Geographical Entity Community Mining Based on Spatial and Semantic Association

Abstract

The relevance of geographic entities has always been the focus of research on geographic information retrieval, geographic knowledge graph and recommendation systems. Traditional research methods, which use spatial or semantic similarity to calculate the correlation between regions, have certain one-sidedness and limitations. The network topology can clearly represent the relationship between entities. However, semantic relationships are difficult to define, so there are few cases where network-related algorithms are used to solve the relevance of geographic entities. With the development of the Internet, web pages provide people with a huge amount of information, and geographical names as a key element are often ignored by researchers, and the rich semantic information contained in it needs further research. This study attempts to explore the geographic entity relevance of integrated semantics and spatial factors based on textual data from a network perspective. Based on the community mining algorithm, the experiment studies the aggregation characteristics of geographic entities and can find areas that are close to each other and tightly related, which is more satisfied with people's common sense.