Abstract
Geographical entity community discovery aims at discovering geographical entities that are closely related to each other. It has shown great practical value in many social applications such as economic development, administrative management, and resource utilization. Traditional research methods use network topology to represent the relationship between geographical entities, but cannot accurately define semantic relationships. To deal with this problem, this paper uses the network news that provides massive semantic information, and integrates the semantic information and spatial information into the geographic entity community discovery algorithm. An edge weight calculation method based on semantic association strength, geographic entity influence, and boundary connection distance is proposed. Experimental analyses show that compared with existing methods, the new algorithm improves the accuracy of spatial community division. A case study on real-world datasets shows that the experimental results of the new method are better than the existing methods and are more satisfied with common sense.
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Yu, M., Wang, Z., Pang, Y., Xu, Y. (2021). Geographical Entity Community Discovery Based on Semantic Similarity. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12837. Springer, Cham. https://doi.org/10.1007/978-3-030-84529-2_35
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