Key points are not available for this paper at this time.
The construction of knowledge graphs for spatialization experts represents a significant research domain within the field of spatial knowledge service platforms. This study aims to resolve the prevalent issues of low entity disambigation accuracy and the inadequate representation of spatial knowledge in conventional expert knowledge graphs. Focusing on the discipline of surveying and mapping, the paper introduces a novel approach to entity disambiguation that synergizes methods for unknown institutional entity resolution with community detection predicated on co-authorship networks. Additionally, by integrating principles pertinent to spatial knowledge organization, the paper enhances the traditional expert knowledge graph with a spatial dimension and formulates a strategic framework for the visualization of knowledge maps. The proposed techniques for entity disambiguation and expert knowledge graph visualization furnish viable references for the development and deployment of knowledge graphs within the survey.
Jin et al. (Thu,) studied this question.