Abstract

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 Hits@10(Filter) by an average of 6.41% for geo-entity prediction, and the Hits@1(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.

Highlights

  • A Knowledge Graph (KG) is a system that understands facts about people, places and things, and how these entities are all connected [1]

  • Link prediction is based on the results of KG completion as Section 3.3, where the translation KE model is trained by the fact triplets of training set

  • The missing geo-entities or geo-relations can be predicted by a vector operation according to both the semantic relationship and the geospatial distance feature between geo-entities in the Geographic Knowledge Graph (GeoKG)

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Summary

Introduction

A Knowledge Graph (KG) is a system that understands facts about people, places and things, and how these entities are all connected [1]. To emphasize the “connection”, facts in a KG are represented as triplets with the form or . Geo-Inf. 2019, 8, 254 ISPRS Int. J. Geo-Inf. 2019, 7, x FOR PEER REVIEW

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