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Detecting geo-relation phrases from web texts for triplet extraction of geographic knowledge:a context-enhanced method

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Big Earth Data 2019年第3期3卷 297-314页

作者： Peiyuan Qiu Li Yu Jialiang Gao Feng Lu State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina National Science Library Chinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina The Academy of Digital China Fuzhou UniversityFuzhouChina Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application NanjingChina

As an effective organization form of geographic information,a geographic knowledge graph(GeoKG)facilitates numerous geography-related analyses and services.The completeness of triplets regarding geographic knowledge determines the quality of GeoKG,thus drawing considerable attention in the related domains.Mass unstructured geographic knowledge scattered in web texts has been regarded as a potential source for enriching the triplets in GeoKGs.The crux of triplet extraction from web texts lies in the detection of key phrases indicating the correct geo-relations between geo-entities.However,the current methods for key-phrase detection are ineffective because the sparseness of the terms in the web texts describing geo-relations results in an insufficient training corpus.In this study,an unsupervised context-enhanced method is proposed to detect geo-relation key phrases from web texts for extracting triplets.External semantic knowledge is introduced to relieve the influence of the sparseness of the georelation description terms in web texts.Specifically,the contexts of geo-entities are fused with category semantic knowledge and word semantic knowledge.Subsequently,an enhanced corpus is generated using frequency-based statistics.Finally,the geo-relation key phrases are detected from the enhanced contexts using the statistical lexical features from the enhanced corpus.Experiments are conducted with real web texts.In comparison with the well-known frequency-based methods,the proposed method improves the precision of detecting the key phrases of the geo-relation description by approximately 20%.Moreover,compared with the well-defined geo-relation properties in DBpedia,the proposed method provides quintuple key-phrases for indicating the geo-relations between geo-entities,which facilitate the generation of new triplets from web texts.

关键词： Geographic knowledge graph triplet extraction geo-entity relation keyphrase detection context enhancement

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