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ISPRS Int. J. Geo-Inf. 2019, 8(2), 77; https://doi.org/10.3390/ijgi8020077

Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

1,2,3
,
1,2,4
and
1,2,4,*
1
State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China
2
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
4
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Received: 23 November 2018 / Revised: 25 January 2019 / Accepted: 29 January 2019 / Published: 6 February 2019
(This article belongs to the Special Issue Cognitive Aspects of Human-Computer Interaction for GIS)
PDF [592 KB, uploaded 6 February 2019]

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.
Keywords: geographic knowledge bases; entity alignment; similarity metrics; similarity combination; knowledge conflation; knowledge integration geographic knowledge bases; entity alignment; similarity metrics; similarity combination; knowledge conflation; knowledge integration
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Sun, K.; Zhu, Y.; Song, J. Progress and Challenges on Entity Alignment of Geographic Knowledge Bases. ISPRS Int. J. Geo-Inf. 2019, 8, 77.

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