[Objectives] With the application of knowledge graph techniques in the field of Geographical Information Science (GIS), the Geographical Knowledge Graph (GeoKG) has become a key research direction. GeoKGs often lack sufficient geographic knowledge coverage, which can negatively impact downstream applications. Therefore, reasoning techniques are essential for GeoKG to complete missing knowledge, identify inconsistencies, and predict trends in geographic phenomena. Unlike reasoning techniques applied to general knowledge graphs, reasoning on GeoKGs must handle the unique and complex spatial and temporal characteristics of geographic phenomena. This paper comprehensively introduces and summarizes recent advances in GeoKG reasoning. [Analysis] First, it introduces the relevant concepts and problem definitions of GeoKG reasoning. Second, it analyzes the two core tasks of GeoKG reasoning: knowledge completion and prediction. The reasoning model for knowledge completion primarily fills gaps in the graph to ensure knowledge integrity, while the reasoning model for prediction aims to forecast future trends based on existing geographic data. These two models are optimized for different application scenarios, with different focuses in processing geographic data. [Prospect] Finally, the paper explores future development trends in GeoKG reasoning, highlighting areas such as processing complex relationships in spatiotemporal data, reasoning with multi-scale geographic knowledge, fusing multimodal data, and enhancing the interpretability and intelligence of reasoning models. Additionally, the integration of GeoKGs with large-scale pre-trained models is expected to become a key area of focus.

As an important method for simulating and understanding geographical environments, model based geographic simulation is essential for extracting geographical knowledge and revealing geographical laws. The meteorological process, as a crucial component of geographic environment, plays a significant role in various aspects of geographic simulation, thereby influencing the comprehensive simulation and resolution of complex problems within the geographic environment. However, meteorological models usually require a high level of expertise, involving intricate simulation knowledge and encompassing various components such as input data, simulation scheme settings, and others. Therefore, efficiently managing and sharing meteorological simulation knowledge has become the key step to improve the scientific rigor and accuracy of simulation. In this study, case-based reasoning and knowledge graph are applied to unify heterogeneous knowledge of meteorological simulation, and a retrieval method of meteorological simulation knowledge considering semantic and graph structure similarity is proposed to enhance the sharing capability of meteorological simulation knowledge. Firstly, based on Chinese meteorological simulation literature data, this study extracts meteorological simulation knowledge by using Bidirectional Long Short-Term Memory with Conditional Random Fields model (BiLSTM-CRF) and constructs a meteorological simulation knowledge base in the knowledge graph with simulation cases as the carrier of knowledge. As an example, in this study, a total of 795 cases, expressed as nodes and edges, are constructed and stored in knowledge graph. Secondly, a similarity assessment model for meteorological simulation cases is constructed to achieve the meteorological simulation knowledge retrieval considering both semantic and graph structure aspects. In detail, the model utilizes the Bert semantic model trained by the meteorological simulation corpus to mine the semantic features of meteorological simulation knowledge cases. The structural features of cases stored in the graph are also extracted, and the weights are set using the Analytic Hierarchy Process (AHP) hierarchical analysis method, so as to accurately measure the similarity of meteorological simulation knowledge cases. Thirdly, a visual prototype system for sharing meteorological simulation knowledge is developed, which can directly recommend parameter schemes for meteorological simulation knowledge based on requirements from users and provide knowledge reference for geographic system simulation personnel involved in meteorological processes. To validate the stability of the retrieval system, we selected 300 input cases, and the retrieval results provide the top 5 similar cases for each case. In this context, the accuracy of the system reaches 91.3%, improving the efficiency of meteorological simulation knowledge sharing and reuse.

In the field of intelligent transportation, various information collection devices have produced a massive amount of multi-source heterogeneous data. These data encompass various types of information, including vehicle trajectories, road conditions, and traffic incidents, soured from devices such as traffic cameras, sensors, and GPS. However, the current challenge faced by researchers and practitioners is how to correlate and integrate the massive amount of heterogeneous data to facilitate decision support. To address this challenge, knowledge graph technology, with its powerful entity-to-entity modeling ability, has shown great potential in knowledge mining, representation, management, and reasoning, making it well-suited for intelligent transportation applications. In this paper, we first review the construction techniques for geographic traffic graphs, multimodal knowledge graphs, and dynamic knowledge graphs, demonstrating the broad applicability of knowledge graphs in the field of intelligent transportation. Secondly, we summarize relevant algorithms of multi-modal knowledge graph representation learning and discuss dynamic knowledge graph representation learning in the field of intelligent transportation. Knowledge graph representation learning technology plays a crucial role in creating high-quality knowledge graphs by capturing and organizing the relationships between entities and their attributes within the transportation domain. This technology utilizes advanced machine learning algorithms to analyze and process the heterogeneous data from various sources to extract meaningful patterns and structures. We also introduce the completion technology and causal reasoning technology in dynamic transportation multi-modal knowledge graph, which is useful for improving the data of intelligent transportation systems. Comprehension ability and decision-making reasoning level have important theoretical significance and practical application prospects. Thirdly, we summarize the solutions of knowledge graph that provide important support for intelligent decision-making in several application scenarios. The utilization of knowledge graphs in intelligent transportation systems facilitates real-time data integration and enables correlation analysis of diverse data sources to provide a holistic view of the traffic ecosystem. This comprehensive understanding empowers decision-makers to implement targeted interventions and proactive measures, ultimately mitigating traffic congestion and reducing the occurrence of accidents. Through the continuous refinement and enrichment of the traffic knowledge graph, the intelligent transportation system can adapt and evolve to address emerging challenges and optimize transport networks for enhanced efficiency and safety. Finally, we analyze and discuss the existing technical bottlenecks. The future of traffic knowledge graphs and their auxiliary applications are also prospected and discussed, highlighting the potential impact of this important technology on intelligent transportation systems.

Nowadays, cities have emerged as one of the core elements for the sustainable development of human society. This also aligns well with the United Nations Sustainable Development Goals on sustainable cities. The pivotal role of cities is also demonstrated by the rapid development of big data and artificial intelligence technologies. There have been more and more studies dedicated to the realm of data-driven urban sustainability, in which the complex processes of urban sustainable development, encompassing social, economic, and ecological dimensions, are monitored, interpreted, and evaluated through massive urban data from multiple sources. However, a common limitation is that most existing studies concentrate on individual application scenarios and singular data sources and ignore the intricate interconnections among diverse urban data sources and multiple urban elements, making it challenging to explore findings across diverse urban sustainability contexts. Therefore, to address this critical gap, in this paper, we propose a novel approach for urban sustainable development driven by Urban Business Area/Region Knowledge Graph (UKG). This approach incudes two fundamental steps: the construction of a comprehensive ontology for the UKG based on massive multi-source urban data, and the subsequent synthesis of knowledge guided by this ontology to create the UKG. The construction of the UKG ontology captures important elements in cities as well as their complex interconnections, e.g., people, locations, and organizations, and their relationships in terms of spatiality, function, and association. This ontological architecture lays the foundation for the subsequent knowledge fusion, ultimately leading to the construction of UKG. The practical applications of UKG in driving urban sustainability are manifold, ranging from real-time status monitoring and nuanced interpretation of urban phenomena to the holistic evaluation of decisions made for urban sustainability. To verify the effectiveness and efficiency of the proposed approach, the paper introduces a novel cross-modality contrastive learning framework that incorporates semantic knowledge for urban sustainability. The proposed framework includes a semantic encoder and a visual encoder to capture information from UKG and urban images (satellite images and street view images), respectively. Based on the assumption that the semantic representation of UKG entities should be close to their corresponding image representations, the proposed framework successfully incorporate semantic knowledge into visual encoder, which further enhances the predictive capabilities of urban socioeconomic indicators derived from urban images. Through empirical validation, this study demonstrates the real-world applicability and generalizability of the UKG framework for urban sustainability.

Geological and mineral resource survey and scientific research in "geology, geophysics, geochemistry, and remote sensing " have established a large amount of geological and mineral survey data, which contain rich knowledge related to mineralization and distribution of gold mine, such as the metallogenic and tectonic setting, geological environment of occurrence, geological characteristics of mineral mine, genesis and metallogenic model of mine, and so on. The transformation from massive mineral related data to effective metallogenic knowledge has become one of the most important breakthroughs to improve the accuracy of geological prospecting. To solve this problem, through the in-depth analysis of knowledge representation, information extraction, and knowledge fusion in knowledge engineering, this paper explores the knowledge graph construction method of gold mine based on ontology. Firstly, referring to industry norms, gold mine knowledge base, and reference material of geological and mineral resource exploration, the metallogenic model of gold mine is sorted out, and the gold mine concept, gold mine entity, gold mine relationship, gold mine geological attribute, and gold mine metallogenic attribute are determined. In addition, the schema layer of gold mine knowledge graph is constructed by using the top-down ontology knowledge representation method, which represents the conceptual model and logical basis of gold mine knowledge graph. Secondly, based on structured, semi-structured, and unstructured multi-source heterogeneous geological data, the deep learning model is used to realize gold mine knowledge extraction, semantic analysis, and knowledge fusion, which enriches the data layer of gold mine knowledge graph and provides data support for gold mine knowledge graph. The gold mine knowledge graph is constructed in a bottom-up way, and the gold mine knowledge triplet is stored by Neo4j graph database, in which nodes represent gold mine concept, gold mine entity, and gold mine attribute value, while edges represent relation and attribute. Finally, the gold mine knowledge management system is developed based on the graph database. It can be applied to the management of gold mine data, acquisition of knowledge, visualization representation of gold mine knowledge graph, inquiry of knowledge, management and presentation of knowledge base, and other functions well, so as to lay a foundation for the intelligent analysis and mining of geological big data. This study develops a geological prospecting method driven by data and knowledge, and provides a reference for identifying, controlling, and managing mineral resources, which can improve the prospecting accuracy in geological exploration.

The continuous generalization of geographic information poses a huge challenge to the classic geographic information analysis modes. Networked knowledge services will gradually become a new mode for geographic information applications, facilitating to transform the form of geographic computing into social computing. Geographic knowledge services need to connect people, institutions, natural environments, geographical entities, geographical units and social events, so as to promote knowledge assisted data intelligence and computational intelligence. Facing the urgent need for spatiotemporal knowledge acquisition, formal expression and analysis, this paper firstly introduces the concepts and characteristics of spatiotemporal knowledge graph. The spatiotemporal knowledge graph is a directed graph composed of geographic spatiotemporal distribution or geo-locational metaphors of knowledge that is a knowledge graph centered on spatiotemporal distribution characteristics. Secondly we proposes a research framework for spatiotemporal knowledge graph. The framework includes various levels from multimodal spatiotemporal big data to spatiotemporal knowledge services that contain ubiquitous spatiotemporal big data layer, spatiotemporal knowledge acquisition technique layer, spatiotemporal knowledge management layer, spatiotemporal knowledge graph layer, software/tools layer, and industrial application layer. Thirdly this paper introduces relevant research progress from text implied geographic information retrieval, heterogeneous geographic semantic web alignment, spatiotemporal knowledge formalization and representation learning. Combined with application practice, we then enumerate the construction and application approaches of domain oriented spatiotemporal knowledge graph. Finally, it discusses the key scientific issues and technical bottlenecks currently faced in the research of spatiotemporal knowledge graph. It is argued that in the era of large models, constructing explicit spatiotemporal knowledge graph and conducting knowledge reasoning to meet domain needs is still the only way for spatiotemporal knowledge services.

Named Entity Recognition (NER) is the basis of many researches in natural language processing. NER can be defined as a classification task. The aim of NER is to locate named entities from unstructured texts and classify them into different predefined categories. Compared with English, Chinese have the features of flexible formation and no exact boundaries. Because of the features of Chinese and the lack of high-quality Chinese named entity datasets, the recognition of Chinese named entities is more difficult than English named entities. Fine-grained entities are subdivisions of coarse-grained entities. The recognition of Chinese fine-grained named entities especially Chinese fine-grained geographic entities is even more difficult than that of Chinese named entities. It is a great hardship for Chinese geographic entity recognition to take both accuracy and recall rate into account. Therefore, improving the performance of Chinese fine-grained geographic entities recognition is quite necessary for us. In this paper we proposed two Chinese fine-grained geographic entity recognition models. These two models are based on joint lexical enhancement. Firstly, we injected the vocabulary into the experimental models. The vocabulary was considered as the 'knowledge' in the models. Then we explored the appropriate fine-grained named entity recognition method based on vocabulary enhancement. And we found two models, BERT-FLAT and LEBERT, that were suitable for fine-grained named entity recognition. Secondly, to further improve the performance of these two models in fine-grained geographical named entities recognition, we improved the above two models with lexical enhancement function in three aspects: pre-training model, adversarial training, and stochastic weight averaging. with these improvements, we developed two joint lexical enhancement models: RoBERTa-wwm-FLAT and LE-RoBERTta-wwm. Finally, we conducted an ablation experiment using these two joint lexical enhancement models. We explored the impacts of different improvement strategies on geographic entity recognition. The experiments based on the CLUENER dataset and one microblog dataset show that, firstly, compared with the models without lexical enhancement function, the models with lexical enhancement function have better performance on fine-grained named entities recognition, and the F1-score was improved by about 10%; Secondly, with the improvements of pre-training model, adversarial training, and stochastic weight averaging, the F1-score of the fine-grained geographic entity recognition task was improved by 0.36%~2.35%; Thirdly, compared with adversarial training and stochastic weight averaging, the pre-trained model had the greatest impact on the recognition accuracy of geographic entities.

Web texts are an important data source for constructing and completing a large-scale knowledge graph that contains a great deal of ubiquitous geographic information. However, the extensive sources, casual expression, and dynamic nature of web texts, as well as the varied quality of implicit geo-information bring great challenges such as multi-level evaluation objects, unclear quality dimensions, diversified evaluation indicators, difficult access to deep-seated indicators, and diversified evaluation methods in the process of geographic information quality assessment. Therefore, a Quality Assessment Framework for implicit Geographic Information from Web Texts (QAF-GIWT) is proposed in this study. The QAF-GIWT is oriented to the process of acquiring geographic information from web texts and defines three levels of quality evaluation objects, i.e., data source level, data item level, and dataset level. The data source level contains websites and web pages, the data item level includes the triplet-formed information extracted from the webpage, and the dataset level is the information aggregated into a Geographic Knowledge Graph (GeoKG). The QAF-GIWT defines four quality dimensions including relevance, novelty, reliability, and integrity, and proposes the corresponding quantitative evaluation indicators for different level evaluation objects including Cell Geographic Semantic Ratio (CGSR), Geographic Semantic Ratio (GSR), Average Geographic Information Ratio (AGIR), Geographic Information Ratio(GIR), Event Time Length, Triplet Existence, Publish Time, Time Validation, Domain Name Time Length, Update Frequency, Average Freshness, Comprehensive Ranking, Category Ranking, Daily Page Visit, Daily User Visit, User Attention, Picture Number, Word Number, Geographic Entities Ratio (GER), Window's Geo-Information Ratio (GIWR), Triplet Missing Rate, Event Information Missing Rate, Relation Missing Rate, Attribute Missing Rate, Location Missing Rate, Relation Redundancy, Attribute Redundancy, etc. It systematically summarizes the characteristics and applicability of the indicator calculation, indicator synthesis, and quality prediction methods involved in the quality evaluation process. Among them, with the help of natural language processing technology and corresponding quality indicator calculation methods, quality indicators are newly constructed from the deep mining of the web texts including CGSR, GSR, AVGIR, GIR, GIWR, GER, etc. In our experiment, the QAF-GIWT framework was designed to adapt to the characteristics of various types of websites e.g., Mafengwo. Aiming at the comprehensive evaluation of multi-level quality indicators, the analytic hierarchy process was used for comprehensive reliability evaluation. Our experiment verified the effectiveness of the QAF-GIWT framework. The QAF-GIWT provides a systematic scheme including quality dimensions, quality indicators, and quality assessment methods for the quality evaluation of geographic information extracted from massive, heterogeneous, and dynamic web texts. The proposed QAF-GIWT can assist in the screening of data sources and filtering of acquired information, greatly reducing the complexity of information acquisition and the redundancy of data storage, and assisting the quality control process of the acquisition of geographic information from web texts.

Location description is the natural language expression of human spatial cognition. Since natural language is the primary and basic means of information transmission in human society, location description is an important medium for transmitting spatial location information in human communication. Spatial positioning based on spatial location description is the key to intelligent transformation of location-based services in the era of big data. To solve the problem that the vagueness of location description in different contexts is significantly different and results in difficulty in positioning, this paper proposes a representation method and reasoning mechanism for vague location description. Firstly, by combing the law of human spatial cognition, the types of elements concerned in the description of natural language are clarified. Based on the analysis of the sources of vagueness, a formal representation of vague location description is constructed. Different from the traditional spatial information modeling which focuses on spatial relationship, the formal representation proposed in this paper establishes the vagueness relation and influence among different information factors by the strategy of multi-factors representation. The formal representation also enhances the semantic analysis ability for the vagueness of location description. Secondly, based on supervaluation theory, the reasoning mechanism of vague location description is proposed from three aspects: spatial object, distance relation, and direction relation. Considering the context semantics of spatial location description, the threshold of observation value is used to carry out spatial reasoning. By being super-valued to different contexts, the reasoning results in different situations are obtained. The aim of the reasoning mechanism is to establish the mapping relationship between vague location description and real spatial location. Thirdly, a Question-Answering (Q&A) system is designed to collect contexts of location description, and a case study on the method is conducted. In the case study, a group of users' viewpoints from Q&A on spatial cognition are transformed into the spatial scope in the real world. These spatial scopes can establish the relationship between qualitative spatial concepts and quantitative spatial data, so as to realize the representation of vague location description in GIS. The results show that the proposed method in this paper can adjust the granularity of formal representation of location description in time according to actual application scenarios, and the spatial reasoning results fit intuitive cognition. In the future, knowledge graphs will be introduced to further improve the semantic reasoning ability and positioning accuracy for vague location description.

The new combat style places new requirements for battlefield environment service support. The intelligent service of battlefield environment urgently needs to improve knowledge based on the global multidimensional battlefield environment data. In view of the knowledge modeling problem of intelligent cognition of battlefield environment, this paper puts forward the classification method of battlefield environment knowledge and considers the battlefield environment knowledge graph as a new form of battlefield environment knowledge representation under the context of big data and artificial intelligence. To solve the fragmentation problem of triplet knowledge representation, a temporal hypergraph representation model of battlefield environment is constructed, a multi-level unified graph model combining entity knowledge, event knowledge, influence process knowledge, and service decision-making knowledge is realized, and all kinds of knowledge are represented as a unified knowledge hypergraph network with spatiotemporal and scene characteristics. Finally, the experimental verification is carried out based on the data of map, event, impact process, and combat impact effectiveness. The hypergraph network realizes the correlation of various battlefield environment knowledge from the semantic level, which can provide support for the further realization of intelligent reasoning and service decision-making based on hypergraph.

Knowledge graphs are an important data infrastructure in AI technologies and applications, and have become a hot research topic in geosciences. The size and topological features in geographic knowledge graphs are usually different from universal knowledge graphs, which are not typical small-world networks. However, existing studies often use the default network search depth when learning geographic knowledge graph representations, and its rationality needs further demonstration. For this purpose, this paper constructs a metro travel knowledge graph based on the topological structure features of metro line network, combined with passenger flow data, POI (Point of Interest) data and built environment data, etc.; then GraphSAGE model is used to learn node multidimensional feature embedding and combine POI data for semantic recognition of station classification results to verify the suitable network search depth for metro travel knowledge graph. The results showed that, compared to the default 2 layers search depth, the node embedding features of this metro travel knowledge graph work optimally when the search depth is 3 layers. This study shows that the hyperparameter selection of the geographic knowledge graph representation is supposed to take into account the geographic features, and it is important to avoid the use of results from fields such as computer science that have not been distinguished. When the search depth is 3 layers, the metro station classification results are also more reasonable and explanatory, which can provide a basis for station planning and passenger flow prediction using knowledge graph and AI methods.

Natural language is an effective tool for humans to describe things, with diversity and ease of dissemination, and can contain human spatial cognitive results. How to use natural language to describe geographic spatial scenes has always been an important research direction in spatial cognition and geographic information science, providing important application values in personalized unmanned tour guides, blind navigation, virtual space scene interpretation, and so on. The essence of natural language description of geographic spatial scenes is the process of transforming the two-dimensional vector of geographic space into a one-dimensional vector in word space. Traditional models perform well in handling spatial relationships, but are somewhat inadequate in natural language description: (1) spatial relationship description models are one-way descriptions of the environment by humans, without considering the impact of the environment on the description; (2) spatial scenes emphasize traversal-based descriptions of spatial relationships, where each set of spatial relationships is equally weighted, which is inconsistent with the varying attention paid by humans to geographic entities and spatial relationships in the environment; (3) the spatial relationship calculation of traditional models is a static description of a single scene, which is difficult to meet the requirement of dynamic description of continuous scenes in practical applications; (4) the natural language style of traditional models is mechanical, lacking necessary knowledge support. This article proposes a spatial scene natural language generation framework Map2Text (M2T) that integrates multiple knowledge graphs. The framework establishes knowledge graphs for spatial relationships, language generation style, and spatial attention, respectively, and realizes the fusion of multiple knowledge graphs and the generation of natural language descriptions of spatial scenes within a unified framework. The spatial scene description knowledge graph solves the pruning problem of traversing spatial relationships, and establishes the relationship between spatial scenes by building a spatial relationship graph, supporting continuous expression of spatial scenes; the natural language style knowledge graph establishes the relationship between spatial expression and language style, achieving diversified language styles that are appropriate for spatial natural language expression; the spatial attention knowledge graph captures the nuances of natural language spatial expression by establishing an attention matrix based on the interaction state between the subject and object of the spatial scene. An experimental prototype system designed based on the Beijing Forbidden City demonstrates that the system-generated results are close to human travel notes, with more complete content coverage and more diverse styles, verifying the effectiveness of the M2T framework and demonstrating the potential value of natural language description of spatial scenes.

Mainstream map retrieval methods for spatial data infrastructures are mainly based on metadata text matching or image similarity calculation, but such approaches lack active perception and understanding of user retrieval intention, and in turn fail to truly meet user requirements. While existing intention recognition methods are incapable to express and recognize map retrieval demands with joint constraints of complex geographic concepts. To address this issue, this paper proposes a map retrieval intention formalization and recognition method by considering geographic semantics, aiming to improve the accuracy of map retrieval in an intention-driven and explainable manner by using relevance feedback samples. More specifically, a formalization model constrained by geographic ontology in the form of "intention-sub-intention-dimension component" is designed for expressing user's map retrieval intention. With the support of the formalization model, a recognition algorithm based on Minimum Description Length (MDL) principle and Random Merging (RM) strategy, named MDL-RM, is proposed by treating intention recognition as a combinational optimization problem. MDL-RM takes the description length of the sample set from relevance feedback as the optimization goal, merges samples randomly with the assistance of geographic ontologies and semantic similarities among geographic terminologies to generate sub-intention candidates, and searches the optimal intention using a greedy search approach. In order to evaluate the accuracy of recognized intention, we proposed a semantic metric, named Best Map Average Semantic Similarity (BMASS), and calculated it along with Jaccard index in five typical map retrieval scenes. Meanwhile, we analyzed the time cost and the influence of parameter settings and validated the effectiveness of random merge and sample augmentation strategy. The experimental results on the synthetic data demonstrate that the proposed method has higher accuracy and sample noise tolerance in most retrieval scenes comparing with the method based on Gene Ontology (RuleGO) and the Decision Tree learning method with Hierarchical Features (DTHF). The random merge strategy can reduce average computing time effectively without declining accuracy, and the sample augmentation strategy facilitates retrieval intention recognition even when the sample size is as low as 20. The proposed method is expected to be adapted and applied into geoportals and catalogue services to improve the service quality and user experiences upon the sharing and discovery of geographic information resources.

The graph structure-based knowledge graph plays important roles not only in the description and reasoning of semantic network, but also in the structured abstraction and spatial reasoning of spatial entities. The relational information of spatial entities is recorded in edges in the knowledge graph. Through the edge-based knowledge graph computational reasoning such as path detection, sub graph alignment, pattern discovery, etc., it can play an important role in spatial scene cognition. Geographic knowledge graph is a knowledge system that formally describes geographic concepts, entities, and their interrelationships. It has both the connotation and characteristics of general knowledge and the specific spatiotemporal characteristics of geographic knowledge. It can connect semantic models with spatiotemporal models to describe semantic relations, spatial relations, and temporal relations, and has great application potential in the expression, understanding, acquisition, and reasoning of geographic knowledge. The existing research work of geographic knowledge graph is mostly focused on semantics, and the extraction and expression of semantic relations are very rich and comprehensive, which can support further functions such as semantic search and association analysis of geographic knowledge. However, the knowledge expression of geographic knowledge graph in spatiotemporal model is relatively lacking, and the existing spatial relationship is limited between elements, rarely involving the further distribution situation and spatial pattern in spatial cognition. Thus, the geographic knowledge graph needs to be strengthened in terms of spatial semantic knowledge. Based on the principle of knowledge graph construction, this paper takes the construction of geographic knowledge graph of buildings as an example to realize the grid-pattern recognition of buildings. Firstly, the buildings are abstracted into entities and expressed as nodes of the graph, and the spatial neighborhood relations between buildings is extracted based on geometric proximity analysis, so as to build the geographic knowledge graph of the building group. On this basis, combined with the domain knowledge of building pattern recognition, it further infers and constructs other spatial semantic relations, and improves the geographic knowledge graph. Then the grid-pattern of the buildings complex scene is expressed as the rules of the knowledge graph, which is based on NoSQL language for reasoning. The results show that this method can effectively extract the linear pattern of buildings and further deduce the grid-pattern, which demonstrates the important role of geographic knowledge graph in spatial reasoning and its good adaptability in domain problem research, and provides ideas for the application of geographic knowledge graph in the field of spatial cognition.

Geoscience Knowledge Graph (GKG) has strong capabilities of knowledge representation and semantic reasoning, thereby becoming a required infrastructure for the development of geoscience big data and geoscience artificial intelligence. However, existing studies on GKG were mainly conducted under the experimental scenarios. Because of a lack of research on the general framework of construction methods, sharing, and application of large-scale GKG for practical applications, it has not been used in practical applications in the geoscience field. For this reason, towards the needs of research and applications of geoscience big data and artificial intelligence for GKG, this paper first studied the construction techniques of large-scale GKG. Then, a general framework for covering the lifecycle of GKG including its construction, sharing, and application was proposed. Taking the big science program “Deep-Time Digital Earth (DDE)” as an example, the practice of developing GKG platform towards the practical application of DDE was carried out. Using this platform, this paper realized the construction of DDE large-scale GKG, the open sharing and application of built GKG, proving that the proposed framework can effectively support the construction, sharing, and application of large-scale GKG. This paper plays an important role in promoting the realization of the practical application value of GKG.

China is one of the countries that are most severely affected by typhoons. The direct economic losses caused by typhoons amount to more than 10 billion yuan, and the affected population is more than one million each year in China. Typhoon disasters seriously threaten the social economy and natural environment of coastal areas. As a result, there is an immediate necessity to improve disaster emergency management and comprehensive disaster prevention and mitigation. With the concept of knowledge graph proposed by Google in 2012, it has gradually become a research hotspot in the field of artificial intelligence and played a role in applications such as information retrieval, question answering, and decision analysis. The information integration and representation capability of the knowledge graphs can provide effective support for dynamic monitoring and management decisions of typhoon disaster events. There are problems with the current typhoon disaster models in the representation of spatiotemporal processes. Most disaster knowledge graphs are analyzed for single elements of disaster events, and the research on ontological representation and analysis of disaster development process is still lacking. Firstly, we propose a typhoon disaster events knowledge representation model established from five levels: concept, object, state, characteristic, and relationship, by analyzing the components and dynamic characteristics from the typhoon disaster mechanism. Second, this model considers the multi-granularity of typhoon event information, and unifies the different feature information into each object. Besides, in order to highlight the evolutionary characteristics of typhoon events, the state is taken as the cross-section of the process in a specific spatiotemporal feature. The state is an action or record of an object occurring in time and space. The process is the procedure that properties, forms, and patterns undergo as it gradually changes over time. Finally, we construct a knowledge graph of typhoon disaster events using the 2021 severe typhoon In-Fa as a case study. The results show that the model not only highlights the characteristics of different objects portrayed in the event, but also expresses the spatiotemporal processes of typhoon disaster events through the state sequences of multiple objects. The knowledge graph can be applied as a primary knowledge source in the emergency decision management of typhoon disaster events, which can undoubtedly enable relevant decision-makers to better perceive the spatial and temporal development of typhoon disaster situations. In disaster assessment, based on the rich contents covered in the knowledge graph, it can cope with assessing various aspects such as human casualties, economic losses, and secondary disasters.

Wetland is of great significance to biodiversity and climate change, and it is also one of the basic living environments of human beings. In order to better understand and express wetland knowledge and the relationship between classifications, this paper proposes an ontology-based wetland knowledge graph construction method. Based on the land cover classification system of GlobeLand 30, this paper establishes the conceptual structure of wetland data and the rich semantic relationship between the elements around wetland type definition, spatial pattern, case distribution, and trend change. Firstly, based on the prior knowledge of wetlands, taking the wetland types in the GlobeLand 30 classification system as an example, we analyze the wetland domain knowledge around the wetland types, feature distribution, and other elements, extract the semantic relationship between knowledge, and construct the ontology database of wetland knowledge by combining top-down and bottom-up methods. The conceptual framework of wetland knowledge graph is formed through ontology modeling. Secondly, based on the wetland knowledge automatically extracted from the technical specification text and encyclopedia website, the extracted conceptual knowledge is stored in the model layer, and the data layer is constructed from bottom to top. The main contents include knowledge acquisition and knowledge fusion. According to the concepts contained in wetland knowledge, the relationship extraction of wetland knowledge is carried out, mainly including attribute relationship, spatial relationship, and temporal relationship. Using the wetland directory crawled from the wetland China website, the wetland entity name and knowledge are directly extracted from Baidu Encyclopedia by means of web crawler to form a triple. Finally, Through the above construction processes of wetland knowledge graph, the wetland related data with different structures are transformed into structured knowledge triple data, and the graph database Neo4j is used for semantic relationship storage with the "node relationship" storage model. Knowledge graph provides a new idea for the study of rich knowledge representation and storage in the field of land cover. It is a bridge between the basic geographic data of surface coverage and spatial knowledge service. It is of great significance to promote the sharing and reasoning analysis of surface coverage data. Taking the wetland land cover type as the research example, the knowledge graph constructed in this paper expands the conceptual description information of wetland entities, explores the wetland knowledge representation method by considering the temporal and spatial characteristics, and provides a new perspective and application demonstration for the expression of land cover knowledge.

With the deepening of global economic integration, maritime traffic congestion and ship accidents occur frequently. In order to supervise and analyze the marine ship activities, the traditional methods mainly use the ship positioning data for data mining without combining other marine multi-source data for the analysis of ship spatiotemporal activity process and behavior pattern, and thus lack deep knowledge mining. Therefore, this paper makes comprehensive use of multi-source data and constructs the ship activity knowledge map based on extracting the semantic information of trajectory, which provides an effective way for the transformation of trajectory spatiotemporal point sequence with low knowledge density to high-order semantic knowledge. Specifically, firstly, by analyzing the characteristics and constituent elements of ship activities, the ontology layer of ship activity knowledge map is designed based on the core idea of "process-event-behavior"; Then, the track semantic information is extracted by Stop/Move model, and the ship emergencies are extracted by DMCNN model to complete the filling of instance layer; Finally, the above model and method are verified by constructing a prototype system. The results show that the ship activity knowledge map constructed in this paper can support the knowledge representation of ship routine activities and emergencies, and realize spatiotemporal activity query and backtracking, so as to achieve the effect of semantic enhancement, which has a certain application value.

Due to the temporal and spatial heterogeneity of the complex earth's surface, the traditional idea of developing new intelligent interpretation algorithms to solve the remote sensing geoscience cognition based on the features of remote sensing images has hit the bottleneck in terms of accuracy and geographic usage when analyzing remote sensing big data. To overcome the bottleneck, we proposed the Geographic Knowledge Graph (GKG) that based on the geographic knowledge to analyze the remote sensing big data, which is inspired by the recently proposed Knowledge Graph from the geographic perspective. It expands the concept of the geographic knowledge and classifies the geographic knowledge into three levels: Data knowledge, conception knowledge, and regularity knowledge. Then, it represents and connects all geographic knowledge in Graph by nodes and edges and realizes the feedback iteration and update between different levels of the geographic knowledge. This representation enables GKG to perform well at knowledge inquiring, reasoning, calibration, and expanding. How to construct multiscale high-dimension geo-entities and how to connect different levels of the geographic knowledge with heterogeneous features are two key technologies. These functions make GKG promising in refining existing geographic knowledge in the era of remote sensing big data, promoting remote sensing interpretation accuracy and geographic usage, and promoting the development of geoscience.

Knowledge graph is widely applied in the field of artificial intelligence. Fusing multisource geospatial data is a hot topic for the transformation of “data-knowledge”. However, the general knowledge graph has low spatial knowledge and some of them is incorrect. Moreover, geographic knowledge graph from Wikipedia has some problems such as missing spatial relation, Chinese attribute, and exact coordinates information. In this paper, we analyze the characteristics of geospatial data and baidubaike.In addition, we propose a knowledge graph construction method based on geographic entities which are extracted from geospatial data and supplemented by attribute information from baidubaike.At the end, the scale of knowledge graph is analyzed in terms of entities and relations. The experiment proves that the conceptual description information of geographic entities is expanded, and there is a higher success rate of linking web page with geographic entities than ever. In addition, the coverage of geographic coordinates is increased to 100%. The knowledge graph constructed in this paper will have an important significance to extend geospatialdata to knowledge.

Geo-information Tupu is an important theoretical exploration field advocated by academician Chen Shupeng in the late of 1990s. Tupu is a Chinese word in Pinyin. It could be understood as map series, graph-spectrum, including the meaning of map, chart, graph, spectrum, plan etc., but we could not find a proper term for it in terminology yet. According to Chen's related literature, this paper discusses the connotation of his thought of geo-information Tupu, and holds that Tupu is the methodology of expression and analysis adopted in many fields, while geo-science tupu is a map series of geo-phenomena and processes in the field of geo-science. Geo-information tupu emphasizes quantitative geo-information (geo-database) and geo-computation, supported by geographic information system, it is expected to use geo-graphic language to visualize and analyze geographical phenomena in space and time, and to simulate the law of relationships between man and environment, reconstructing its past, evaluating its present situation and even predicting its future. The idea of geo-information Tupu is closely related to academician Chen's scientific career. Under the guidance of many famous Chinese profesosors, he laid a solid geographical foundation in his youth. In the early stage of career, he participated in a large number of practice of integrated geography and map design. These experiences promoted him to combine the comprehensive thinking of geography with the graphic representation. The development of remote sensing and GIS expanded his vision on geo-technology. In his later years, he put his passion and led China's development in these fields. Geo-information Tupu was developed, which was a collection of rich theories, methods and technical achievements accumulated in the study of geographical science all his life. Geo-information Tupu is a scientific problem that academician Chen Shupeng left to the later generations. Therefore, the authors puts forward some further reserch directions according to the newest related study progress in order to inherit Chen's scientific question. The development of Holographic map is an important basis for the visualization of geo-information Tupu. It is helpful to discover new geographical phenomena and new geographical laws. The "spectrum" detection of land surface information by remote sensing should be supported by the thought of "map" analysis in geography. It fanally come back to answer the core questions of geography, such as the distribution of complex surface and its mechanism. The cognition of geo-information Tupu takes the relationship between man and environment as the center to construct a new cognition theory from real to vitual geographical space. The goal of geo-information Tupu is to assist in the discovery and utilization of new geo-knowledge, and to lay a theoretical and methodological foundation for realizing automatic and intelligent geo-knowledge Tupu.

The idea of geo-information Tupu is reviewed and summarized by reading and analyzing the theses published by Academician of Chinese Academy of Sciences, Prof. Chen Shupeng in this paper, which come from his scientific probe into geo-system. Geo-information Tupu is the result of combination of Geo-Tupu and information technology, which aims at discovering spatial and temporal knowledge and rule of geo-science, and can provide service to social and economic construction. Geo-information Tupu is classified into different types for each branch of geo-science and each industry according to its research object, and it is also classified into symptom Tupu, diagnosisTupu and realization Tupu according to its function. Prof. Chen developed the technology method for generating geo-information Tupu, proposed the workflow of geo-information Tupu, promoted the application of geo-information Tupu to the economic and social development. Prof. Chen indicated innovation directions of geo-information Tupu development which include innovation of technology and method of obtaining geo-science big data, improvement of intelligent and automatic level of generating geo-information Tupu, and enhancement of the wide and deep application of geo-information Tupu in social and economic construction. Prof. Chen pointed out grid map is important base of geo-information Tupu. The idea of geo-information Tupu proposed by Prof. Chen still has important guidance value in promoting the advance of geo-information science and meeting the major strategic needs of our country. The exploratory practice and results under the guidance of the idea of geo-information Tupu proposed by Prof. Chen were concluded here. The thematic information extraction technology based on knowledge discovery and the fine geo-surface element acquisition technology based on multi-data and multi-knowledge cooperation have been developed in recent years, which improves the automation level and efficiency of obtaining geo-science elements such as waterbody, settlements, green vegetation cover, snow cover, vegetation and landuse types in detail. The study and application of spatio-temporal change information Tupu have been broaden. The intelligent level and efficiency of generating the spatio-temporal change Tupu have been improved. The application of Geo-information Tupuin in social and economic development, ecological construction, environmental protection and disaster risk prevention and control has shown great prospects in recent years. Finally, in order to meet the major strategic needs of jointly building a global community with a shared future for mankind and the internationalization of geo-science in the new era, this paper puts forward the research on the key technologies and its applications of the global geo-science information Tupu, so that the large knowledge are intelligently and automatically discovered from big data of the geo-system through big observation, and the large knowledge is broadly applied to every aspect and every place through internet, which will support the modernization of the governance system and ability of the geo-surface system, and enhance the level and ability of geo-sciences to serve economic and social development, and highlight the characteristics and advantages of the geoscience.