LLM-Driven Ontology Construction for Enterprise Knowledge Graphs
This addresses the problem of automating ontology construction for enterprises, but it is incremental as it builds on existing LLM methods with a new pipeline.
The paper tackles the resource-intensive manual process of constructing ontologies for enterprise knowledge graphs by introducing OntoEKG, an LLM-driven pipeline that accelerates generation from unstructured data, achieving a fuzzy-match F1-score of 0.724 in the Data domain.
Enterprise Knowledge Graphs have become essential for unifying heterogeneous data and enforcing semantic governance. However, the construction of their underlying ontologies remains a resource-intensive, manual process that relies heavily on domain expertise. This paper introduces OntoEKG, a LLM-driven pipeline designed to accelerate the generation of domain-specific ontologies from unstructured enterprise data. Our approach decomposes the modelling task into two distinct phases: an extraction module that identifies core classes and properties, and an entailment module that logically structures these elements into a hierarchy before serialising them into standard RDF. Addressing the significant lack of comprehensive benchmarks for end-to-end ontology construction, we adopt a new evaluation dataset derived from documents across the Data, Finance, and Logistics sectors. Experimental results highlight both the potential and the challenges of this approach, achieving a fuzzy-match F1-score of 0.724 in the Data domain while revealing limitations in scope definition and hierarchical reasoning.