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Negative Sample Generation for geographic knowledge graph Embedding via Joint Entity Semantic Similarity and Clustering

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TRANSACTIONS IN GIS 2025年第2期29卷

作者： Qiu, Qinjun Lu, Siqi Ma, Kai Zhu, Yunqiang Huang, Zehua Xie, Zhong Tao, Liufeng Wang, Shu China Three Gorges Univ Hubei Key Lab Intelligent Vis Based Monitoring Hyd Yichang Hubei Peoples R China China Three Gorges Univ Coll Comp & Informat Technol Yichang Peoples R China China Univ Geosci Sch Comp Sci Wuhan Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources State Key Lab Resources & Environm Informat Syst Beijing Peoples R China

geographic knowledge graph (GKG) embedding (referred to as knowledge representation learning) enables the mapping of geographic entities and geographic relationships into a continuous vector space, thereby better capturing the semantic and structural information among entities in geographic space. The representation learning of GKGs requires generating corresponding negative samples based on positive samples. Negative sampling is an essential component of GKG embedding models. However, traditional negative sample generation algorithms suffer from high error rates and poor adaptability to GKGs, leading to potential replacements of entities that may not be logically reasonable in terms of geographic relationships, thus reducing the model's performance and generalization ability. To address the aforementioned challenge, this paper proposes a novel negative sampling approach for GKGs embedding, predicated on the amalgamation of entity semantic congruence and clustering techniques. Primarily, the proposed methodology employs the FastText model to encode entities within the KG into vector representations, thereby aiming to procure precise semantic embeddings of said entities. Subsequently, for improved delineation of semantically akin entities, the K-means algorithm is invoked to partition the entities into distinct clusters. Within the purview of negative sampling, entities within the same cluster as the substituted entity are randomly selected as negative instances, thus augmenting the fidelity of negative sample generation. Finally, experiments and analyses were conducted on the open-source datasets WN18, FB15k, and the constructed geographic dataset GKG12. The experimental results show that the proposed method can effectively improve the accuracy of GKG representation learning compared to the existing representation models.

关键词： entity semantic similarity geographic knowledge graph K-means knowledge graph embedding negative sample generation

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AugGKG: a grid-augmented geographic knowledge graph representation and spatio-temporal query model

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INTERNATIONAL JOURNAL OF DIGITAL EARTH 2023年第2期16卷 4934-4957页

作者： Han, Bing Qu, Tengteng Tong, Xiaochong Wang, Haipeng Liu, Hao Huo, Yuhao Cheng, Chengqi Peking Univ Acad Adv Interdisciplinary Studies Beijing Peoples R China Peking Univ Coll Engn Beijing Peoples R China Informat Engn Univ Zhengzhou Peoples R China Naval Aviat Univ Inst Informat Fus Yantai Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Wuhan Inst Digital Engn Wuhan Peoples R China

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.

关键词： geographic knowledge graph geographic knowledge representation time slice spatio-temporal query geo-hidden layer

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Construction of geographic knowledge graph based on sea area and islands supervision data 6

Construction of geographic knowledge graph based on sea area...

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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Sun, Hanrui Guo, Xue Jiang, Xiaoyi Zhu, Junxia Kang, Linchong He, Long National Marine Data and Information Service Tianjin300171 China Marine Information Technology Innovation Center of the Ministry of Natural Resources Tianjin300171 China Marine Academy of Zhejiang Province Hangzhou310012 China

ISBN: (纸本)9781510688018

In the era of big data, obtaining useful knowledge from massive data has become a big problem. With strong ability of data description and relationship recognition, knowledge graph can be applied to knowledge discovery in marine field. This paper reviews the research status of the marine domain knowledge graph, puts forward the technical framework and process of the construction of the domain knowledge graph, and constructs geographic knowledge graph based on the data resource system of sea area and island and the supervision requirements. Firstly, adopt a top-down method to design the schema layer, which is the abstract model of concepts, attributes and relationships. Then, automatically extract entities, relationships and attributes from the business database. Finally, use the graph database to store the above entity nodes and relationship edges,and build a knowledge graph, and support the comprehensive judgment and prediction analysis of marine resources management. © 2025 SPIE.

关键词： geographic knowledge graph knowledge graph sea area and islands supervision spatial relationship

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RESEARCH ON THE CONSTRUCTION OF geographic knowledge graph INTEGRATING NATURAL DISASTER INFORMATION

RESEARCH ON THE CONSTRUCTION OF GEOGRAPHIC KNOWLEDGE GRAPH I...

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ISPRS TC III and TC IV Urban Geoinformatics Conference

作者： Zhang, Binyu Yin, Chuan Liu, Kexin Zhai, Xi Sun, Yuqing Du, Mingyi Beijing Univ Civil Engn & Architecture Sch Geomat & Urban Spatial Informat Beijing 102616 Peoples R China Natl Geomat Ctr China Informat Serv Dept Beijing 100830 Peoples R China Capital Normal Univ State Key Lab Incubat Base Urban Environm Process Beijing 100048 Peoples R China

Natural disasters have a significant impact on the environment and economies of all countries around the world, and a large amount of multi-source heterogeneous geographic information data is generated every day. However, due to a lack of knowledge transformation capabilities, these nations continue to struggle with the issue of "a large amount of data and little knowledge". Therefore, it is of great significance how to extract geographic knowledge related to disasters from the vast data and construct a geographic knowledge graph integrating disaster information. Based on the theory related to knowledge extraction, this paper proposes a method to construct a natural disaster knowledge graph integrating geographic information. The core of this knowledge graph is to construct the association relationship between natural disaster concepts, research areas, and spatial data. The vocabulary and relationships associated with disaster concepts are primarily transformed by an existing word list of geographic narratives, which then provide rich semantic relationships of domain concepts for the entire knowledge graph. The research areas and spatial data types are mainly obtained through knowledge entity extraction and disambiguation methods. This disaster knowledge graph can support applications well such as natural disaster visualization and analysis, data recommendation systems, and intelligent Q&A systems, which can further improve the intelligence of natural disaster knowledge services and is expected to promote the sharing and reuse of domain knowledge graphs to a certain extent.

关键词： Natural Disasters geographic knowledge graph knowledge Service knowledge Engineering Natural Language Processing Linked Data

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MGKGR: Multimodal Semantic Fusion for geographic knowledge graph Representation

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ALGORITHMS 2024年第12期17卷 593-593页

作者： Zhang, Jianqiang Chen, Renyao Li, Shengwen Li, Tailong Yao, Hong China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci State Key Lab Biogeol & Environm Geol Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430078 Peoples R China China Univ Geosci Sch Future Technol Wuhan 430074 Peoples R China

geographic knowledge graph representation learning embeds entities and relationships in geographic knowledge graphs into a low-dimensional continuous vector space, which serves as a basic method that bridges geographic knowledge graphs and geographic applications. Previous geographic knowledge graph representation methods primarily learn the vectors of entities and their relationships from their spatial attributes and relationships, which ignores various semantics of entities, resulting in poor embeddings on geographic knowledge graphs. This study proposes a two-stage multimodal geographic knowledge graph representation (MGKGR) model that integrates multiple kinds of semantics to improve the embedding learning of geographic knowledge graph representation. Specifically, in the first stage, a spatial feature fusion method for modality enhancement is proposed to combine the structural features of geographic knowledge graphs with two modal semantic features. In the second stage, a multi-level modality feature fusion method is proposed to integrate heterogeneous features from different modalities. By fusing the semantics of text and images, the performance of geographic knowledge graph representation is improved, providing accurate representations for downstream geographic intelligence tasks. Extensive experiments on two datasets show that the proposed MGKGR model outperforms the baselines. Moreover, the results demonstrate that integrating textual and image data into geographic knowledge graphs can effectively enhance the model's performance.

关键词： multimodal geographic knowledge graph knowledge graph representation

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The Construction Method of geographic knowledge graph Ontology Model Based on GML 2019

The Construction Method of Geographic Knowledge Graph Ontolo...

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Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence

作者： Chen Xiaohui Liu Yinzhen Xu Li Ge Lei Ma Yiwei Information Engineering University Zheng Zhou Beijing Zheng Zhou

ISBN: (纸本)9781450376273

geographic ontology model is the conceptual model of geographic knowledge graph and the logical basis for constructing the pattern layer of geographic knowledge graph. In the classification of geographic ontology research, geographic ontology model is in the category of domain ontology. It is a set of abstract structures to express ontology according to the spatial location, attribute characteristics and relational characteristics of geographic data. This paper discussed the logical components and architecture of geographic ontology, designed the geographic ontology model reference to GML, described the model using OWL language, and constructed the geographic ontology model based on GML. The geographic ontology model comprises three sub-models: element model, geometric model and spatial relation model. Finally, based on Protégé ontology construction tool, this paper designed the semantic description of geographic entity and realized the construction of geographic ontology system.

关键词： geographic knowledge graph logical composition geographic ontology ontology model construction GML

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A knowledge-Guided Spatio-Temporal Correlation Measure Considering Rules and Dependency Syntax for knowledge graph Adaptive Representation

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TRANSACTIONS IN GIS 2025年第1期29卷

作者： Qiu, Qinjun Li, Haiyan Hu, Xinxin Tian, Miao Ma, Kai Zhu, Yunqiang Sun, Kai Li, Weirong Wang, Shu Xie, Zhong China Univ Geosci Sch Comp Sci Wuhan Peoples R China China Univ Geosci Key Lab Geol Survey & Evaluat Minist Educ Wuhan Peoples R China China Univ Geosci Key Lab Quantitat Resource Evaluat & Informat Engn Minist Nat Resources Wuhan Peoples R China China Three Gorges Univ Hubei Key Lab Intelligent Vis Based Monitoring Hyd Yichang Peoples R China China Three Gorges Univ Coll Comp & Informat Technol Yichang Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources State Key Lab Resources & Environm Informat Syst Beijing Peoples R China

geographic knowledge graphs (KGs) mainly describe static facts and have difficulty representing changes, greatly limiting their application in geographic information retrieval and geographic spatio-temporal processes. By analyzing the spatio-temporal features and evolution of geographic elements, this paper measures the degree of correlation between spatio-temporal information and tuples and further accurately characterizes the semantic knowledge of tuples to support accurate computation and inference of KGs. This paper proposes a novel knowledge-guided quantitative measure framework for spatio-temporal correlation by considering rules and dependency syntax from natural language texts. Firstly, the natural language processing (NLP) stage preprocess the texts and extracts the candidate tuples by dependency syntactic analysis and rule matching. Secondly, we model the spatio-temporal correlation measures by considering semantic (entity types and tuple predicate) and syntactic features (dependency distance and dependency path). Finally, we establish a specific threshold value with the extracted candidates and performing multiple levels of categorization to form the final spatio-temporal correlation strength (strong, moderate, and weak). The experimental results with a large dataset indicate that the proposed method achieves an F-score of over 0.73, which is better than those of the existing methods. The proposed spatio-temporal correlation framework has more advantages in representing geographic evolutionary knowledge, revealing the evolution mechanism of geographic elements and the evolutionary reasons.

关键词： dependent syntax geographic information retrieval geographic knowledge graph knowledge-guided approach spatio-temporal correlation

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An entity alignment approach coupling NGBoost and SHAP for constructing spatio-temporal evolution knowledge graph from historical atlases

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INTERNATIONAL JOURNAL OF geographicAL INFORMATION SCIENCE 2024年第8期38卷 1468-1485页

作者： Yang, Yongquan Cao, Min Kong, Dehui Hu, Xue Zhang, Xueying Chen, Min Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ PRC Nanjing Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Resou Nanjing Peoples R China

The historical atlases provide a wealth of information about the evolution of geography over time and space. The alignment of geographical entities across varying time periods is a crucial aspect of extracting meaningful insights into the spatio-temporal dynamics of geography. This paper proposes a geographic entity alignment approach coupling Natural Gradient Boosting (NGBoost) with SHapley Additive exPlanation (SHAP). Taking the historical atlas of China as a case study, a geographic entity alignment model based on NGBoost is constructed considering the different kinds of similarity features of geographic entities, including semantic, distance, shape, size and topology. The contribution of similarity features in the NGBoost model is analyzed using the SHAP framework so as to improve the explanatory capacity of the model. The spatio-temporal evolution relationships of geographic entities are generated by association rules depending on alignment types and represented as quadruples, for constructing geographic knowledge graphs. The proposed NGBoost method was found a superior accuracy by comparing with BP neural networks, random forests, and other alternative methods for aligning geographic entities. The constructed geographic spatio-temporal evolution knowledge graphs offer valuable support for the queries of evolutionary knowledge.

关键词： geographic knowledge graph spatio-temporal evolution NGBoost SHAP historical atlases

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Interactive Analysis of Epidemic Situations Based on a Spatiotemporal Information knowledge graph of COVID-19

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IEEE ACCESS 2022年 10卷 46782-46795页

作者： Jiang, Bingchuan You, Xiong Li, Ke Li, Tingting Zhou, Xiaojun Tan, Liheng PLA Strateg Support Force Informat Engn Univ Inst Geospatial Informat Zhengzhou 450052 Peoples R China

In view of the lack of data association in spatiotemporal information analysis and the lack of spatiotemporal situation analysis in knowledge graphs, this article combines the semantic web of the geographic knowledge graph with the visual analysis model of spatial information and puts forward the comprehensive utilization of the related technologies of the geographic knowledge graph and big data visual analysis. Then, it realizes the situational analysis of COVID-19 (Coronavirus Disease 2019) and the exploration of patient relationships through interactive collaborative analysis. The main contributions of the paper are as follows. (1) Based on the characteristics of the geographic knowledge graph, a patient entity model and an entity relationship type and knowledge representation method are proposed, and a knowledge graph of the spatiotemporal information of COVID-19 is constructed. (2) To analyse the COVID-19 patients' situations and explore their relationships, an analytical framework is designed. The framework, combining the semantic web of the geographic knowledge graph and the visual analysis model of geographic information, allows one to analyse the semantic web by using the node attribute similarity calculation, key stage mining, community prediction and other methods. (3)An efficient epidemic prevention and anti-epidemic method is proposed which is of referential significance. It is based on experiments and the collaborative analysis of the semantic web and spatial information, allowing for real-time situational understanding, the discovery of patients' relationships, the analysis of the spatiotemporal distribution of patients, super spreader mining, key node analysis, and the prevention and control of high-risk groups.

关键词： Epidemics Spatiotemporal phenomena knowledge engineering Analytical models COVID-19 Semantic Web Data models COVID-19 geographic knowledge graph spatiotemporal big data visual analysis

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Iterative geographic Entity Alignment with Cross-Attention 22nd

Iterative Geographic Entity Alignment with Cross-Attention

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22nd International Semantic Web Conference (ISWC)

作者： Dsouza, Alishiba Yu, Ran Windoffer, Moritz Demidova, Elena Univ Bonn Data Sci & Intelligent Syst DSIS Bonn Germany Lamarr Inst Machine Learning & Artificial Intelli Bonn Germany

ISBN: (纸本)9783031472398;9783031472404

Aligning schemas and entities of community-created geographic data sources with ontologies and knowledge graphs is a promising research direction for making this data widely accessible and reusable for semantic applications. However, such alignment is challenging due to the substantial differences in entity representations and sparse inter-linking across sources, as well as high heterogeneity of schema elements and sparse entity annotations in community-created geographic data. To address these challenges, we propose a novel cross-attention-based iterative alignment approach called IGEA in this paper. IGEA adopts cross-attention to align heterogeneous context representations across geographic data sources and knowledge graphs. Moreover, IGEA employs an iterative approach for schema and entity alignment to overcome annotation and interlinking sparsity. Experiments on real-world datasets from several countries demonstrate that our proposed approach increases entity alignment performance compared to baseline methods by up to 18% points in F1-score. IGEA increases the performance of the entity and tag-to-class alignment by 7 and 8% points in terms of F1-score, respectively, by employing the iterative method.

关键词： geographic knowledge graph Iterative Neural Entity Alignment

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