[2603.29878] Performance Evaluation of LLMs in Automated RDF Knowledge Graph Generation

Computer Science > Information Retrieval arXiv:2603.29878 (cs) [Submitted on 6 Feb 2026] Title:Performance Evaluation of LLMs in Automated RDF Knowledge Graph Generation Authors:Ioana Ramona Martin, Tudor Cioara, Ionut Anghel, Gabriel Arcas View a PDF of the paper titled Performance Evaluation of LLMs in Automated RDF Knowledge Graph Generation, by Ioana Ramona Martin and 2 other authors View PDF Abstract:Cloud systems generate large, heterogeneous log data containing critical infrastructure, application, and security information. Transforming these logs into RDF triples enables their integration into knowledge graphs, improving interpretability, root-cause analysis, and cross-service reasoning beyond what raw logs allow. Large Language Models (LLMs) offer a promising approach to automate RDF knowledge graph generation; however, their effectiveness on complex cloud logs remains largely unexplored. In this paper, we evaluate multiple LLM architectures and prompting strategies for automated RDF extraction using a controlled framework with two pipelines for systematically processing semi-structured log data. The extraction pipeline integrates multiple LLMs to identify relevant entities and relationships, automatically generating subject-predicate-object triples. These outputs are evaluated using a dedicated validation pipeline with both syntactic and semantic metrics to assess accuracy, completeness, and quality. Due to the lack of public ground-truth datasets, we created a r...