Geography is a discipline that touches multiple sciences and has been key to bridging numerous fields of knowl edge.This gives geography the advantage of connecting natural(e.g.,biology,ecology,climatology,geomorphol ...Geography is a discipline that touches multiple sciences and has been key to bridging numerous fields of knowl edge.This gives geography the advantage of connecting natural(e.g.,biology,ecology,climatology,geomorphol ogy)with social and human(e.g.,education,demography,sociology)sciences.The spatialisation of information from different sciences allows us to understand distribution patterns and connections between different realities.Thus,geographical knowledge is essential for an integrated and consistent understanding of our world.The Sus tainable Development Goals(SDGs)established by the United Nations(UN)in 2015 were essential to unifying the world towards a common goal.To achieve these,17 goals and 169 targets were created,and knowledge from multiple sciences is needed to support them.It is a huge challenge,and different knowledge branches are needed to connect.Geography and geographical knowledge have this capacity and support all 17 goals and 169 targets.Although this is a reality,as it will be explained in this editorial,SDG’s achievement for some is becoming utopic and unrealistic due to our world’s differences.It is time to think about the post-2030 SDGs,in which geography and geographic knowledge will be essential unequivocally.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
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
Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and di...Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.展开更多
Higher education institutions across the world are responding to globalization through internationalization.However,there is limited research that focuses on the benefits of both internationalization-at-home and cross...Higher education institutions across the world are responding to globalization through internationalization.However,there is limited research that focuses on the benefits of both internationalization-at-home and cross-border internationalization to students’cosmopolitan competency from the perspective of students.Therefore,this qualitative case study explored the benefits of internationalization to students from the perspectives of both domestic and international undergraduate and graduate students at two U.S universities.Purposeful and snowball sampling strategies were adopted to identify sixteen students.Data were garnered via interviews,institutions’websites,and documents.Constant comparative method was employed to analyze the data.Findings from this study revealed that students acquired bilingual or multilingual abilities,fi rsthand cultural knowledge,global knowledge,cultural nuances critical to showing respect to people from different cultures and geographical backgrounds,friendship and networking,personal growth,high tendency to develop empathy through university internationalization,and opportunity to taste food from different parts of the world.The study recommends that,institutions of higher education should provide opportunities such as foreign language courses,Rosetta Stones,language laboratories,foreign language conversation hour sessions,English as a Second Language(ESL)or Intensive English Language program for students.Also,administrators and faculty are encouraged to provide a platform for study abroad returnees to share their experiences with their colleagues.Higher education institutions should continue to recruit more international students to enrich students’experiences and global learning.展开更多
文摘Geography is a discipline that touches multiple sciences and has been key to bridging numerous fields of knowl edge.This gives geography the advantage of connecting natural(e.g.,biology,ecology,climatology,geomorphol ogy)with social and human(e.g.,education,demography,sociology)sciences.The spatialisation of information from different sciences allows us to understand distribution patterns and connections between different realities.Thus,geographical knowledge is essential for an integrated and consistent understanding of our world.The Sus tainable Development Goals(SDGs)established by the United Nations(UN)in 2015 were essential to unifying the world towards a common goal.To achieve these,17 goals and 169 targets were created,and knowledge from multiple sciences is needed to support them.It is a huge challenge,and different knowledge branches are needed to connect.Geography and geographical knowledge have this capacity and support all 17 goals and 169 targets.Although this is a reality,as it will be explained in this editorial,SDG’s achievement for some is becoming utopic and unrealistic due to our world’s differences.It is time to think about the post-2030 SDGs,in which geography and geographic knowledge will be essential unequivocally.
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.
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Deep-time Digital Earth(DDE)Big Science Program(No.GJ-C03-SGF-2025-004)National Natural Science Foundation of China(No.42394063)Sichuan Science and Technology Program(No.2025ZNSFSC0325).
文摘Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.
文摘Higher education institutions across the world are responding to globalization through internationalization.However,there is limited research that focuses on the benefits of both internationalization-at-home and cross-border internationalization to students’cosmopolitan competency from the perspective of students.Therefore,this qualitative case study explored the benefits of internationalization to students from the perspectives of both domestic and international undergraduate and graduate students at two U.S universities.Purposeful and snowball sampling strategies were adopted to identify sixteen students.Data were garnered via interviews,institutions’websites,and documents.Constant comparative method was employed to analyze the data.Findings from this study revealed that students acquired bilingual or multilingual abilities,fi rsthand cultural knowledge,global knowledge,cultural nuances critical to showing respect to people from different cultures and geographical backgrounds,friendship and networking,personal growth,high tendency to develop empathy through university internationalization,and opportunity to taste food from different parts of the world.The study recommends that,institutions of higher education should provide opportunities such as foreign language courses,Rosetta Stones,language laboratories,foreign language conversation hour sessions,English as a Second Language(ESL)or Intensive English Language program for students.Also,administrators and faculty are encouraged to provide a platform for study abroad returnees to share their experiences with their colleagues.Higher education institutions should continue to recruit more international students to enrich students’experiences and global learning.
