Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

Abstract

Geographic knowledge bases (GKBs) with multiple sources and forms are of obvious heterogeneity, which hinders the integration of geographic knowledge. Entity alignment provides an effective way to find correspondences of entities by measuring the multidimensional similarity between entities from different GKBs, thereby overcoming the semantic gap. Thus, many efforts have been made in this field. This paper initially proposes basic definitions and a general framework for the entity alignment of GKBs. Specifically, the state-of-the-art of algorithms of entity alignment of GKBs is reviewed from the three aspects of similarity metrics, similarity combination, and alignment judgement; the evaluation procedure of alignment results is also summarized. On this basis, eight challenges for future studies are identified. There is a lack of methods to assess the qualities of GKBs. The alignment process should be improved by determining the best composition of heterogeneous features, optimizing alignment algorithms, and incorporating background knowledge. Furthermore, a unified infrastructure, techniques for aligning large-scale GKBs, and deep learning-based alignment techniques should be developed. Meanwhile, the generation of benchmark datasets for the entity alignment of GKBs and the applications of this field need to be investigated. The progress of this field will be accelerated by addressing these challenges.

Hierarchical Semantic Correspondence Analysis on Feature Classes between Two Geospatial Datasets Using a Graph Embedding Method

“…This is an advantage in terms of generality as the proposed method can be applied to various geospatial datasets. Moreover, an advanced method could be developed by combining various similarity measures, such as lexical similarity, structural similarity, category similarity, shape similarity, and so on [18,28,29] into the co-occurrence matrix, in which rows and columns represent entities under analysis, such as feature classes in this study. To combine these various similarity measures between these entities, it is necessary to determinate their weight.…”

“…Although the above studies showed good results, there is room for improvement by applying recent semantic analysis techniques [16][17][18] and developing new approaches to obtain hierarchical corresponding relations of feature classes between geospatial datasets, as well as within each dataset. These techniques begin from a co-occurrence matrix in which rows and columns represent individual entities used for analysis; in this study, feature classes are these entities.…”

Multi-source knowledge fusion: a survey

“…At the same time, using the link information in the Wikipedia text (the correspondence between anchor text and entity) to make the word representation of entity in text as close as possible to the entity representation in knowledge bases, so as to realize the representation learning of text and knowledge base fusion; Zhong et al [29] also used similar ideas to fuse entity description information. Sun et al [30] summarized the current status of entity alignment algorithms in the field of geographical knowledge base research from three aspects of similarity measurement, similarity combination and consistency judgment, summarized the evaluation process of alignment results, and proposed the basic definition and general framework of entity alignment in a geographical knowledge graph. Guo et.al [31] proposed recurrent skipping networks for entity alignment (RSN4EA), which leverages biased RW (Radom Walk) sampling for generating long paths across knowledge graphs and generates the paths with a novel RSN (recurrent skipping network).…”

Guest Editor’s Editorial “Cognitive Aspects of Human-Computer Interaction for GIS”

“…Click the info button The review article of K. Sun, Y. Zhu and J. Song [15] describes the "Progress and Challenges on Entity Alignment of Geographic Knowledge Bases (GKBs)". To overcome the heterogeneity of GKBs, an entity alignment provides an effective way to find correspondencies of entities by measuring the multidimensional similarity between the entities from different GKBs.…”