BEKG: A Built Environment Knowledge Graph

TL;DR

This paper presents BEKG, a comprehensive knowledge graph for the built environment created from over 80,000 paper abstracts, utilizing BERT-based models for entity and relation extraction, with a visualization system for domain insights.

Contribution

The paper introduces a large-scale built environment knowledge graph built from extensive literature, with annotated datasets and BERT models for accurate information extraction.

Findings

Achieved over 85% accuracy in entity and relation extraction tasks.

Constructed a knowledge graph with more than 200,000 entities and relations.

Developed a visualization system to explore domain-specific connections.

Abstract

Practices in the built environment have become more digitalized with the rapid development of modern design and construction technologies. However, the requirement of practitioners or scholars to gather complicated professional knowledge in the built environment has not been satisfied yet. In this paper, more than 80,000 paper abstracts in the built environment field were obtained to build a knowledge graph, a knowledge base storing entities and their connective relations in a graph-structured data model. To ensure the retrieval accuracy of the entities and relations in the knowledge graph, two well-annotated datasets have been created, containing 2,000 instances and 1,450 instances each in 29 relations for the named entity recognition task and relation extraction task respectively. These two tasks were solved by two BERT-based models trained on the proposed dataset. Both models attained an accuracy above 85% on these two tasks. More than 200,000 high-quality relations and entities were obtained using these models to extract all abstract data. Finally, this knowledge graph is presented as a self-developed visualization system to reveal relations between various entities in the domain. Both the source code and the annotated dataset can be found here: https://github.com/HKUST-KnowComp/BEKG.