Aug 2026· European Conference on Knowledge Management· Vol 27, pp. 691-698· 0 citations· 25 references
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.
Recent advances in Large Language Model (LLM) “agent” systems have moved language models beyond single-turn generation toward goal-directed workflows that can plan, ask clarifying questions, and iteratively refine outputs. In parallel, retrieval-augmented generation (RAG) has become a practical way to ground these agents in enterprise knowledge—enabling models to leverage internal documentation and policies without expensive and recurring retraining. However, RAG is only as strong as the underlying corpus: when key information is missing, outdated, or fragmented, retrieval cannot fill the gaps. In many organizations, assembling high-quality, up-to-date documents remains a persistent bottleneck, limiting both the reliability of downstream applications and the speed at which knowledge can be operationalized. To address this, we introduce the Assistant-Scribe-Knowledge Checker (ASK) framework for generating structured specification documents through guided interviews. ASK decomposes the end-to-end workflow into three specialized LLM agents: an Assistant that asks questions to the user and steers the interview through adaptive follow-ups; a Scribe that continuously summarizes what has been said and records it into a schema-constrained specification document with explicit content requirements; and a Knowledge Checker that evaluates the evolving document against those specifications, detects gaps or weakly supported entries, and advises the Assistant on the next best questions to ask to reach the desired completeness and quality. This separation supports seamless document creation while improving quality control over content, structure, and coverage. By distributing responsibilities across three lightweight, role-specific agents, ASK reduces reliance on a single large model and enables modular scaling across teams and domains. We evaluated the ASK framework in a consulting-firm setting where consultants are required to produce project “return-of-experience” documents (successes, failures, challenges, and solutions) to capture reusable knowledge. Compared to documents authored manually, ASK-guided interviews consistently produced more complete, better-structured, and higher-quality specifications with less variability across authors. Beyond measurable quality gains, consultants also reported a clear preference for the interview-based workflow, citing lower effort and a more natural way to articulate tacit project knowledge.
Sylvain Roudiere, Bianca Lento· European Conference on Knowl...· 0 citations
Experiments on three different domain tasks show that FKGLM can effectively integrate LLMs and large-scale knowledge graphs, leading to a significant enhancement in the reasoning capabilities of LLMs.
Yulin Zhou, Yongbin Qin, Chuan Lin· Journal of King Saud Univers...· 0 citations
As experienced workers retire across industrialized economies, organizations risk losing procedural expertise that often remains tacit, undocumented or scattered across unstructured documents. Translating this knowledge into structured, machine-readable representations is difficult to scale, labor-intensive, and prone to inconsistency when done manually. This paper addresses the automated construction of knowledge graphs from natural language procedural descriptions, developing a generic approach for transforming unstructured expert knowledge into structured knowledge graphs that support downstream retrieval and question-answering applications. Three text-to-knowledge-graph approaches were designed, implemented, and systematically evaluated. The first employed a large general-purpose language model (Qwen3-32B) with a single-stage zero-shot extraction prompt, the second applied the same strategy using a smaller base model (Llama2-13B), and the third combined supervised fine-tuning of the smaller model on synthetic extraction data with a decomposed extraction architecture targeting one to two ontology elements per phase. All approaches were evaluated across six procedural descriptions spanning multiple technical domains, with chunk size, model temperature, and ontology detail as configuration parameters. Results were assessed for intrinsic quality and extrinsic fitness for use, measured by question-answering accuracy in a Graph-RAG application. The results demonstrate that extraction strategy is a more decisive factor than model capacity. The fine-tuned model achieved a Question Answering (QA) pass rate of 55.3%, compared to 46.8% for the large model and 33% for the small base model, outperforming the general-purpose model on both intrinsic quality metrics and downstream performance. Average node degree, duplication rate, and ontology simplicity emerge as the strongest predictors of retrieval performance. Shorter ontologies consistently outperformed richer ones, suggesting that ontology design should be driven by the intended downstream application rather than semantic completeness. Over-extraction of procedural steps did not degrade performance but instead improved retrieval robustness by providing additional entry points for graph traversal. The findings offer practical guidance for designing scalable, locally deployable knowledge graph construction pipelines for procedural texts under computational and confidentiality constraints.
Erik Sörqvist, Kenneth Obinna, Clara Bersch et al.· European Conference on Knowl...· 0 citations
The findings suggest that AI-based structured extraction may redefine how organisations formalise expertise, shifting from document-centric storage toward schema-driven knowledge architectures.
Dilyan Georgiev, E. Gourova· European Conference on Knowl...· 0 citations
Kevo is a co-evolving framework that jointly learns to construct a structured knowledge base and reason over it for knowledge-intensive question answering, which leads to larger, better-connected knowledge structures with higher answer reachability, while also improving compositional factual reasoning and controllability compared to standard retrieval baselines.
Ryan Thomas Noonan, Lin-Xi Zhao, Meng-Han Xu et al.· 0 citations
Knowledge graphs used by agentic systems are often treated as flat stores of extracted triples, with little record of who owns a fact, why it was admitted, or how it should be used downstream. We argue that reliable agentic knowledge systems require governance as an essential component of graph construction to bridge this gap. We propose MAGG, a principled multi-agent framework for constructing Governed Knowledge Graphs that introduces explicit governance decisions for reliable and trustworthy knowledge sharing. A domain classifier first induces entity and relation types directly from document content, enabling operation in open-world settings without fixed schemas. Candidate triples are assigned to domain owners, reviewed against supporting evidence, admitted through governance decisions, and stored with audit metadata. The same ownership structure is reused during question answering, where queries are routed to domain-specific graph experts rather than answered through undifferentiated retrieval. Our evaluation demonstrates MAGG's effectiveness: On SciERC, MAGG improves strict triple F1 by 47% and mapped triple F1 by 51% over flat insertion. A blinded review of 120 triples finds governed-only triples more often source-supported than flat-only ones, and revised triples supported in 100% of cases. Finally, on MuSiQue, MAGG outperforms Microsoft GraphRAG by 9.0 exact-match points and 11.2 token-F1 points.
Pranav Bykampadi, Neel Mokaria, Vishesh Narayan et al.· 0 citations
