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.