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Knowledge Embedding with Geospatial Distance Restriction for Geographic Knowledge Graph Completion

by 1, 1,2, 3 and 1,2,4,5,*
1
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
National Science Library, Chinese Academy of Sciences, Beijing 100190, China
4
Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China
5
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(6), 254; https://doi.org/10.3390/ijgi8060254
Received: 4 May 2019 / Accepted: 26 May 2019 / Published: 30 May 2019
A Geographic Knowledge Graph (GeoKG) links geographic relation triplets into a large-scale semantic network utilizing the semantic of geo-entities and geo-relations. Unfortunately, the sparsity of geo-related information distribution on the web leads to a situation where information extraction systems can hardly detect enough references of geographic information in the massive web resource to be able to build relatively complete GeoKGs. This incompleteness, due to missing geo-entities or geo-relations in GeoKG fact triplets, seriously impacts the performance of GeoKG applications. In this paper, a method with geospatial distance restriction is presented to optimize knowledge embedding for GeoKG completion. This method aims to encode both the semantic information and geospatial distance restriction of geo-entities and geo-relations into a continuous, low-dimensional vector space. Then, the missing facts of the GeoKG can be supplemented through vector operations. Specifically, the geospatial distance restriction is realized as the weights of the objective functions of current translation knowledge embedding models. These optimized models output the optimized representations of geo-entities and geo-relations for the GeoKG’s completion. The effects of the presented method are validated with a real GeoKG. Compared with the results of the original models, the presented method improves the metric [email protected](Filter) by an average of 6.41% for geo-entity prediction, and the [email protected](Filter) by an average of 31.92%, for geo-relation prediction. Furthermore, the capacity of the proposed method to predict the locations of unknown entities is validated. The results show the geospatial distance restriction reduced the average error distance of prediction by between 54.43% and 57.24%. All the results support the geospatial distance restriction hiding in the GeoKG contributing to refining the embedding representations of geo-entities and geo-relations, which plays a crucial role in improving the quality of GeoKG completion. View Full-Text
Keywords: geographic knowledge graph; geographic knowledge embedding; knowledge graph completion; geographic relation triplet geographic knowledge graph; geographic knowledge embedding; knowledge graph completion; geographic relation triplet
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MDPI and ACS Style

Qiu, P.; Gao, J.; Yu, L.; Lu, F. Knowledge Embedding with Geospatial Distance Restriction for Geographic Knowledge Graph Completion. ISPRS Int. J. Geo-Inf. 2019, 8, 254. https://doi.org/10.3390/ijgi8060254

AMA Style

Qiu P, Gao J, Yu L, Lu F. Knowledge Embedding with Geospatial Distance Restriction for Geographic Knowledge Graph Completion. ISPRS International Journal of Geo-Information. 2019; 8(6):254. https://doi.org/10.3390/ijgi8060254

Chicago/Turabian Style

Qiu, Peiyuan, Jialiang Gao, Li Yu, and Feng Lu. 2019. "Knowledge Embedding with Geospatial Distance Restriction for Geographic Knowledge Graph Completion" ISPRS International Journal of Geo-Information 8, no. 6: 254. https://doi.org/10.3390/ijgi8060254

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