An integrated approach of knowledge extraction and ontology-based reasoning for green building evaluation and electricity efficiency

Botong Li^1*Hongjie Jia¹Hongwei Yu²Cong Fu²

• ¹Tianjin University, Tianjin, China
• ²Beijing Guodian Futong Science and Technology Development CO. LTD, Beijing, China

Promoting green building practices is essential in the broader effort to combat climate change and achieve sustainable development. Green building evaluation plays a vital role in assessing a structure's performance across various criteria, ensuring that it aligns with sustainable electricity usage, environmental protection, and occupant well-being. Current approaches to building ontologies for green building evaluation are largely manual and subjective, relying heavily on expert input. This study presents a novel approach to green building evaluation by integrating automated knowledge extraction and ontology development within an intelligent assessment framework.Utilizing advanced natural language processing (NLP) and machine learning techniques, the research extracts relevant knowledge from diverse sources such as regulations, standards, and academic literature. The extracted knowledge is then organized into an ontology using Protégé, which facilitates the application of Semantic Web Rule Language (SWRL) rules for comprehensive green building evaluation, thus contributing to high electricity efficiency. The proposed method enhances the accuracy, objectivity, and efficiency of the evaluation process for green building, addressing the need for scalable and automated tools in the field of sustainability.