Abstract
Abstract This article explores how natural language processing techniques can be applied to extract information from spatial planning documents and how this information can be represented in a knowledge graph. The proposed method uses named entity recognition to extract relevant information from text and structure it into labels and corresponding values. The extracted information is represented in the form of a knowledge graph, which allows for better understanding and management of complex relationships between different elements in spatial planning documents. For this purpose, a dedicated ontology was developed. The research demonstrates that the proposed method achieves good results with high precision, recall, and F1 scores for all entity types, with particularly remarkable results for biologically active area predictions. The practical application of this method in spatial planning can contribute to improving decision-making processes and streamlined collaboration between different entities involved in spatial planning.
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.