A dynamic weighted model for semantic similarity measurement between geographic feature categories

doi:10.11947/j.AGCS.2023.20220330

Abstract

Abstract: Semantic similarity is a key technology to solve the problem of semantic heterogeneity of geographic feature categories, and plays an important role in geographic data sharing and exchange applications. In this article, a semantic similarity calculation model of geographic feature categories based on dynamic weights is proposed to represent the difference in importance of semantic properties among geographic feature categories for the need of fundamental geographical domain application. TF-IDF algorithm is introduced and the particularity of the property value is used to calculate the dynamic weight of a semantic property. And the similarity model between a pair of complex properties is proposed, and then the final similarity between geographic feature categories is calculated. Finally, 200 pairs of samples are selected from the basic geographic element categories to calculate the semantic similarity, and compared with other similarity calculation models. The experimental results show that the model proposed in this article can effectively reflect the importance difference of semantic properties and obtain more reasonable semantic similarity between geographic feature categories.

Key words: semantic similarity, TF-IDF, dynamic weight, geographic feature category

CLC Number:

P227

TAN Yongbin, GAO Lingling, LI Lin, CHENG Penggen, WANG Hong, LI Xiaolong, CHEN Cheng. A dynamic weighted model for semantic similarity measurement between geographic feature categories[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(5): 843-851.

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