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ISPRS Int. J. Geo-Inf. 2019, 8(2), 59;

A Knowledge-Based Filtering Method for Open Relations among Geo-Entities

National Science Library, Chinese Academy of Sciences, Beijing 100190, China
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences, Beijing 100049, China
Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Author to whom correspondence should be addressed.
Received: 12 November 2018 / Revised: 16 January 2019 / Accepted: 25 January 2019 / Published: 28 January 2019
(This article belongs to the Special Issue Open Science in the Geospatial Domain)
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Knowledge graphs (KGs) are crucial resources for supporting geographical knowledge services. Given the vast geographical knowledge in web text, extraction of geo-entity relations from web text has become the core technology for construction of geographical KGs; furthermore, it directly affects the quality of geographical knowledge services. However, web text inevitably contains noise and geographical knowledge can be sparsely distributed, both of which greatly restrict the quality of geo-entity relationship extraction. We propose a method for filtering geo-entity relations based on existing knowledge bases (KBs). Accordingly, ontology knowledge, fact knowledge, and synonym knowledge are integrated to generate geo-related knowledge. Then, the extracted geo-entity relationships and the geo-related knowledge are transferred into vectors, and the maximum similarity between vectors is the confidence value of one extracted geo-entity relationship triple. Our method takes full advantage of existing KBs to assess the quality of geographical information in web text, which is helpful to improve the richness and freshness of geographical KGs. Compared with the Stanford OpenIE method, our method decreased the mean square error (MSE) from 0.62 to 0.06 in the confidence interval [0.7, 1], and improved the area under the receiver operating characteristic (ROC) curve (AUC) from 0.51 to 0.89. View Full-Text
Keywords: geographical knowledge service; knowledge graphs; open relation extraction; confidence assessment geographical knowledge service; knowledge graphs; open relation extraction; confidence assessment

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Yu, L.; Qiu, P.; Gao, J.; Lu, F. A Knowledge-Based Filtering Method for Open Relations among Geo-Entities. ISPRS Int. J. Geo-Inf. 2019, 8, 59.

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