Domain Ontology-Driven Knowledge Graph Generation from Text

A knowledge graph serves as a unified and standardized representation for extracting and representing textual information. In the field of knowledge extraction and representation research, named entity recognition and relation extraction provide effective solutions for knowledge graph generation tasks. However, it is a challenge that lies in extracting domain-specific knowledge from the rich and general textual corpora and generating corresponding domain knowledge graphs to support domain-specific reasoning, question-answering, and decision-making tasks. The hierarchical domain knowledge representation model (i.e., domain ontology) provides a solution for this problem. Therefore, we propose an end-to-end approach based on domain ontology embedding and pre-trained language models for domain knowledge graph generation from text, which incorporates domain node recognition and domain relation extraction phases. We evaluated our domain ontology-driven model on the Wikidata-TekGen dataset and the DBpedia-WebNLG dataset, and the results indicate that our approach based on the pre-trained language models with fewer parameters compared with the baseline models has significantly contributed to the domain knowledge graph generation without prompts.