This work proposes a set of metrics designed to evaluate the ability of LRMs to generate formal knowledge in order to solve domain-specific problems, and curates – based on a collection of LRM-generated Prolog programs – common issues in generated KBs, and derive metrics that can be used to assess such issues.
Experiments show that precision improves by filtering invalid candidates, while recall is preserved due to retaining candidates whose constraints are not explicitly violated, and a three-valued constraint semantics that avoids incorrect rejections under open-world assumptions.
Emanuel Kitzelmann· Deutsche Jahrestagung für Kü...· 0 citations
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.
Rafael Luna, Gabriel S. Luna, C. E. Barbosa et al.· European Conference on Knowl...· 0 citations
The Assistant-Scribe-Knowledge Checker (ASK) framework for generating structured specification documents through guided interviews is evaluated in a consulting-firm setting where consultants are required to produce project “return-of-experience” documents to capture reusable knowledge.
Sylvain Roudiere, Bianca Lento· European Conference on Knowl...· 0 citations
This research proposes a large language model–based approach combined with structured algorithms that process domain-specific competency questions and unstructured construction documents to build ontologies and extract structured knowledge graphs to support the automated generation of quality inspection checklists and...
Yi-Heng Wang, Juan Wang, W. Fang· Journal of construction engi...· 0 citations
This paper proposes a deterministic, explainable framework for validating uncertain entities during Knowledge Graph (KG) expansion. The contribution is conceptual and formal: we present the framework and demonstrate its internal mathematical coherence, while empirical evaluation is explicitly deferred to future work. S...
Simon Von Oppenkowski, Benedikt Lerch, Klemens Schnattinger· European Conference on Knowl...· 0 citations
This work forms the task of Information-Needs-Guided VKG Enrichment (IN-VKGE), and proposes an iterative framework that leverages large language models to assess whether information needs can be supported using SPARQL execution feedback and generate ontology and mapping enrichment proposals.
Lin Ren, Guohui Xiao, Guilin Qi et al.· Proceedings of the Thirty-Fi...· 0 citations
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