Abstract
AbstractIn order to fully automate the environmental regulatory compliance checking process, rules should be automatically extracted from applicable environmental regulatory textual documents, such as energy conservation codes. In the authors’ automated compliance checking (ACC) approach, prior to rule extraction, the text is first classified into predefined categories to only retrieve relevant clauses and filter out irrelevant ones, thereby improving the efficiency and accuracy of rule extraction. Machine learning (ML) techniques have been commonly used for text classification (TC). Nonontology-based, ML-based TC has, generally, performed well. However, given the need for an exceptionally high performance in TC to support high performance in ACC, further TC performance improvement is needed. To address this need, an ontology-based TC algorithm is proposed to further improve the classification performance by utilizing the semantic features of the text. A domain ontology for conceptualizing the environment...
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.