Geographic entity knowledge graph generation method taking spatio-temporal characteristics into consideration and related device

Abstract

The invention discloses a geographic entity knowledge graph generation method considering spatio-temporal characteristics and a related device, and relates to the technical field of knowledge engineering.According to the method, a multi-layer geographic entity knowledge graph structure model comprising a concept layer, an entity layer and a relation layer is established, and a'code-scale-entity-relation 'knowledge representation method is integrated in the model; a unique spatial identity code of a geographic entity is introduced to support invariable identification of any geographic entity under scale or time sequence change; by expanding the same geographic entity node into geographic entity sub-nodes in different states, formalized description of the geographic entity scale and process features is ingeniously realized, and specific spatial-temporal features and geoscience mechanisms of geographic entity knowledge can be reflected.

Description

Technical Field

[0001] The present application relates to the field of knowledge engineering technology, and in particular to a method and related device for generating a geographic entity knowledge graph taking into account temporal and spatial characteristics. Background Art

[0002] A geographic entity is a geographic object in the real world that occupies a certain and continuous spatial position and range and has the same attributes or complete functions. Geographic entity data is a digital description of geographic entities in computers. It is an important type of spatiotemporal information data that provides a unified positioning framework and analysis basis for other data. With the rapid development of new technologies such as cloud computing, the Internet of Things, big data, information and communication technology (ICT) and artificial intelligence (AI), it is difficult to meet the needs of technological development and practical applications to intuitively or abstractly reflect geographic entities in the form of spatial data (such as vector data and raster data). Constructing semantic models of geographic entities, realizing the formal expression of geographic entity knowledge, and promoting the "human-computer compatible understanding" of geographic entities have become hot topics in the field of spatiotemporal information science.

[0003] Information representation is to describe information in a certain form using computer-acceptable symbols. Knowledge graph is a knowledge base system based on semantic network, which represents knowledge definition (schema) and knowledge instance (instance) in a unified triple form <node 1, relationship, node 2>, making knowledge acquisition, knowledge fusion and knowledge reasoning have significant advantages in operability and computability. However, in addition to the connotation and characteristics of general knowledge, geographic entity knowledge also has specific spatiotemporal characteristics and geoscience mechanism characteristics. The existing knowledge formalization expression method with "entity-relationship" as the basic structure cannot reflect the specific spatiotemporal characteristics and geoscience mechanism of geographic entity knowledge. Summary of the invention

[0004] The purpose of this application is to provide a method and related device for generating a geographic entity knowledge graph that takes into account both temporal and spatial characteristics, which can reflect the specific temporal and spatial characteristics and geological mechanisms of geographic entity knowledge.

[0005] To achieve the above objectives, this application provides the following solutions.

[0006] In a first aspect, the present application provides a method for generating a geographic entity knowledge graph taking into account temporal and spatial characteristics, comprising the following steps.

[0007] Build a multi-level geographical entity knowledge graph structure model, where the multi-level geographical entity knowledge graph structure model includes a concept layer, an entity layer, and a relationship layer. The concept layer includes several concepts, which are used to describe the geometric and semantic features of geographical entities. The entity layer includes several instantiated entity groups, and each instantiated entity group includes an instantiated main entity, an instantiated sub-entity, and entity attributes. The instantiated main entity is a specific manifestation of the concept, and the instantiated sub-entity is a geographical entity obtained by expanding the instantiated main geography based on scale features. The entity attributes include a spatial identity code and scale features. The relationship layer is used to describe relationship types, and the relationship types include relationships between concepts, between concepts and entities, and between entities. The entities include instantiated main entities and instantiated sub-entities.

[0008] Generate a geographical entity knowledge graph according to the multi-level geographical entity knowledge graph structure model.

[0009] In a second aspect, the present application provides a geographical entity knowledge graph generation device that takes into account spatio-temporal features, including.

[0010] A model construction module for building a multi-level geographical entity knowledge graph structure model, where the multi-level geographical entity knowledge graph structure model includes a concept layer, an entity layer, and a relationship layer. The concept layer includes several concepts, which are used to describe the geometric and semantic features of geographical entities. The entity layer includes several instantiated entity groups, and each instantiated entity group includes an instantiated main entity, an instantiated sub-entity, and entity attributes. The instantiated main entity is a specific manifestation of the concept, and the instantiated sub-entity is a geographical entity obtained by expanding the instantiated main geography based on scale features. The entity attributes include a spatial identity code and scale features. The relationship layer is used to describe relationship types, and the relationship types include relationships between concepts, between concepts and entities, and between entities. The entities include instantiated main entities and instantiated sub-entities.

[0011] A knowledge graph generation module for generating a geographical entity knowledge graph according to the multi-level geographical entity knowledge graph structure model.

[0012] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the method for generating a geographical entity knowledge graph that takes into account spatio-temporal features described in the first aspect above.

[0013] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the method for generating a geographical entity knowledge graph that takes into account spatio-temporal features described in the first aspect above.

[0014] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for generating a geographic entity knowledge graph that takes into account temporal and spatial characteristics as described in the first aspect above.

[0015] According to the specific embodiments provided in this application, the technical effects of this application are as follows.

[0016] The present application provides a method and related device for generating a geographic entity knowledge graph that takes into account spatiotemporal characteristics. The method establishes a multi-level geographic entity knowledge graph structure model including a concept layer, an entity layer, and a relationship layer. The model incorporates the knowledge representation method of "coding-scale-entity-relationship". By introducing a unique spatial identity code for geographic entities, it supports the invariant identification of any geographic entity under scale or temporal changes; by expanding the same geographic entity node into geographic entity sub-nodes in different states, it cleverly realizes the formal description of the scale and process characteristics of geographic entities, which can reflect the specific spatiotemporal characteristics and geological mechanisms of geographic entity knowledge. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 This is a schematic diagram of a sample of the traditional "entity-relationship" oriented knowledge graph in Example 1 of the present application.

[0019] Figure 2 A flowchart of a method for generating a geographic entity knowledge graph taking into account temporal and spatial characteristics provided in Example 1 of the present application.

[0020] Figure 3 This is a schematic diagram of the expression of the multi-level geographic entity knowledge expression model in Example 1 of the present application.

[0021] Figure 4 This is a schematic diagram of the unique spatial identity coding structure of the geographic entity in Example 1 of the present application.

[0022] Figure 5 This is a schematic diagram of the “scale-coding-entity-relationship” geographic entity knowledge graph in Example 1 of the present application.

[0023] Figure 6 A schematic diagram of the structure of a computer device provided in Example 3 of the present application. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0026] Example 1 The most basic semantic unit in the knowledge graph is represented by a triple of <node 1, relationship, node 2>, where a node can represent a concept, entity, or attribute. Figure 1 This is a typical example of a geographic entity knowledge graph, where <Road A, length, 234.1m> is a semantic unit, "Road A" and "234.1m" are two entities, and "length" represents the relationship between them. Each road entity also has attribute information such as name and mesh ID.

[0027] Obviously, this kind of knowledge graph expression has the following problems: First, there is a lack of modeling of scale characteristics. Human cognition of geographic entities has scale characteristics. Taking the road network as an example, at the macro scale, more attention is paid to the road network characteristics formed by trunk roads, and the relationship between roads is mainly concentrated on the trunk roads; at the micro scale, attention is paid to the road network characteristics composed of trunk roads, secondary trunk roads and branch roads, and there are relationships between roads of different levels. Second, there is a lack of modeling of process characteristics. In essence, knowledge is the subject's expression of the state of things and their laws of change. The existing knowledge graph structure can only express a static relationship between two entities, and cannot reflect the change process of geographic entities. <Road A, length, 234.1m> means that the length of road A at a certain moment is 234.1m. After the road is expanded or damaged, its length may change.

[0028] In view of the technical defect that the existing technology cannot reflect the specific spatiotemporal characteristics and geoscience mechanisms of geographic entity knowledge, this embodiment combines the spatiotemporal characteristics of geographic entity knowledge and the expression form of knowledge graph to provide a method for generating geographic entity knowledge graph that takes into account spatiotemporal characteristics, such as Figure 2 As shown, the following steps are included.

[0029] S1. Establish a multi-level geographic entity knowledge graph structure model, wherein the multi-level geographic entity knowledge graph structure model includes a concept layer, an entity layer and a relationship layer. The concept layer includes a number of concepts, and the concepts are used to describe the geometric features and semantic features of the land entity. The entity layer includes a number of instantiated entity groups, and the instantiated entity groups include instantiated main entities, instantiated sub-entities and entity attributes. The instantiated main entity is a concrete manifestation of the concept. The instantiated sub-entity is a geographic entity obtained by expanding the instantiated main geography based on scale characteristics. The entity attributes include spatial identity coding and scale characteristics. The relationship layer is used to describe relationship types, and the relationship types include relationships between concepts, between concepts and entities, and between entities. The entities include instantiated main entities and instantiated sub-entities.

[0030] S2, generating a geographic entity knowledge graph according to the multi-level geographic entity knowledge graph structure model.

[0031] S2-1, using triples to describe the multi-level geographic entity knowledge graph structure model, and establishing a directed graph consisting of "nodes-edges", wherein the nodes in the directed graph represent the concepts, entities, concept attributes and entity attributes, and the edges in the directed graph represent the relationship types.

[0032] S2-2, generating a geographic entity knowledge graph based on the directed graph.

[0033] In order to make the specific execution process of the above steps more clear to those skilled in the art, a detailed explanation is given below.

[0034] (1) Establish a geographic entity knowledge expression model covering the three levels of “concept-entity-relationship” to describe the basic components of geographic entity knowledge and its logical relationships. The specific steps are as follows.

[0035] A geographic entity knowledge expression model covering three levels of “concept-entity-relationship” is established to describe the basic composition of geographic entity knowledge and its logical relationship. Its structure is as follows: Figure 3 As shown in the figure, the concept layer includes the length, direction, level, name, etc. that describe the basic geometric and semantic features of geographic entity knowledge; the entity layer is an instance of the concept, containing specific entity attributes, such as road section 1, road section 2, etc., all belong to road entities; the relationship layer includes various relationships between concepts, between attributes, and between entities, such as concept subordination, attribute association, etc.

[0036] (2) Establish the spatial identity code of geographic entities based on the Beidou grid location code to achieve global unique identification of geographic entities. The specific steps are as follows.

[0037] Establish a unique spatial identity coding structure for geographic entities including "location code + classification code + sequence code" to globally uniquely identify any geographic entity, such as Figure 4 shown.

[0038] The location code is a 24-bit or 42-bit code. The specific encoding method is determined based on "GB / T 39409-2020 Beidou Grid Location Code", and the grid level is expanded according to the multi-granularity characteristics of geographic entities.

[0039] The classification code adopts a 6-digit code or a mixed code of numbers and letters. Geographical entities are divided into three categories: natural geographical entities (including mountains, water bodies, ice and snow, oceans, agricultural and forestry land and other land, etc.), artificial geographical entities (including water conservancy, transportation, buildings (structures) and facilities, pipelines, courtyards, etc.) and management geographical entities (including administrative division units, place names, national land space planning units, other management areas, other management entities, etc.). The first digit is the major category code, where 1 represents natural geographical entities, 2 represents artificial geographical entities, and 3 represents management geographical entities; the 2nd to 5th digits represent the subclass code, which can be further subdivided into primary, secondary and tertiary categories. For example, the code for river entities is 120200; the code for house entities is 230100. The specific coding method can be determined by referring to "GB / T 13923-2022 Basic Geographic Information Elements Classification and Code".

[0040] When the spatial grids and categories of geographic entities are exactly the same, sequence codes are used to distinguish different geographic entities. The sequence code is a 4-digit code with a value of 0000 to 9999. When the sequence code is set for the first time, the sequence codes of various geographic entities in the same spatial grid and the same category are coded sequentially starting from 0000. After the first coding, when new geographic entities appear in the same spatial grid and the same category, the codes should be appended in sequence after the largest sequence code.

[0041] (3) Drawing on the semantic network knowledge representation method adopted by the knowledge graph, on the basis of the existing "entity-relationship" knowledge expression method, a knowledge representation method that integrates "coding-scale-entity-relationship" is proposed, and data is organized in the form of triples to obtain a geographic entity knowledge graph, thereby realizing the structured organization and formal description of geographic entity knowledge, and laying a theoretical and methodological foundation for the generalization, application, sharing and coordination of knowledge.

[0042] Taking into account the unique scale and spatial characteristics of road selection knowledge, the general "entity-relationship" knowledge representation method is optimized, and a knowledge representation method integrating "coding-scale-entity-relationship" is proposed. Usually, scale characteristics are described in terms of scale, 1:500-1:2000 is microscale; 1:2000 (not included)-1:25000 is mesoscale; and 1:25000 and above is macroscale. The specific steps are as follows.

[0043] (3-1): The single node representing a geographic entity in the geographic entity knowledge expression model is expanded into multiple sub-nodes according to the scale characteristics, and the scale attribute values ​​are recorded in the sub-nodes, thereby realizing the formal description of the scale characteristics.

[0044] (3-2): Add a "coding" attribute to each entity to achieve the invariant identification of any geographic entity under changes in scale or time series.

[0045] (3-3): Enrich the spatial relationship types of the geographic entity knowledge expression model and represent them with edges between nodes, such as adding topological connection and topological separation relationships, thereby realizing the formal description of spatial features and obtaining a multi-level geographic entity knowledge graph structure.

[0046] (3-4): Use triples to describe various relationships in the multi-level geographic entity knowledge graph structure, establish a large-scale directed graph consisting of "points-edges", and form a geographic entity knowledge graph. The points represent geographic entity concepts, geographic entity instances, and attribute values, and the edges represent the relationships between concepts, the relationships between concepts and entities, and the relationships between entities, such as Figure 5 shown.

[0047] This embodiment combines the spatiotemporal characteristics of geographic knowledge and the expression form of knowledge graphs to propose a formal expression method for geographic entity knowledge that takes into account the spatiotemporal characteristics. The method involves: first, constructing a geographic entity knowledge expression model covering three levels of "concept-entity-relationship" to provide a basic framework for the formal expression of geographic entity knowledge; second, introducing a unique spatial identity code for geographic entities in the formal expression process to support the invariant identification of any geographic entity under scale or time series changes; third, establishing a knowledge representation method that integrates "coding-scale-entity-relationship" to cleverly achieve a formal description of the scale and process characteristics of geographic entities by expanding the same geographic entity node into geographic entity sub-nodes in different states.

[0048] Based on the geographic entity knowledge graph proposed in this embodiment, the following effects can be achieved.

[0049] 1. The integrated description of geographic entity geometry, attributes, relationships and other information provides a powerful tool for the sharing and interoperability of related knowledge.

[0050] 2. It can effectively describe and record the temporal and spatial evolution laws of geographic entities, such as recording the multiple states of geographic entities at different time phases, and recording the correlation between geographic entities under multi-scale transformation conditions.

[0051] 3. It can support the efficient integration, knowledge reasoning and feature mining of geographic entity spatiotemporal information.

[0052] Example 2 This embodiment provides a device for generating a geographic entity knowledge graph taking into account temporal and spatial characteristics, including:

[0053] A model building module is used to establish a multi-level geographic entity knowledge graph structure model, wherein the multi-level geographic entity knowledge graph structure model includes a concept layer, an entity layer and a relationship layer, the concept layer includes a number of concepts, the concepts are used to describe the geometric features and semantic features of the land entity, the entity layer includes a number of instantiated entity groups, the instantiated entity groups include instantiated main entities, instantiated sub-entities and entity attributes, the instantiated main entity is a concrete manifestation of the concept, the instantiated sub-entity is a geographic entity obtained by expanding the instantiated main geography based on scale characteristics, the entity attributes include spatial identity coding and scale characteristics, the relationship layer is used to describe the relationship type, the relationship type includes the relationship between concepts, between concepts and entities, and between entities, and the entities include instantiated main entities and instantiated sub-entities.

[0054] The knowledge graph generation module is used to generate a geographic entity knowledge graph based on the multi-level geographic entity knowledge graph structure model.

[0055] Example 3 This embodiment provides a computer device, which may be a server or a terminal. Its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data in the method for generating a geographic entity knowledge graph taking into account spatiotemporal features. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the method for generating a geographic entity knowledge graph taking into account spatiotemporal features described in Example 1 is implemented.

[0056] Those skilled in the art will understand that Figure 6The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0057] Example 4 This embodiment provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for generating a geographic entity knowledge graph taking into account both temporal and spatial characteristics as described in Embodiment 1 is implemented.

[0058] Example 5 This embodiment provides a computer program product, including a computer program, which, when executed by a processor, implements the method for generating a geographic entity knowledge graph taking into account both temporal and spatial characteristics as described in Embodiment 1.

[0059] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0060] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0061] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0062] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0063] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for generating a geographic entity knowledge graph taking into account temporal and spatial characteristics, characterized in that: The method for generating a geographic entity knowledge graph taking into account temporal and spatial characteristics includes: Establish a multi-level geographic entity knowledge graph structure model, wherein the multi-level geographic entity knowledge graph structure model includes a concept layer, an entity layer and a relationship layer, the concept layer includes a number of concepts, the concepts are used to describe the geometric features and semantic features of the land entity, the entity layer includes a number of instantiated entity groups, the instantiated entity groups include instantiated main entities, instantiated sub-entities and entity attributes, the instantiated main entity is a concrete manifestation of the concept, the instantiated sub-entity is a geographic entity obtained by extending the instantiated main geography based on scale features, the entity attributes include spatial identity coding and scale features, the relationship layer is used to describe the relationship type, the relationship type includes the relationship between concepts, between concepts and entities, and between entities, and the entity includes the instantiated main entity and the instantiated sub-entity; Generate a geographic entity knowledge graph based on the multi-level geographic entity knowledge graph structure model.

2. The method for generating a geographic entity knowledge graph taking into account both temporal and spatial characteristics according to claim 1, characterized in that: The spatial identity code is composed of a position code, a classification code and a sequence code.

3. The method for generating a geographic entity knowledge graph taking into account both temporal and spatial characteristics according to claim 2, characterized in that: The location code is a code determined based on the Beidou grid location code.

4. The method for generating a geographic entity knowledge graph taking into account both temporal and spatial characteristics according to claim 2, characterized in that: The classification code is a code determined according to the type of geographic entity, which includes natural geographic entity, artificial geographic entity and administrative geographic entity.

5. The method for generating a geographic entity knowledge graph taking into account both temporal and spatial characteristics according to claim 1, characterized in that: Generating a geographic entity knowledge graph according to the multi-level geographic entity knowledge graph structure model specifically includes: Describing the multi-level geographic entity knowledge graph structure model in a triple manner, establishing a directed graph consisting of "nodes-edges", wherein the nodes in the directed graph represent the concepts, entities, concept attributes and entity attributes, and the edges in the directed graph represent the relationship types; Generate a geographic entity knowledge graph based on the directed graph.

6. A device for generating a geographic entity knowledge graph taking into account temporal and spatial characteristics, characterized in that: The device for generating a geographic entity knowledge graph taking into account temporal and spatial characteristics comprises: A model building module is used to establish a multi-level geographic entity knowledge graph structure model, wherein the multi-level geographic entity knowledge graph structure model includes a concept layer, an entity layer and a relationship layer, the concept layer includes a number of concepts, the concepts are used to describe the geometric features and semantic features of the land entity, the entity layer includes a number of instantiated entity groups, the instantiated entity groups include instantiated main entities, instantiated sub-entities and entity attributes, the instantiated main entity is a concrete manifestation of the concept, the instantiated sub-entity is a geographic entity obtained by expanding the instantiated main geography based on scale characteristics, the entity attributes include spatial identity coding and scale characteristics, the relationship layer is used to describe the relationship type, the relationship type includes the relationship between concepts, between concepts and entities, and between entities, and the entities include instantiated main entities and instantiated sub-entities; The knowledge graph generation module is used to generate a geographic entity knowledge graph based on the multi-level geographic entity knowledge graph structure model.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for generating a geographic entity knowledge graph taking into account spatiotemporal characteristics as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the method for generating a geographic entity knowledge graph taking into account temporal and spatial characteristics as described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, it implements the method for generating a geographic entity knowledge graph taking into account temporal and spatial characteristics as described in any one of claims 1 to 5.

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