Ontology-grounded Automatic Knowledge Graph Construction by LLM under Wikidata schema
This work addresses scalable knowledge graph construction with minimal human intervention for interoperability with Wikidata, though it appears incremental.
The paper tackles the problem of automatic knowledge graph construction by proposing an ontology-grounded approach using Large Language Models, achieving competitive performance on benchmark datasets.
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.