Geographic Knowledge Graph Attribute Normalization: Improving the Accuracy by Fusing Optimal Granularity Clustering and Co-Occurrence Analysis

作者：Chuan Yin, Binyu Zhang, Wanzeng Liu, Mingyi Du, Nana Luo, Xi Zhai, Ba Tu · 发表于：ISPRS International Journal of Geo-Information · 年份：2022 · DOI：10.3390/ijgi11070360 · 被引用次数：6 · 研究领域：Geographic Information Systems Studies、Advanced Graph Neural Networks、Data Quality and Management

Expansion of the entity attribute information of geographic knowledge graphs is essentially the fusion of the Internet’s encyclopedic knowledge. However, it lacks structured attribute information, and synonymy and polysemy always exist. These reduce the quality of the knowledge graph and cause incomplete and inaccurate semantic retrieval. Therefore, we normalize the attributes of a geographic knowledge graph based on optimal granularity clustering and co-occurrence analysis, and use structure and the semantic relation of the entity attributes to identify synonymy and correlation between attributes. Specifically: (1) We design a classification system for geographic attributes, that is, using a community discovery algorithm to classify the attribute names. The optimal clustering granularity is identified by the marker target detection algorithm. (2) We complete the fine-grained identification of attribute relations by analyzing co-occurrence relations of the attributes and rule inference. (3) Finally, the performance of the system is verified by manual discrimination using the case of “landscape, forest, field, lake and grass”. The results show the following: (1) The average precision of spatial relations was 0.974 and the average recall was 0.937; the average precision of data relations was 0.977 and the average recall was 0.998. (2) The average F1 for similarity results is 0.473; the average F1 for co-occurrence analysis results is 0.735; the average F1 for rule-based modific...