Geographic categories mapping based on ontology attribute characteristics learning

Abstract

The heterogeneity of geospatial information will persist for a long time. As the key to overcome the semantic heterogeneity, categories mapping has gained considerable attention. In previous studies, the existing geographic ontologies cannot support enough multi-semantic extension to conquer semantic heterogeneity effectively. When introduced to explore categories mapping, many semantic similarity measures encountered the problem of subjective weight setting and failed to make full use of the organizational structure information of categories. The rapid development of Artificial Intelligence (AI) and Natural Language Processing (NLP) bring new enlightenment to the semantic analysis and understanding in the geographic information field. Therefore, this paper proposes a new geographic categories mapping method based on ontology attribute characteristics learning, which utilizes ontology attributes and the classification hierarchy of geographic categories. Firstly, a basic semantic framework based on ontology attributes is defined to realize the semantic vectorization descriptions of geographic categories, by extracting semantic knowledge from definitions. Then, a new hierarchical coding method is proposed to describe the classification hierarchy of categories and identify the classification status of each category. After that, a self-learning mapping mechanism based on BP neural network is used to establish the non-linear relationship between ontology attribute eigenvectors and classification states, which can support categories mapping. Finally, some categories mappings are formed by this method to evaluate transition effects, and introduces the category differentiation degree to analyze the influence of classification structure on prediction accuracy. The preliminary results show the feasibility and reliability of the proposed model for automatic semantic mapping.