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 d...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.展开更多
As an emerging knowledge representation model in the domain of knowledge graphs,geographic knowledge graph can take full advantage of semantic,spatial and temporal information to facilitate answering spatio-temporal q...As an emerging knowledge representation model in the domain of knowledge graphs,geographic knowledge graph can take full advantage of semantic,spatial and temporal information to facilitate answering spatio-temporal questions and completing relations.However,the representation of geographic knowledge graphs still has issues such as the difficulty of unified heterogeneous spatio-temporal data modelling,weak ability to answer spatio-temporal queries for dynamic multiobjective problems,and low efficiency of graph querying.This paper presents a grid-augmented geographic knowledge graph(AugGKG)based on the GeoSOT global subdivision grid model and time slice subgraph architecture.AugGKG discretely normalizes the spatio-temporal data of the graph,which involves five types of nodes and two types of relations.By using the geo-hidden layer of the graph and geocoding algebraic operations,the AugGKG can quickly answer complex multiobjective spatio-temporal queries and complete implicit spatio-temporal relations.Compared with existing geographic knowledge graphs(YAGO,GeoKG and GEKG),the comparative experiments verified the obvious advantages of AugGKG in terms of uniformity of accuracy,completeness,and efficiency.Hence,AugGKG is expected to be regarded as an innovative and robust geographic knowledge graph that can perform fast computation and relation completion for complex spatio-temporal queries in future geospatial question answering applications.展开更多
基金This research was supported by the National Natural Science Foundation of China[41631177,41801320].
文摘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.
基金supported by the Songshan Laboratory Projects[grant number 221100211000-03]Excellent Youth Fund of Natural Science Foundation of Henan Province[grant number 212300410096]+2 种基金National Natural Science Foundation of China[grant numbers 62076249,42201513]National Defense Basic Scientific Research Program of China[grant number 2022-JCJQ-JJ-0287]Natural Science Foundation of Shandong Province[grant number ZR202209130044]。
文摘As an emerging knowledge representation model in the domain of knowledge graphs,geographic knowledge graph can take full advantage of semantic,spatial and temporal information to facilitate answering spatio-temporal questions and completing relations.However,the representation of geographic knowledge graphs still has issues such as the difficulty of unified heterogeneous spatio-temporal data modelling,weak ability to answer spatio-temporal queries for dynamic multiobjective problems,and low efficiency of graph querying.This paper presents a grid-augmented geographic knowledge graph(AugGKG)based on the GeoSOT global subdivision grid model and time slice subgraph architecture.AugGKG discretely normalizes the spatio-temporal data of the graph,which involves five types of nodes and two types of relations.By using the geo-hidden layer of the graph and geocoding algebraic operations,the AugGKG can quickly answer complex multiobjective spatio-temporal queries and complete implicit spatio-temporal relations.Compared with existing geographic knowledge graphs(YAGO,GeoKG and GEKG),the comparative experiments verified the obvious advantages of AugGKG in terms of uniformity of accuracy,completeness,and efficiency.Hence,AugGKG is expected to be regarded as an innovative and robust geographic knowledge graph that can perform fast computation and relation completion for complex spatio-temporal queries in future geospatial question answering applications.
