LLM-Driven Ontology Construction for Enterprise Knowledge Graphs

TL;DR

This paper presents OntoEKG, a novel LLM-driven pipeline that automates the creation of domain-specific ontologies for enterprise knowledge graphs, reducing manual effort and improving efficiency.

Contribution

The paper introduces OntoEKG, a new two-phase approach leveraging LLMs for automated ontology construction from unstructured enterprise data.

Findings

Achieved a fuzzy-match F1-score of 0.724 in the Data domain.

Demonstrated potential of LLMs in ontology extraction and structuring.

Identified challenges in scope definition and hierarchical reasoning.

Abstract

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.