Knowledge graph processing method and device, equipment and storage medium

Abstract

The invention relates to the technical field of data processing, and provides a knowledge graph processing method and device, equipment and a storage medium, and the method comprises the steps: obtaining a preset knowledge graph which comprises graph nodes; obtaining editing data when a first user edits map nodes in the knowledge map and version information of the map nodes edited by the first user; comparing the version information of the graph node edited by the first user with the version information of the graph node in the current knowledge graph; and according to a comparison result of the version information of the graph node edited by the first user and the version information of the graph node in the current knowledge graph, determining whether to edit the graph node in the knowledge graph according to the editing data. In the field of insurance, an insurance knowledge graph can be constructed according to insurance service data. In a medical scene, the medical knowledge graph can be constructed according to medical related knowledge, so that a plurality of users can cooperatively edit contents of graph nodes such as diseases and symptoms in the medical knowledge graph.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to methods, devices, equipment and storage media for processing knowledge graphs. Background Art

[0002] With the development of network technology, the financial sector is plagued by widespread issues such as fragmented data storage and duplicated content. For example, insurance companies typically store large amounts of data, including insurance products, claims, agent information, and customer information. In the medical field, for example, hospital systems typically store extensive amounts of patient information, medical equipment information, and medical staff information. As business volumes continue to expand, the volume of data related to both the financial and medical sectors will also surge.

[0003] Related technologies propose data management solutions based on knowledge graphs. Knowledge graphs are semantic networks used to describe relationships between entities. They graphically represent real-world knowledge, with the core focus being on converting information into a graphical structure. Knowledge graphs can effectively process massive amounts of insurance data for financial institutions, such as insurance companies.

[0004] However, in the process of data management, related technologies are prone to affecting the integrity of data due to user editing of data, thereby reducing the user experience. Summary of the Invention

[0005] The main purpose of this application is to provide a knowledge graph processing method, device, equipment and storage medium to ensure the content integrity of the graph nodes, realize multi-user collaborative editing, and thus improve the user experience during editing.

[0006] In a first aspect, the present application provides a method for processing a knowledge graph, comprising:

[0007] Obtaining a preset knowledge graph, wherein the knowledge graph includes graph nodes;

[0008] Obtaining editing data when a first user edits a graph node in the knowledge graph, and version information of the graph node edited by the first user;

[0009] Comparing the version information of the graph node edited by the first user with the version information of the graph node in the current knowledge graph;

[0010] Based on the comparison result of the version information of the graph node edited by the first user and the version information of the graph node in the current knowledge graph, determine whether to edit the graph node in the knowledge graph according to the editing data.

[0011] In a second aspect, the present application further provides a knowledge graph processing device, comprising:

[0012] A first acquisition module is used to acquire a preset knowledge graph, where the knowledge graph includes graph nodes;

[0013] A second acquisition module is used to obtain editing data when a first user edits a graph node in the knowledge graph, and version information of the graph node edited by the first user;

[0014] A comparison module, configured to compare the version information of the graph node edited by the first user with the version information of the graph node in the current knowledge graph;

[0015] An editing module is used to determine whether to edit the graph node in the knowledge graph according to the editing data based on the comparison result of the version information of the graph node edited by the first user and the version information of the graph node in the current knowledge graph.

[0016] In a third aspect, the present application further provides a computer device, comprising a memory and a processor;

[0017] The memory is used to store computer programs;

[0018] The processor is used to execute the computer program and implement the steps of the knowledge graph processing method as described above when executing the computer program.

[0019] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the knowledge graph processing method as described above are implemented.

[0020] The present application provides a method, apparatus, device and storage medium for processing a knowledge graph, wherein the method includes: obtaining a preset knowledge graph, the knowledge graph including graph nodes; obtaining editing data when a first user edits a graph node in the knowledge graph, and version information of the graph node edited by the first user; comparing the version information of the graph node edited by the first user with the version information of the graph node in the current knowledge graph; and determining whether to edit the graph node in the knowledge graph according to the editing data based on the comparison result of the version information of the graph node edited by the first user and the version information of the graph node in the current knowledge graph. This application can determine whether other users are editing the graph node at the same time by comparing the version information of the graph node edited by the first user with the version information of the graph node of the current knowledge graph; then determine whether to edit the graph node according to the editing data edited by the first user based on the comparison result of the two version information, so as to ensure the content integrity of the graph node when other users edit the graph node at the same time, and the editing data edited by the first user will not be affected by other users editing the graph node at the same time, thereby realizing multi-user collaborative editing, thereby improving the user experience when editing. In the insurance field, an insurance knowledge graph can be constructed based on insurance business data. In a medical scenario, a medical knowledge graph can be constructed based on medical-related knowledge to enable multiple users to collaboratively edit the content of graph nodes such as diseases and symptoms in the medical knowledge graph. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 A schematic diagram of a process for processing a knowledge graph provided in an embodiment of the present application;

[0023] Figure 2 A schematic diagram of the connection between the server and the terminal device provided in the embodiment of the present application;

[0024] Figure 3 A schematic block diagram of a knowledge graph processing device provided in an embodiment of the present application;

[0025] Figure 4 A schematic block diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0027] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0028] The embodiments of the present application provide a method, apparatus, device, and storage medium for processing a knowledge graph. The method for processing the knowledge graph can be applied to a terminal device, which can be a mobile phone, tablet computer, laptop computer, desktop computer, or other device. It can also be applied to a server, which can be a standalone server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0029] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0030] See also Figure 1 , Figure 1 A flowchart of a method for processing a knowledge graph provided in an embodiment of the present application. It should be noted that the method for processing a knowledge graph provided in an embodiment of the present application can be used in a terminal device, and of course can also be used in a server.

[0031] like Figure 2 As shown, the knowledge graph processing method is applied to a server, and the server and the terminal device are in communication connection. The server can send the edited content of the graph nodes in the knowledge graph obtained by the knowledge graph processing method to the terminal device. Of course, this is not limited to this and is not limited here.

[0032] In specific implementation, the terminal device includes but is not limited to: any one of a mobile phone, a tablet computer, a laptop computer, and a desktop computer; the server can be a single server or a server cluster, or a cloud server that provides cloud computing services.

[0033] like Figure 1As shown, the knowledge graph processing method includes steps S101 to S104.

[0034] Step S101: Obtain a preset knowledge graph, where the knowledge graph includes graph nodes.

[0035] As you can understand, a knowledge graph is a technology for representing and organizing knowledge using a graph structure, used to describe real-world entities and the relationships between them. The core elements of a knowledge graph include entities, relationships, and attributes. Taking healthcare as an example, the corresponding healthcare knowledge graph may include medical entities, elderly care entities, and person entities. Medical entities may include diseases, symptoms, medications, examination items, and treatment plans. Elder care entities may include elderly care institutions, elderly care service programs, and elderly care facilities. Person entities may include doctors, nurses, caregivers, patients, and family members. The corresponding healthcare knowledge graph may include medical relationships and elderly care relationships. Medical relationships may include the "manifestation" relationship between diseases and symptoms, and the "applicability" relationship between diseases and medications. Elder care relationships may include the "providence" relationship between elderly care institutions and elderly care service programs, and the "service" relationship between caregivers and residents. Attributes may include the concept of the disease, the value and efficacy of medications, and the name of the doctor.

[0036] The graph nodes in the embodiment of the present application may include entities and relationships. The collected knowledge data may first be subjected to knowledge extraction such as entity extraction, entity attribute extraction, and entity relationship extraction, and then different knowledge may be integrated to construct a knowledge graph. Specifically, entities may be identified from operation and maintenance documents and fault records in insurance business data, such as core business systems, database services, certain fault events, etc. Entity attributes may also be identified, such as fault time, module to which it belongs, person in charge, scope of influence, etc., which are used to describe information of each graph node in the knowledge graph. Entity relationships may also be identified, such as semantic relationships between entities (depends on, is responsible for processing, affects, belongs to, etc.), to construct edges between nodes in the knowledge graph.

[0037] In the insurance business scenario, the insured, the insured, the beneficiary, the insurance product, the insurance clauses, and the insurance risk factors can be considered as entities in the insurance knowledge graph. The relationship between the insured and the insured, the relationship between the insurance product and the corresponding clauses, and the relationship between the insurance product and the corresponding insurance risk factors can be considered as relationships between entities in the insurance knowledge graph. The insured's identity information and the specific product information of the insurance product can be considered as attributes of the entities in the insurance knowledge graph.

[0038] In medical business scenarios, natural language processing technology can be used to identify entities such as diseases, symptoms, and drugs in collected medical data; it can also identify relationships such as diseases and corresponding treatment drugs, diseases and corresponding symptoms; and it can also identify attributes such as the value and efficacy of drugs.

[0039] Step S102: Obtain the editing data when the first user edits the graph node in the knowledge graph, and the version information of the graph node edited by the first user.

[0040] It is understood that the content of each graph node in the knowledge graph can be edited. Edit data refers to the content edited by different users on any graph node in the knowledge graph. Version information refers to the version number corresponding to the content of the graph node. In this embodiment of the application, a version number can be configured for the content of each graph node in the knowledge graph. For example, the version number corresponding to the content of graph node A can be 001.

[0041] For example, the edit data generated when a first user edits a graph node in a knowledge graph refers to the edited data that has not yet been updated in the knowledge graph after the user edits the graph node's content. The version information of a graph node edited by the first user refers to the version number corresponding to the graph node's content when the user initiates the edit operation. Taking the medical field as an example, a knowledge graph related to this field includes multiple graph nodes, which can represent medical entities such as diseases, examination items, and medications. For example, a graph node is Drug A, and its content includes a retail price of 20 yuan, an effective duration of one hour, and an expiration date of 24 months. When the first user initiates an edit operation on the graph node for Drug A, the version number corresponding to the Drug A content is 001. If the first user changes the effective duration of Drug A to 40 minutes, and this "40 minutes" has not yet been updated in the knowledge graph, then the content "Drug A's effective duration is 40 minutes" represents the edit data generated when the first user edits the graph node in the knowledge graph. Correspondingly, the version information of the graph node edited by the first user is the version number when the first user initiates editing of the graph node for drug A, that is, 001.

[0042] Step S103: Compare the version information of the graph node edited by the first user with the version information of the graph node in the current knowledge graph.

[0043] It is understandable that there will generally be multiple users editing the same graph node in the knowledge graph at the same time, and when different contents of the same graph node are updated to the knowledge graph, corresponding version information will be generated, that is, the same graph node in the knowledge graph can have different version numbers. Therefore, it is possible to determine whether other users are editing the same graph node at the same time by comparing different version information. Specifically, the version information of the graph node edited by the first user refers to the version number corresponding to the content of the graph node when the user initiates the operation of editing the graph node. The version information of the graph node in the current knowledge graph refers to the version number of the graph node in the current knowledge graph when the first user completes the editing operation on the graph node and the corresponding editing data has not been updated to the knowledge graph. The embodiment of the present application can determine whether other users are editing the same graph node at the same time by comparing the version number of the first user when initiating the editing operation on the graph node and the version number of the graph node in the current knowledge graph.

[0044] Step S104: Based on the comparison result of the version information of the graph node edited by the first user and the version information of the graph node in the current knowledge graph, determine whether to edit the graph node in the knowledge graph according to the editing data.

[0045] For example, based on the comparison result of the version information of the graph node edited by the first user and the version information of the graph node in the current knowledge graph, it can be determined whether other users are editing the graph node at the same time when the first user initiates an edit operation on the graph node. Based on this, in order to ensure the consistency of the knowledge graph, the embodiment of the present application can determine whether to edit the graph node according to the editing data edited by the first user based on the comparison result of the two version information.

[0046] Taking the insurance application scenario as an example, in the insurance knowledge graph constructed based on insurance business data, the first user initiates an editing operation on the insured graph node in the insurance knowledge graph. At this time, the version information of the graph node is 001. Assuming that the name of the insured is A, when the first user completes the editing operation on the insured, that is, changes the insured's name to B, if no other user modifies the insured's information during this period, the version information of the insured graph node in the current knowledge graph will still be 001; if a second user edits the insured's information at the same time during this period, for example, changes the insured's name to C, and the content edited by the second user has been updated to the insurance knowledge graph when the first user completes the editing, then the version information of the insured graph node in the current knowledge graph is 002; therefore, by comparing the two version information, that is, comparing 001 and 002, it can be determined whether the insured's name is modified according to the name B modified by the first user.

[0047] The embodiment of the present application can determine whether other users are editing the graph node at the same time by comparing the version information of the graph node edited by the first user with the version information of the graph node in the current knowledge graph, and thus determine whether to edit the graph node in the knowledge graph based on the editing data of the first user, so as to ensure the content integrity of the graph node and realize multi-user collaborative editing.

[0048] The method for processing the knowledge graph provided in the above embodiment includes: obtaining a preset knowledge graph, the knowledge graph including graph nodes; obtaining the editing data when the first user edits the graph node in the knowledge graph, and the version information of the graph node edited by the first user; comparing the version information of the graph node edited by the first user with the version information of the graph node in the current knowledge graph; and determining whether to edit the graph node in the knowledge graph according to the editing data based on the comparison result of the version information of the graph node edited by the first user and the version information of the graph node in the current knowledge graph. The present application can determine whether there are other users who edit the graph node at the same time by comparing the version information of the graph node edited by the first user with the version information of the graph node in the current knowledge graph; and then determine whether to edit the graph node according to the editing data edited by the first user based on the comparison result of the two version information, so as to ensure the content integrity of the graph node when other users edit the graph node at the same time, and the editing data edited by the first user will not be affected by other users editing the graph node at the same time, thereby realizing multi-user collaborative editing, thereby improving the user experience when editing. In the insurance sector, insurance knowledge graphs can be constructed based on insurance business data. In healthcare scenarios, medical knowledge graphs can be constructed based on medical-related knowledge, enabling multiple users to collaboratively edit the content of graph nodes such as diseases and symptoms in the medical knowledge graph.

[0049] In an exemplary embodiment, step S104 includes step S1041 and step S1042.

[0050] Step S1041: When the version information of the graph node edited by the first user is consistent with the version information of the graph node in the current knowledge graph, edit the graph node in the knowledge graph according to the editing data.

[0051] Step S1042: When the version information of the graph node edited by the first user is inconsistent with the version information of the graph node in the current knowledge graph, the target editing data is determined based on the editing data and the node data of the graph node in the current knowledge graph, and the graph node in the knowledge graph is edited based on the target editing data.

[0052] For example, if the version number of the graph node edited by the first user is the same as the version number of the graph node in the current knowledge graph, this means that no other user edited the graph node simultaneously between the time the first user initiated the edit operation and the time the edit operation was completed and the edit data was obtained. Accordingly, the node data of the graph node in the current knowledge graph is the same as the content of the graph node in the knowledge graph when the first user initiated the edit operation. Therefore, the graph node in the knowledge graph can be edited based on the edit data edited by the first user.

[0053] For example, when the version number of the graph node edited by the first user is inconsistent with the version number of the graph node in the current knowledge graph, it means that other users have edited the graph node at the same time during the period from when the first user initiated the editing operation on the graph node to when the editing operation was completed and the editing data was obtained. Correspondingly, the node data of the graph node in the current knowledge graph is different from the content of the graph node in the knowledge graph when the first user initiated the editing operation. Therefore, the graph node in the knowledge graph cannot be modified directly based on the editing data edited by the first user. Instead, it is necessary to determine the target editing data based on the editing data edited by the first user and the node data of the graph node in the current knowledge graph, so as to edit the graph node in the knowledge graph based on the target editing data, thereby avoiding the loss of data in the knowledge graph caused by multiple users editing the graph node at the same time.

[0054] The embodiment of the present application edits the graph nodes in the knowledge graph by comparing the version information of the graph nodes at different time points, thereby realizing multi-user collaborative editing and ensuring the integrity of the knowledge graph.

[0055] In an exemplary embodiment, step S104 further includes step S201 .

[0056] Modify the version information of the graph node based on the version information of the graph node in the current knowledge graph.

[0057] It is understandable that after modifying the content of a graph node in a knowledge graph, the version number of the graph node can be updated so that when subsequent users modify the graph node of the knowledge graph, they can determine whether other users are editing the graph node at the same time by comparing whether the two version numbers of the same graph node at different time points are consistent. Specifically, the modified version number can be obtained by incrementing the original version number. For example, when a user initiates an editing operation, the version information of the graph node is 001. After editing the graph node according to the editing data, the version information of the graph node is modified to 002.

[0058] In an exemplary embodiment, step S1042 includes step S421 and step S422.

[0059] Step S421: Display the edited data and the node data of the graph nodes in the current knowledge graph on the human-computer interaction interface.

[0060] Step S422: In response to the user's selection operation on editing data and node data on the human-computer interaction interface, target editing data is determined.

[0061] For example, when the version number of the graph node edited by the first user is inconsistent with the version number of the graph node in the current knowledge graph, the editing data edited by the first user and the node data of the graph node in the current knowledge graph can be displayed on the human-computer interaction interface for the user to select the target editing data. In other words, the first user can judge and determine the target editing data corresponding to the graph node. In addition, while displaying the editing data edited by the first user and the node data of the graph node in the current knowledge graph on the human-computer interaction interface, options such as merging data, abandoning this edit, and returning to the historical version can also be displayed for the first user to choose.

[0062] In response to the user's selection operation on the editing data and node data on the human interaction interface, the target editing data can be determined. In the embodiment of the present application, the target editing data can be the editing data of the first user, or the node data of the graph node in the current knowledge graph, or the merged content of the editing data and the node data. Specifically, the merging of the editing data and the node data can be automatically merged through text recognition technology, or the data merging can be achieved in response to the user's dragging operation on the editing data and the node data.

[0063] Take the medical field as an example, the doctor-patient relationship between patient A and the doctor. In the original medical-related knowledge graph, patient A and doctor B are in a doctor-patient relationship. The first user changes it to patient A and doctor C as a doctor-patient relationship, and another user changes it to patient A and doctor D as a doctor-patient relationship. At this time, it can be displayed on the human-computer interaction interface that patient A and doctor C are in a doctor-patient relationship, and patient A and doctor D are in a doctor-patient relationship. Then, based on the first user's dragging operation on the data corresponding to these two doctor-patient relationships, the data can be merged to obtain the target editing data, that is, patient A and doctors C and D are all in a doctor-patient relationship. The embodiment of the present application displays the editing data and node data in a visual manner so that the user can compare different data more intuitively, thereby determining the target editing data.

[0064] In an exemplary embodiment, step A to step D are further included before step S402.

[0065] Step A: Determine the first editing type based on the editing data when the first user edits the graph node in the knowledge graph, and the node data of the graph node edited by the first user.

[0066] Step B: Determine the second editing type based on the node data of the graph node edited by the first user and the node data of the graph node in the current knowledge graph.

[0067] Step C: Determine the knowledge conflict type corresponding to the edited data based on the first edit type and the second edit type.

[0068] Step D: Store the conflict event information corresponding to the knowledge conflict type. The conflict event information includes the attribute information of the first user, the time information edited by the first user, the node data of the graph node edited by the first user, the editing data, and the node data of the graph node in the current knowledge graph.

[0069] For example, both the first editing type and the second editing type may include attribute editing and relationship editing of graph nodes. For example, in an insurance scenario, modifying the identity information of the policyholder is an attribute modification, and modifying the relationship between the policyholder and the insured is a relationship edit. Knowledge conflict types may include attribute conflicts and relationship conflicts. An attribute conflict occurs when at least two users simultaneously make different edits to the attributes of the same graph node in the knowledge graph; for example, two users simultaneously modify the address of the insured, and the two modified addresses are contradictory. A relationship conflict occurs when at least two users simultaneously make different edits to the relationship of the same graph node in the knowledge graph; for example, two users simultaneously modify the relationship between the policyholder and the insured, that is, one user modifies that policyholder A and the insured B are in a father-daughter relationship and an insured relationship, and the other user modifies that policyholder A and the insured B do not have an insured relationship, and policyholder A and the insured C are in an insured relationship.

[0070] Specifically, when the version information of the graph node edited by the first user is inconsistent with the version information of the graph node in the current knowledge graph, the large language model can be used to identify the editing data when the first user edits the graph node in the knowledge graph and the node data of the graph node edited by the first user to determine the type of editing of the graph node by the first user, that is, the first editing type; the large language model can be used to identify the node data of the graph node edited by the first user (that is, the content of the graph node in the knowledge graph when the first user initiates the editing operation) and the node data of the graph node in the current knowledge graph (that is, the content of the graph node in the current knowledge graph) to determine the type of editing of the graph node by other users, that is, the second editing type. Based on the first editing type and the second editing type, it can be determined whether there is an attribute conflict type or a relationship conflict type when multiple users edit the same graph node.

[0071] It is understandable that after detecting the existence of a knowledge conflict type, the conflict can be marked, and different knowledge conflict types correspond to different marks, so that users can quickly determine the editing problems in the knowledge graph. In addition, the embodiment of the present application can store the conflict event information corresponding to the knowledge conflict type, for example, the attribute information of the first user, the time information edited by the first user, the node data of the graph node edited by the first user, the editing data, and the node data of the graph node in the current knowledge graph, to help users view the update record of the graph node.

[0072] Furthermore, embodiments of the present application can record every knowledge update in the knowledge graph through blockchain technology. Specifically, all edits corresponding to graph nodes in the knowledge graph can be synchronized to a consortium chain or private chain, so that every edit made by a user to a graph node generates an on-chain transaction record, for example, recording the user's identity information, time information, and other data, to ensure that the content in the knowledge graph is not tampered with.

[0073] In an exemplary embodiment, the method further includes step S301 and step S302.

[0074] Step S301: Obtain editing operations of multiple users on the knowledge graph.

[0075] Step S302: When it is detected that at least two users initiate editing operations on the same graph node in the knowledge graph, the editing status of the graph node is displayed.

[0076] For example, embodiments of the present application can obtain multiple users' editing operations on the knowledge graph to monitor each graph node in the knowledge graph. If at least two users are detected to have initiated editing operations on the same graph node in the knowledge graph, the editing status of the graph node will be displayed to prompt users that there is collaborative editing, avoiding frequent knowledge conflicts, thereby improving the transparency of multiple users editing the knowledge graph simultaneously.

[0077] In an exemplary embodiment, the knowledge graph includes multiple sub-graphs, and each sub-graph includes different skill knowledge; the method also includes steps S401 and S402.

[0078] Step S401: According to the attribute information of the user to be recommended, obtain at least one target sub-graph corresponding to the attribute information of the user to be recommended from the knowledge graph.

[0079] Step S402: Push at least one target sub-graph to a terminal device to be used by the user to be recommended.

[0080] For enterprises, a knowledge graph can be constructed based on the business data related to the enterprise, wherein the knowledge graph can include multiple sub-graphs of skill knowledge. In actual applications, the sub-graph corresponding to the skill knowledge that the recommended user needs to master can be pushed based on the attribute information such as the department, position, and rank of the recommended user. For example, a database engineering intern needs to master the use of the database platform; a junior database engineer needs to have the ability to review script problems; a senior database engineer needs to be able to locate the cause of the script execution error and provide repair suggestions; a senior database engineer needs to be able to combine the business and help users with database selection and business model construction. Specifically, a model corresponding to attribute information such as position and rank can be established first, and the knowledge graph can be associated with the model. Then, based on the attribute information such as the position and rank of the recommended user, at least one sub-graph and the next level of sub-graph that the user needs to master can be displayed.

[0081] Taking the medical field as an example, a knowledge graph can include knowledge graphs related to medical care. Each knowledge graph can include multiple sub-graphs, each containing different nursing knowledge. Different medical staff require different nursing knowledge. Based on this, at least one target sub-graph suitable for the user to be recommended can be obtained from the medical care-related knowledge graph based on attribute information such as the medical staff's department, position, and scope of work. This at least one target sub-graph can be pushed to the terminal device used by the user to be recommended, thereby scientifically training medical staff and improving their capabilities in a short period of time.

[0082] See also Figure 3 , Figure 3 A schematic block diagram of a knowledge graph processing device provided in an embodiment of the present application. The knowledge graph processing device can be configured in a server or terminal device to execute the aforementioned knowledge graph processing method.

[0083] like Figure 3 As shown, the knowledge graph processing device includes: a first acquisition module 110, a second acquisition module 120, a comparison module 130 and an editing module 140.

[0084] The first acquisition module 110 is used to obtain a preset knowledge graph, which includes graph nodes.

[0085] The second acquisition module 120 is used to obtain the editing data when the first user edits the graph node in the knowledge graph, and the version information of the graph node edited by the first user.

[0086] The comparison module 130 is used to compare the version information of the graph node edited by the first user with the version information of the graph node in the current knowledge graph.

[0087] The editing module 140 is used to determine whether to edit the graph node in the knowledge graph according to the editing data based on the comparison result of the version information of the graph node edited by the first user and the version information of the graph node in the current knowledge graph.

[0088] In an exemplary embodiment, the editing module 140 includes a first editing submodule and a second editing submodule.

[0089] The first editing submodule is used to edit the graph node in the knowledge graph according to the editing data when the version information of the graph node edited by the first user is consistent with the version information of the graph node in the current knowledge graph.

[0090] The second editing sub-module is used to determine the target editing data based on the editing data and the node data of the graph node in the current knowledge graph when the version information of the graph node edited by the first user is inconsistent with the version information of the graph node in the current knowledge graph, and edit the graph node in the knowledge graph according to the target editing data.

[0091] In an exemplary embodiment, the apparatus further comprises a modification module.

[0092] The modification module is used to modify the version information of the graph node based on the version information of the graph node in the current knowledge graph.

[0093] In an exemplary embodiment, the second editing submodule includes a display submodule and a selection submodule.

[0094] The display submodule is used to display the edited data and the node data of the graph nodes in the current knowledge graph in the human-computer interaction interface.

[0095] The selection submodule is used to determine target editing data in response to the user's selection operation on editing data and node data on the human-computer interaction interface.

[0096] In an exemplary embodiment, the apparatus further includes a first type determination module, a second type determination module, a conflict type determination module, and a storage module.

[0097] The first type determination module is used to determine the first editing type based on the editing data when the first user edits the graph node in the knowledge graph, and the node data of the graph node edited by the first user.

[0098] The second type determination module is used to determine the second editing type based on the node data of the graph node edited by the first user and the node data of the graph node in the current knowledge graph.

[0099] The conflict type determination module is used to determine the knowledge conflict type corresponding to the edited data according to the first edit type and the second edit type.

[0100] The storage module is used to store conflict event information corresponding to the knowledge conflict type. The conflict event information includes the attribute information of the first user, the time information edited by the first user, the node data of the graph node edited by the first user, the editing data, and the node data of the graph node in the current knowledge graph.

[0101] In an exemplary embodiment, the device further includes a third acquisition module and a status display module.

[0102] The third acquisition module is used to obtain the editing operations of multiple users on the knowledge graph.

[0103] The status display module is used to display the editing status of the graph node when it detects that at least two users initiate editing operations on the same graph node in the knowledge graph.

[0104] In an exemplary embodiment, the knowledge graph includes multiple sub-graphs, and each sub-graph includes different skill knowledge; the device also includes a fourth acquisition module and a push module.

[0105] The fourth acquisition module is used to obtain at least one target sub-graph corresponding to the attribute information of the user to be recommended from the knowledge graph based on the attribute information of the user to be recommended.

[0106] The push module is used to push at least one target sub-graph to the terminal device used by the user to be recommended.

[0107] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0108] The method of the present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0109] Illustratively, the above-mentioned method and apparatus may be implemented in the form of a computer program, which may be run on a computer device.

[0110] See also Figure 4 , Figure 4 This is a schematic block diagram of the structure of a computer device provided in an embodiment of the present application. The computer device can be a server or a terminal device.

[0111] like Figure 4 As shown, the computer device includes a processor, a memory, and a network interface connected through a system bus, wherein the memory may include a storage medium and an internal memory.

[0112] The storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause the processor to perform the steps of any one of the knowledge graph processing methods.

[0113] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0114] The internal memory provides an environment for the operation of the computer program in the storage medium. When the computer program is executed by the processor, the processor can execute the steps of any knowledge graph processing method.

[0115] This network interface is used for network communication, such as sending assigned tasks.

[0116] Those skilled in the art will understand that Figure 4 The 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 shown in the figure, or combine certain components, or have a different component arrangement.

[0117] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0118] In one embodiment, the processor is used to execute a computer program and can implement the following steps when executing the computer program:

[0119] Obtain a preset knowledge graph, which includes graph nodes;

[0120] Obtaining editing data when the first user edits a graph node in the knowledge graph, and version information of the graph node edited by the first user;

[0121] Comparing the version information of the graph node edited by the first user with the version information of the graph node in the current knowledge graph;

[0122] Based on the comparison result of the version information of the graph node edited by the first user and the version information of the graph node in the current knowledge graph, determine whether to edit the graph node in the knowledge graph according to the editing data.

[0123] It should be noted that technical personnel in the relevant field can clearly understand that for the convenience and conciseness of description, the specific processing process of the knowledge graph described above can refer to the corresponding process in the embodiment of the aforementioned knowledge graph processing method, and will not be repeated here.

[0124] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the following steps can be implemented:

[0125] Obtain a preset knowledge graph, which includes graph nodes;

[0126] Obtaining editing data when the first user edits a graph node in the knowledge graph, and version information of the graph node edited by the first user;

[0127] Comparing the version information of the graph node edited by the first user with the version information of the graph node in the current knowledge graph;

[0128] Based on the comparison result of the version information of the graph node edited by the first user and the version information of the graph node in the current knowledge graph, determine whether to edit the graph node in the knowledge graph according to the editing data.

[0129] The computer-readable storage medium may be an internal storage unit of the computer device in the aforementioned embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the computer device.

[0130] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium can be referred to the embodiments of the aforementioned knowledge graph processing method.

[0131] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. 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 memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0132] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0133] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for processing a knowledge graph, characterized in that: include: Obtaining a preset knowledge graph, wherein the knowledge graph includes graph nodes; Obtaining editing data when a first user edits a graph node in the knowledge graph, and version information of the graph node edited by the first user; Comparing the version information of the graph node edited by the first user with the version information of the graph node in the current knowledge graph; Based on the comparison result of the version information of the graph node edited by the first user and the version information of the graph node in the current knowledge graph, determine whether to edit the graph node in the knowledge graph according to the editing data.

2. The method for processing a knowledge graph according to claim 1, wherein: The determining whether to edit the graph node in the knowledge graph according to the edit data based on a comparison result of the version information of the graph node edited by the first user and the version information of the graph node in the current knowledge graph includes: When the version information of the graph node edited by the first user is consistent with the version information of the graph node in the current knowledge graph, editing the graph node in the knowledge graph according to the edit data; When the version information of the graph node edited by the first user is inconsistent with the version information of the graph node in the current knowledge graph, the target editing data is determined based on the editing data and the node data of the graph node in the current knowledge graph, and the graph node in the knowledge graph is edited based on the target editing data.

3. The method for processing a knowledge graph according to claim 1, wherein: After editing the graph node in the knowledge graph, the method further includes: Modify the version information of the graph node based on the version information of the graph node in the current knowledge graph.

4. The method for processing a knowledge graph according to claim 2, wherein: The determining target editing data according to the editing data and the node data of the graph node in the current knowledge graph includes: Displaying the edited data and the node data of the graph nodes in the current knowledge graph on a human-computer interaction interface; In response to a user's selection operation on the editing data and the node data on the human-computer interaction interface, target editing data is determined.

5. The method for processing a knowledge graph according to claim 2, wherein: Before determining target editing data based on the editing data and the node data of the graph node in the current knowledge graph, and editing the graph node in the knowledge graph based on the target editing data, the method further includes: Determining a first editing type according to editing data of the graph node in the knowledge graph performed by the first user and node data of the graph node edited by the first user; Determining a second edit type according to the node data of the graph node edited by the first user and the node data of the graph node in the current knowledge graph; Determining a knowledge conflict type corresponding to the edited data according to the first edit type and the second edit type; The conflict event information corresponding to the knowledge conflict type is stored, and the conflict event information includes the attribute information of the first user, the time information edited by the first user, the node data of the graph node edited by the first user, the editing data and the node data of the graph node in the current knowledge graph.

6. The method for processing a knowledge graph according to any one of claims 1 to 5, characterized in that: The method further comprises: Obtaining editing operations of multiple users on the knowledge graph; When it is detected that at least two users initiate editing operations on the same graph node in the knowledge graph, the editing status of the graph node is displayed.

7. The method for processing a knowledge graph according to any one of claims 1 to 5, characterized in that: The knowledge graph includes a plurality of sub-graphs, each of which includes different skill knowledge; and the method further includes: According to the attribute information of the user to be recommended, obtaining at least one target subgraph corresponding to the attribute information of the user to be recommended from the knowledge graph; Push the at least one target sub-graph to the terminal device used by the user to be recommended.

8. A knowledge graph processing device, characterized in that: include: A first acquisition module is used to acquire a preset knowledge graph, where the knowledge graph includes graph nodes; A second acquisition module is used to obtain editing data when a first user edits a graph node in the knowledge graph, and version information of the graph node edited by the first user; A comparison module, configured to compare the version information of the graph node edited by the first user with the version information of the graph node in the current knowledge graph; An editing module is used to determine whether to edit the graph node in the knowledge graph according to the editing data based on the comparison result of the version information of the graph node edited by the first user and the version information of the graph node in the current knowledge graph.

9. A computer device, characterized in that: The computer device includes a memory and a processor; The memory is used to store computer programs; The processor is used to execute the computer program and implement the knowledge graph processing method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the knowledge graph processing method as described in any one of claims 1 to 7 are implemented.

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