Transforming text into knowledge graph: Extracting and structuring information from spatial development plans

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.