CITYTWIN – AI-based Decision Support System for Semantic Search and Analysis of Location-based Information for Urban and Site Planning

Abstract. The development of a knowledge-based decision support system for the evaluation and planning of location and urban development concepts was implemented. In order to achieve this goal, cross-domain ontologies were developed for interdisciplinary databases, which are then mapped in semantic networks. The exponential growth in computing power in the hardware sector alone can no longer solve this problem, but at the same time enables the application of new methods for storing and evaluating data. Essentially, it is no longer just about the digital recording of object properties in conventional databases, but also about the digital representation of their significance for specific questions and the linking of meanings across the boundaries of specialist domains. This information is stored in a multimedia knowledge base, together with the methods and rules for its use and the evaluations and decisions based on it. The motivation for this project is the rapidly growing amount of data, which extends across ever new specialist domains and can no longer be sufficiently integrated into the decision-making of experts using conventional methods of knowledge acquisition. After determining this data, it was linked to a georeferencing. Within the framework of the project, documents were analyzed with the help of AI and examined for semantic text corpora. This data was georeferenced. Various algorithms were used to accomplish this task, including TF-IDF, TextRank and Word2Vec.