Ontology-grounded Automatic Knowledge Graph Construction by LLM under Wikidata schema

TL;DR

This paper introduces an ontology-grounded method using Large Language Models to construct high-quality, interpretable knowledge graphs aligned with Wikidata, enabling scalable and semi-automated knowledge base expansion.

Contribution

It presents a novel ontology-based framework leveraging LLMs for knowledge graph construction that ensures consistency, interpretability, and interoperability with Wikidata.

Findings

Competitive performance on benchmark datasets

High-quality, human-interpretable knowledge graphs

Potential for scalable, semi-automated knowledge base expansion

Abstract

We propose an ontology-grounded approach to Knowledge Graph (KG) construction using Large Language Models (LLMs) on a knowledge base. An ontology is authored by generating Competency Questions (CQ) on knowledge base to discover knowledge scope, extracting relations from CQs, and attempt to replace equivalent relations by their counterpart in Wikidata. To ensure consistency and interpretability in the resulting KG, we ground generation of KG with the authored ontology based on extracted relations. Evaluation on benchmark datasets demonstrates competitive performance in knowledge graph construction task. Our work presents a promising direction for scalable KG construction pipeline with minimal human intervention, that yields high quality and human-interpretable KGs, which are interoperable with Wikidata semantics for potential knowledge base expansion.