Entity relation network query response method, apparatus and device, and medium

Abstract

The invention relates to the technical field of computers, in particular to a query response method, device and equipment of an entity relation network and a medium, the entity relation network is constructed based on multiple types of relation judgment data, and relation edges have multiple clear association relation types. When the query request of the specified target relationship type is received, the association state information under the target relationship type can be directly and accurately positioned and queried in the network, so that the problem that the prior art can only respond to pre-stored single relationship type query and cannot accurately respond to multi-type and multi-dimensional association relationship query is effectively solved; and the query result is more comprehensive. Moreover, the entity relationship network fusing various clear association relationship types is constructed, so that a high-quality and structured data basis can be provided for applications such as deep graph analysis, relationship-based intelligent recommendation, decision support and the like, and the bottleneck of limited analysis value of a traditional sparse network is broken through.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a query response method, apparatus, device, and medium for entity relationship networks. Background Technology

[0002] Currently, in the field of entity relationship network construction, graph technology has been applied to integrate multi-source data to build entity relationship networks. A typical construction method employs a static construction paradigm based on explicit association rules: First, entities are extracted from structured data sources. Then, according to predefined explicit association rules (such as direct references), relationship edges representing a single association type are established between entity nodes. Subsequently, the complete network containing all such relationship edges is persistently stored to support subsequent query retrieval.

[0003] However, relying on this static construction paradigm can only respond to a small number of query requests that explicitly establish relationships. Query requests with implicit, non-explicit relationships cannot be responded to directly, resulting in incomplete query results. Summary of the Invention

[0004] To address the problems in the prior art, embodiments of this application provide a method, apparatus, device, and medium for constructing an enterprise entity relationship network, which can meet users' query needs for complex relationships.

[0005] In a first aspect, embodiments of this application provide a query response method for an entity relationship network, the method comprising:

[0006] Retrieve association data of multiple objects from at least one data source, wherein the association data includes various different types of relationship determination data;

[0007] For any given object, based on various types of relationship determination data of that given object, a target object that is associated with that object is identified from other objects; and a relationship edge representing a specific type of association is constructed between the node corresponding to the given object and the node corresponding to the target object.

[0008] In response to an entity relationship query request, a query operation is performed in the entity relationship network to obtain response data for the entity relationship query request. The entity relationship network consists of nodes corresponding to the multiple objects and constructed relationship edges. The response data includes the association status information of the object to be queried under the target relationship type. The target relationship type is the association relationship type indicated in the entity relationship query request.

[0009] The response data is returned through the output interface.

[0010] In one possible implementation, the various types of relationship determination data include some or all of the following:

[0011] The first type of determination data representing the ownership relationship between objects;

[0012] The second type of judgment data characterizing the transaction relationship between objects;

[0013] The third type of judgment data characterizing the hierarchical relationship between objects;

[0014] The fourth type of judgment data characterizing the spatial relationship between objects.

[0015] In one possible implementation, the various types of relationship determination data include the first type of determination data;

[0016] The relationship determination data for any one object includes the first type of determination data, and the step of determining the target object that has an association relationship with the object from other objects includes:

[0017] The ownership-related parties are determined from the first type of determination data, and the ownership-related parties include any one of the objects and the ownership-related objects of the any one object.

[0018] The ownership association object is determined as the target object, wherein the relationship edge constructed between the node corresponding to any object and the node corresponding to the target object represents the ownership association relationship.

[0019] In one possible implementation, the various types of relationship determination data include the second type of determination data;

[0020] The relationship determination data for any one object includes the second type of determination data, and the step of determining the target object that has an association relationship with that object from other objects includes:

[0021] The transaction parties are determined from the second type of determination data, wherein the transaction parties include any one of the objects and the transaction-related objects of the object;

[0022] The transaction-related object is determined as the target object, wherein the relationship edge constructed between the node corresponding to any object and the node corresponding to the target object represents the transaction-related relationship.

[0023] In one possible implementation, the various types of relationship determination data include the third type of determination data;

[0024] The relationship determination data for any one object includes the third type of determination data, and the step of determining the target object that has an association relationship with the object from other objects includes:

[0025] Based on the industry attribute data of any one of the objects in the third type of judgment data, determine the industry level node to which the object belongs in the industry knowledge graph, wherein the industry knowledge graph contains multiple industry level nodes;

[0026] Based on the upstream and downstream order between the hierarchical nodes defined in the industry knowledge graph, the upstream object of any object and / or the downstream object of any object are determined as the target object. The relationship edge constructed between the node corresponding to the target object and the node corresponding to the target object represents the hierarchical association relationship of the industry.

[0027] In one possible implementation, the various types of relationship determination data include the fourth type of determination data;

[0028] The relationship determination data for any one object includes the fourth type of determination data, and the step of determining the target object that has an association relationship with the object from other objects includes:

[0029] The location data of any one of the objects is determined based on the fourth type of determination data;

[0030] Based on the location data of any one of the objects, determine the distance between the object and other objects;

[0031] If the distance between any two objects is less than a preset proximity distance threshold, then the object is determined as the target object. The relational edge constructed between the node corresponding to the object and the node corresponding to the target object represents a spatial proximity relationship.

[0032] In one possible implementation, in response to an entity relationship query request, a query operation is performed in the entity relationship network to obtain response data for the entity relationship query request, including:

[0033] If the entity relationship query request is a query targeting the target industry level, then based on the relationship edge representing the industry level association, the core object in the target industry level is determined, and the upstream and downstream objects associated with the core object are determined along the relationship edge.

[0034] If the entity relationship query request is a query targeting the target transaction network, then based on the relationship edges that represent the transaction association, the object pairs with transaction association relationships are determined;

[0035] If the entity relationship query request is for a target spatial region, then the set of objects located within the target spatial region is determined based on the relationship edges that represent spatial proximity relationships.

[0036] Secondly, embodiments of this application provide a query response apparatus for an entity relationship network, the apparatus comprising:

[0037] An acquisition unit is used to acquire association data of multiple objects from at least one data source, wherein the association data includes multiple different types of relationship determination data;

[0038] The determining unit is used to determine, for any given object, a target object that is associated with that object from other objects based on various types of relationship determination data of that given object; and to construct a relationship edge representing a specific type of association between the node corresponding to the given object and the node corresponding to the target object.

[0039] The query unit is used to respond to an entity relationship query request by performing a query operation in the entity relationship network to obtain response data for the entity relationship query request. The entity relationship network consists of nodes corresponding to the multiple objects and constructed relationship edges. The response data includes the association status information of the object to be queried under the target relationship type. The target relationship type is the association relationship type indicated in the entity relationship query request.

[0040] An output unit is used to return the response data through an output interface.

[0041] Thirdly, embodiments of this application provide an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and when the computer program is executed by the processor, it implements the method described in any one of the query response methods for entity relationship networks in the first aspect.

[0042] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method described in any one of the query response methods for entity relationship networks in the first aspect.

[0043] The technical solution provided in this application has at least the following beneficial effects:

[0044] In this embodiment, the entity relationship network is constructed object-by-object in a fine-grained manner based on various types of relationship determination data, with relationship edges possessing multiple clearly defined association types. When a query request for a specified target relationship type is received, the network can be directly and accurately located and queried for the association status information under the target relationship type. This effectively solves the problem that existing technologies can only respond to queries for a pre-stored single relationship type and cannot accurately respond to queries for multiple types and dimensions of association relationships, resulting in more comprehensive query results. Furthermore, constructing this entity relationship network that integrates multiple clearly defined association types can provide a high-quality, structured data foundation for deep graph analysis and relationship-based intelligent recommendation, decision support, and other applications, breaking through the bottleneck of limited value in traditional sparse network analysis. Attached Figure Description

[0045] Figure 1 A flowchart illustrating a query response method for an entity relationship network provided in an embodiment of this application;

[0046] Figure 2 A structural diagram of a query response device for an entity relationship network provided in an embodiment of this application;

[0047] Figure 3 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0049] The terms "first" and "second" in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the term "comprising" and any variations thereof are intended to cover non-exclusive protection. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices. The term "multiple" in this application can mean at least two, for example, two, three, or more, and the embodiments of this application do not impose limitations.

[0050] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These embodiments should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description. It should be noted that in the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solutions of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0051] The acquisition, transmission, storage, and use of data in this application all comply with the requirements of relevant national laws and regulations.

[0052] Before introducing the query response method for entity relationship networks provided in the embodiments of this application, the technical background of the embodiments of this application will be described in detail below for ease of understanding.

[0053] Currently, in the field of entity relationship network construction, graph technology has been applied to integrate multi-source data to build entity relationship networks. A typical construction method employs a static construction paradigm based on explicit association rules: First, entities are extracted from structured data sources. Then, according to predefined explicit association rules (such as direct references), relationship edges representing a single association type are established between entity nodes. Subsequently, the complete network containing all such relationship edges is persistently stored to support subsequent query retrieval.

[0054] However, relying on this static construction paradigm can only respond to a small number of query requests that explicitly establish relationships. Query requests with implicit, non-explicit relationships cannot be responded to directly, resulting in incomplete query results.

[0055] In view of this, embodiments of this application provide a query response method, apparatus, device, and medium for entity relationship networks, used to meet users' query needs for complex relationships.

[0056] refer to Figure 1 , Figure 1 An exemplary query response method for an entity relationship network is provided in this application embodiment. This method can be executed by a data processing device and includes the following steps:

[0057] Step S101: Obtain the associated data of multiple objects from at least one data source.

[0058] Among them, the associated data includes various types of relationship determination data.

[0059] In this embodiment of the application, the data processing device can collect raw data of multiple objects from multiple internal or external heterogeneous data sources through API interfaces, database connections, file crawling, and other methods.

[0060] These data sources include, but are not limited to: the National Enterprise Credit Information Publicity System, third-party commercial data platforms such as Tianyancha (used to obtain enterprise business registration information, equity investment, shareholder and senior management information), stock exchange disclosure platforms and enterprise annual reports (used to obtain financial data, foreign investment, customer and supplier directories), government procurement websites and various bidding platforms (used to obtain bidding announcements and winning bid information), the State Intellectual Property Office (used to obtain patent and trademark information), geographic information services (used to obtain the latitude and longitude coordinates of the enterprise's registered address), as well as bank internal credit records, transaction records, CRM system data, etc.

[0061] Furthermore, the acquired raw data undergoes ETL processes such as cleaning, deduplication, and format standardization to be transformed into structured relational data. This relational data includes various types of relation determination data used for subsequent relation determination.

[0062] In one alternative implementation, a differentiated update strategy can be established to ensure the timeliness of the data.

[0063] For highly dynamic data such as business registration changes, financing events, bidding announcements, and public opinion news, events can be subscribed to via APIs that connect to the data source through message queues (such as Kafka). Once an update is detected, an incremental processing flow is immediately triggered to update the data and relationships of the relevant objects.

[0064] For data with long update cycles, such as financial statements and annual reports, you can set up scheduled tasks to perform periodic (e.g., monthly) full or incremental synchronization to ensure the integrity of basic information.

[0065] In this application embodiment, the relationship determination data is mainly divided into four categories:

[0066] The first type of data (used to characterize the ownership relationship between objects) includes information such as the equity shareholding ratio between enterprises, investment amount, and the chain of actual controllers.

[0067] The second category of data (used to characterize transaction relationships between objects) includes information on the tendering party and the winning bidder in bidding records, the buyer and seller in sales contracts, and customers and suppliers disclosed in annual reports.

[0068] The third type of judgment data (used to characterize the hierarchical relationship between objects): such as the national economic industry classification code of an enterprise, description of its main business, product information, etc., which can be mapped to a pre-built industrial chain knowledge base.

[0069] The fourth type of judgment data (used to characterize the spatial relationship between objects) includes the company's registered address, business address, and their converted precise latitude and longitude coordinates.

[0070] Step S102: For any given object, based on the various types of relationship determination data of the given object, determine the target object that has an association relationship with the given object from other objects; and construct a relationship edge representing a specific association relationship type between the node corresponding to the given object and the node corresponding to the target object.

[0071] In one optional embodiment, based on the first type of determination data of any object, the ownership-related parties can be determined, including the arbitrary object and the ownership-related object of the arbitrary object. Subsequently, the ownership-related object can be determined as the target object, wherein the relationship edge constructed between the node corresponding to the arbitrary object and the node corresponding to the target object represents the ownership-related relationship.

[0072] The following explanation uses Company A as an example.

[0073] For example, the first type of determination data for company A can be analyzed. If it is found that company B is a shareholder of company A (or company A is a shareholder of company B), then company B can be determined as the target object of company A in the ownership dimension. Therefore, the relationship edge constructed between the node corresponding to company A and the node corresponding to company B represents the ownership association. For example, edge A-(equity holding)->B.

[0074] In another alternative embodiment, the transacting parties can be determined from the second type of determination data. These transacting parties include any single object and any object's associated transacting object. Then, the associated transacting object can be identified as the target object, where the relationship edges constructed between the node corresponding to any single object and the node corresponding to the target object represent the transacting relationship.

[0075] Let's continue with the example of Company A.

[0076] For example, we can analyze company A's recent bidding records. If we find that company A is the bidding party and company C wins the bid, then company C can be identified as company A's target object in the transaction dimension from the two parties involved, A and C. Similarly, we can identify supplier D as the target object from the supplier directory. Therefore, the relationship edge constructed between the node corresponding to company A and the node corresponding to company C represents the transaction association. For example, edge A-(bidding)->C.

[0077] In another optional embodiment, the industry-level node to which any object belongs in the industry knowledge graph can be determined based on the industry attribute data of any object in the third type of judgment data. The industry knowledge graph contains multiple industry-level nodes. Subsequently, based on the upstream and downstream order between the hierarchical nodes defined in the industry knowledge graph, the upstream object and / or downstream object of any object can be determined as the target object. The relationship edges constructed between the node corresponding to any object and the node corresponding to the target object represent the industry-level association.

[0078] Continuing the previous example, the industry code of company A can be mapped to a pre-built industry knowledge graph (this graph defines multiple levels of nodes from raw materials to end consumers, as well as the upstream and downstream order). Assuming company A belongs to the "auto parts manufacturing" segment, company E, belonging to the upstream special steel production segment, and company F, belonging to the downstream vehicle assembly segment, can be automatically identified as company A's target objects in the industry hierarchy. Then, the relationship edges constructed between the nodes corresponding to company A and the nodes corresponding to company E represent the industry hierarchy connections. For example, edge A - (upstream supplier) -> E.

[0079] In another optional embodiment, the location data of any object can be determined based on the fourth type of determination data. Then, based on the location data of any object, the distance between any object and other objects can be determined. If the distance between any object and any other object is less than a preset proximity distance threshold, then that object can be determined as the target object. The relational edges constructed between the node corresponding to any object and the node corresponding to the target object represent the spatial proximity relationship.

[0080] Continuing the previous example, we can calculate the distance between the coordinates of company A and the coordinates of other companies. If the distance between company A and company G is less than a preset proximity distance threshold, then company G is identified as the spatial target object of company A. Therefore, the relationship edges constructed between the nodes corresponding to company A and the nodes corresponding to company G represent spatial proximity associations. For example, the edge A-(neighbor)->G.

[0081] Furthermore, whenever a target object is identified, a relationship edge with a clear type label can be constructed between the node corresponding to enterprise A and the node corresponding to the target object in the graph database. This process of "identifying the target object - constructing the relationship edge" is repeated for each object.

[0082] Ultimately, all object nodes and all constructed relation edges with type labels together form a large-scale, multi-dimensional, semantically rich entity relation network (i.e., an enterprise graph), which is persistently stored in a graph database (such as Neo4j).

[0083] Step S103: In response to the entity relationship query request, a query operation is performed in the entity relationship network to obtain response data for the entity relationship query request.

[0084] The entity relationship network consists of nodes corresponding to multiple objects and established relationship edges. The response data includes the association status information of the object to be queried under the target relationship type. The target relationship type is the association relationship type to be queried indicated in the entity relationship query request.

[0085] In practice, when a user (such as an account manager) needs to conduct customer acquisition or risk control analysis, they can submit an entity relationship query request through a client (such as a web interface or mobile application). This request must include at least: the object (one or more) to be queried and the target relationship type.

[0086] For example, query requests can first be processed by the intelligent assistant module. This module adopts a collaborative architecture of "large model + small model". The large model (such as LLM) is responsible for parsing the complex query intent entered by the user in natural language (e.g., "find financially healthy potential suppliers in the new energy vehicle industry chain in the Yangtze River Delta region") and transforming it into executable structured query instructions. The small model focuses on precise calculations in specific domains.

[0087] Furthermore, based on the parsed query instructions, the enterprise graph engine can be invoked to perform graph computations within the entity relationship network. This includes tasks such as multi-hop relationship penetration, path searching, community discovery (identifying industry chain clusters), or centrality calculation (locating the leading enterprise in the chain).

[0088] At the same time, it can also trigger quantitative analysis models such as financial and interest rate spread models. The financial model quantifies and scores the solvency and profitability of the companies involved, forming a financial health index; the interest rate spread model combines market interest rates and corporate credit data to predict interest rate spread sensitivity.

[0089] In the embodiments of this application, for reasoning that requires the integration of external knowledge, KG-RAG technology can be used to combine the graph query results with the semantic capabilities of the large model to generate traceable and interpretable analytical basis.

[0090] The calculated relationship topology (such as upstream and downstream links in the industrial chain) and the quantitative indicators (financial scores, risk coefficients) calculated by the model can be integrated in real time. For example, when displaying a supply chain, the financial health of the core enterprise and the probability of risk transmission to its upstream and downstream enterprises can be simultaneously marked. Application modules such as regional maps, industry maps, and enterprise details share this integrated data, realizing the linkage between views and information. For example, clicking on an industrial cluster from the regional map allows users to drill down to view the industrial chain composition of the cluster and the quantitative assessment of key enterprises.

[0091] In this embodiment of the application, different graph traversal or calculation algorithms can be invoked depending on the query intent:

[0092] If the entity relationship query request is a query targeting the target industry level, then based on the relationship edges that represent the industry level association, the core object in the target industry level is determined, and the upstream and downstream objects associated with the core object are determined along the relationship edges.

[0093] For example, if the query request is "find the core enterprises and their upstream and downstream companies in the new energy vehicle battery industry chain", then you can first locate the "battery manufacturing" link in the industry knowledge graph, find the core enterprise (chain leader) in this link, and then traverse along the industry hierarchy relationship edges to find all related upstream and downstream objects, such as raw materials and vehicle manufacturers.

[0094] In this embodiment of the application, if the entity relationship query request is a query for the target transaction network, then the object pairs with transaction relationships can be determined based on the relationship edges that represent the transaction relationship.

[0095] For example, if the query request is "find all companies that have had transactions with target company H in the past year", then starting from the company H node, find all directly related transaction pairs along all transaction relationship edges (such as bidding ->, supply ->).

[0096] In this embodiment of the application, if the entity relationship query request is a query for a target spatial region, the set of objects located within the target spatial region can be determined based on the relationship edges that represent spatial proximity relationships.

[0097] For example, if the query request is "list all companies in the field of artificial intelligence in Zhangjiang Science City", then spatial proximity relationships can be used to perform a fast regional search to obtain the set of objects within the target spatial area.

[0098] Finally, the results of the query operation (such as a list of nodes, a set of paths, and statistical metrics) are encapsulated into structured response data. This data not only contains a list of objects but also specific association status information between them under the target relationship type, such as path details and the time when the relationship occurred, forming a complete association status report.

[0099] In addition, the response data can also integrate quantitative decision-making data such as financial health, interest rate spread forecasts, and risk warnings, as well as semantic interpretations and customer recommendation texts generated by the intelligent assistant.

[0100] Step S104: Return response data through the output interface.

[0101] In this step, the generated rich response data can be returned to the client via API or other means. The client can then present this data to the user in the form of visualizations, analysis reports, or lists (such as a "List of Potential Supply Chain Finance Clients").

[0102] Subsequent business operations performed by users based on these in-depth analysis results (such as tagging customers and initiating credit granting) generate feedback data that is then fed back to the data layer, thereby driving continuous optimization of the graph and model, realizing a complete technical and business closed loop of "data acquisition → network construction → intelligent analysis → business application → data feedback".

[0103] Through the above four steps, this application constructs an intelligent entity relationship network query response system that can be dynamically updated, supports multi-dimensional relationship discovery, and deeply integrates quantitative models and semantic understanding capabilities. It effectively solves the problems of traditional solutions having a single relationship dimension, static and superficial analysis, and insufficient decision support.

[0104] Based on the same inventive concept, embodiments of this application provide a query response device for an entity relationship network. Please refer to... Figure 2 The device includes: an acquisition unit 201, a determination unit 202, a query unit 203, and an output unit 204, wherein:

[0105] The acquisition unit 201 is used to acquire association data of multiple objects from at least one data source, wherein the association data includes various types of relationship determination data.

[0106] The determining unit 202 is used to determine, for any given object, a target object that has an association with the given object from other objects based on various types of relationship determination data of the given object; and to construct a relationship edge representing a specific type of association between the node corresponding to the given object and the node corresponding to the target object.

[0107] The query unit 203 is used to respond to an entity relationship query request by performing a query operation in the entity relationship network and obtaining response data for the entity relationship query request. The entity relationship network consists of nodes corresponding to multiple objects and constructed relationship edges. The response data includes the association status information of the object to be queried under the target relationship type. The target relationship type is the association relationship type indicated in the entity relationship query request.

[0108] Output unit 204 is used to return response data through the output interface.

[0109] The entity relationship network query response device provided in this application embodiment and the entity relationship network query response method in the above embodiments have the same beneficial effects, and will not be described in detail here.

[0110] Having introduced the query response method and query response apparatus for entity relationship networks according to exemplary embodiments of this application, the electronic device provided according to embodiments of this application will now be described.

[0111] This application provides an electronic device that can implement the query response method for entity relationship networks discussed above. Please refer to... Figure 3 The device includes a memory 301, a processor 302, and a bus 303.

[0112] The memory 301 is used to store computer programs executed by the processor 302. The memory 301 may mainly include a program storage area and a data storage area. The program storage area may store the operating system and programs required to run instant messaging functions, etc.; the data storage area may store various instant messaging information and operation instruction sets, etc.

[0113] Memory 301 may be volatile memory, such as random-access memory (RAM); memory 301 may also be non-volatile memory, such as read-only memory, flash memory, hard disk drive (HDD), or solid-state drive (SSD); or memory 301 may be any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. Memory 301 may be a combination of the above-described memories.

[0114] Processor 302 may include one or more central processing units (CPUs) or digital processing units, etc. Processor 302 is used to implement the entity relationship network query response method in the above embodiments when calling the computer program stored in memory 301.

[0115] This application embodiment does not limit the specific connection medium between the memory 301 and the processor 302 described above. This application embodiment... Figure 3 The memory 301 and the processor 302 are connected via a bus 303, and the bus 303 is in Figure 3 The connections between other components are shown in thick lines only and are not intended to be limiting. Bus 303 can be divided into address bus, data bus, control bus, etc. For ease of illustration, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0116] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium. The computer program product includes computer program code, which, when executed on a computer, causes the computer to perform any of the entity-relationship network query response methods discussed above. Since the principle by which the above-described computer-readable storage medium solves the problem is similar to that of the entity-relationship network query response method, the implementation of the above-described computer-readable storage medium can be found in the implementation of the method; repeated details will not be elaborated further.

[0117] Based on the same inventive concept, this application also provides a computer program product, which includes computer program code. When the computer program code is run on a computer, it causes the computer to execute any of the entity relationship network query response methods discussed above. Since the principle of solving the problem by the above-described computer program product is similar to that of the entity relationship network query response method, the implementation of the above-described computer program product can refer to the implementation of the method, and repeated details will not be described again.

[0118] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0120] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of user-operated steps to be executed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0122] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for responding to a query of an entity-relationship network, characterized by, The method is executed by a data processing device and includes: Retrieve association data of multiple objects from at least one data source, wherein the association data includes various different types of relationship determination data; For any given object, based on various types of relationship determination data of that given object, a target object that is associated with that object is identified from other objects; and a relationship edge representing a specific type of association is constructed between the node corresponding to the given object and the node corresponding to the target object. In response to an entity relationship query request, a query operation is performed in the entity relationship network to obtain response data for the entity relationship query request. The entity relationship network consists of nodes corresponding to the multiple objects and constructed relationship edges. The response data includes the association status information of the object to be queried under the target relationship type. The target relationship type is the association relationship type indicated in the entity relationship query request. The response data is returned through the output interface.

2. The method of claim 1, wherein, The various types of relationship determination data include some or all of the following: The first type of determination data representing the ownership relationship between objects; The second type of judgment data characterizing the transaction relationship between objects; The third type of judgment data characterizing the hierarchical relationship between objects; The fourth type of judgment data characterizing the spatial relationship between objects.

3. The method of claim 2, wherein, The various types of relationship determination data include the first type of determination data; The relationship determination data for any one object includes the first type of determination data, and the step of determining the target object that has an association relationship with the object from other objects includes: The ownership-related parties are determined from the first type of determination data, and the ownership-related parties include any one of the objects and the ownership-related objects of the any one object. The ownership association object is determined as the target object, wherein the relationship edge constructed between the node corresponding to any object and the node corresponding to the target object represents the ownership association relationship.

4. The method according to claim 2, characterized in that, The various types of relationship determination data include the second type of determination data; The relationship determination data for any one object includes the second type of determination data, and the step of determining the target object that has an association relationship with that object from other objects includes: The transaction parties are determined from the second type of determination data, wherein the transaction parties include any one of the objects and the transaction-related objects of the object; The transaction-related object is determined as the target object, wherein the relationship edge constructed between the node corresponding to any object and the node corresponding to the target object represents the transaction-related relationship.

5. The method according to claim 2, characterized in that, The various types of relationship determination data include the third type of determination data; The relationship determination data for any one object includes the third type of determination data, and the step of determining the target object that has an association relationship with the object from other objects includes: Based on the industry attribute data of any one of the objects in the third type of judgment data, determine the industry level node to which the object belongs in the industry knowledge graph, wherein the industry knowledge graph contains multiple industry level nodes; Based on the upstream and downstream order between hierarchical nodes defined in the industry knowledge graph, the upstream object of any object and / or the downstream object of any object are determined as the target object. The relationship edge constructed between the node corresponding to the target object and the node corresponding to the target object represents the hierarchical association relationship of the industry.

6. The method according to claim 2, characterized in that, The various types of relationship determination data include the fourth type of determination data; The relationship determination data for any one object includes the fourth type of determination data, and the step of determining the target object that has an association relationship with the object from other objects includes: The location data of any one of the objects is determined based on the fourth type of determination data; Based on the location data of any one of the objects, determine the distance between the object and other objects; If the distance between any two objects is less than a preset proximity distance threshold, then the object is determined as the target object. The relational edge constructed between the node corresponding to the object and the node corresponding to the target object represents a spatial proximity relationship.

7. The method according to claim 1, characterized in that, In response to an entity relationship query request, a query operation is performed in the entity relationship network to obtain response data for the entity relationship query request, including: If the entity relationship query request is a query targeting the target industry level, then based on the relationship edge representing the industry level association, the core object in the target industry level is determined, and the upstream and downstream objects associated with the core object are determined along the relationship edge. If the entity relationship query request is a query targeting the target transaction network, then based on the relationship edges that represent transaction relationships, the object pairs with transaction relationships are determined; If the entity relationship query request is for a target spatial region, then the set of objects located within the target spatial region is determined based on the relationship edges that represent spatial proximity relationships.

8. A query response device for an entity relationship network, characterized in that, The device includes: An acquisition unit is used to acquire association data of multiple objects from at least one data source, wherein the association data includes multiple different types of relationship determination data; The determining unit is used to determine, for any given object, a target object that is associated with that object from other objects based on various types of relationship determination data of that given object; and to construct a relationship edge representing a specific type of association between the node corresponding to the given object and the node corresponding to the target object. The query unit is used to respond to an entity relationship query request by performing a query operation in the entity relationship network to obtain response data for the entity relationship query request. The entity relationship network consists of nodes corresponding to the multiple objects and constructed relationship edges. The response data includes the association status information of the object to be queried under the target relationship type. The target relationship type is the association relationship type indicated in the entity relationship query request. An output unit is used to return the response data through an output interface.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can run on the processor, and when the computer program is executed by the processor, it implements the method of any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program therein, characterized in that: When the computer program is executed by a processor, it implements the method according to any one of claims 1 to 7.

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