  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.
