abstract
The externalisation of tacit knowledge, defined as practical, experiential knowledge that is difficult to formalise, represents one of the greatest challenges faced by institutions that rely on the expertise of their members. Due to its subjective and contextual nature, this type of knowledge resists capture through traditional methods such as interviews and observations, which are costly, difficult to standardise, and limited in reusability. This work proposes an architecture called K-GRASP (Knowledge Graph-based Retrieval-Augmented Structured Prompting), which combines the representational power of knowledge graphs (KG) with the probabilistic reasoning capabilities of Large Language Models (LLMs) to address this challenge. The proposed solution is divided into two phases: (i) a capture phase, in which an interviewer LLM conducts structured sessions with experts and converts their responses into RDF triples stored in a semantic graph, and (ii) a retrieval phase, in which a consultative LLM uses Retrieval-Augmented Generation (RAG) to translate natural language questions into SPARQL queries (SPARQL Protocol and RDF Query Language), retrieve relevant subgraphs, and generate contextualized responses. The architecture enables the systematic, reusable, and accessible codification of tacit knowledge, allowing for its large-scale preservation and dissemination. By integrating LLMs with formal representation structures, K-GRASP offers a robust, scalable, and interpretable solution to a historically complex problem in knowledge management. In the capture phase, prompts can be shaped to elicit concrete cases, boundary conditions, and decision rationales. At the same time, responses are mapped to an agreed-upon vocabulary to reduce drift across sessions. The graph can also retain provenance and scope cues (e.g., source expert, date, and stated assumptions), which may support later review and incremental refinement. In the retrieval phase, returning both the synthesised answer and the underlying triples can make the consultation more transparent and highlight gaps or ambiguities, as well as operational concerns such as access control, privacy, and versioning.