BEKG: A Built Environment Knowledge Graph
Xiaojun Yang, Haoyu Zhong, Penglin Du, Keyi Zhou, Xingjin, Lai, Zhengdong Wang, Yik Lun Lau, Yangqiu Song, Liyaning Tang

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBIM and Construction Integration · Data Quality and Management
MethodsBalanced Selection
