CN113220907A - Business knowledge graph construction method and device, medium and electronic equipment - Google Patents
Business knowledge graph construction method and device, medium and electronic equipment Download PDFInfo
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Abstract
The present disclosure provides a method of constructing a business knowledge graph, a device for constructing a business knowledge graph, a computer-readable medium, and an electronic device; relates to the technical field of data processing. The construction method of the service knowledge graph comprises the following steps: acquiring a service log corresponding to a target service, and constructing a basic service chain corresponding to the target service according to the service log; and aggregating the basic service chains based on the same service node contained in each basic service chain to obtain a service knowledge graph. The method extracts the service knowledge system from related knowledge islands such as continuous service logs, scattered service logs, error service logs and the like through a basic service chain, and performs fine classification and combination to obtain the service knowledge graph with strong relevance and inclusion. And the service data storage is carried out based on the service knowledge map, which is beneficial to classifying and storing the service knowledge so as to read and query the corresponding service knowledge.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for constructing a business knowledge graph, a computer-readable medium, and an electronic device.
Background
Data mining refers to a nontrivial process of revealing implicit, previously unknown and potentially valuable information from a large amount of data in a database, and is mainly based on artificial intelligence, machine learning, pattern recognition, statistics, databases, visualization technologies and the like, and analyzes enterprise data in a highly automated manner to make inductive reasoning. For example, in the related art, a knowledge graph indicating entity relationships may be formed by mirroring entity relationship mining in data.
In the existing business processing process, business knowledge is generally streamed by means of document retention, oral transmission and the like. However, the business of a company cannot be inherited by oral transmission, and although the knowledge learning problem can be solved by the retained documents, the documents cannot track the current flow of the system in real time, and serious errors can be caused if the documents are not updated in time.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The purpose of the present disclosure is to provide a business knowledge graph construction method, a business knowledge graph construction apparatus, a computer readable medium, and an electronic device, which can obtain a business knowledge system with a stronger association by concatenating business-related knowledge through a business knowledge graph.
According to a first aspect of the present disclosure, there is provided a method for constructing a business knowledge graph, including: acquiring a service log corresponding to a target service, and constructing a basic service chain corresponding to the target service according to the service log; and aggregating the basic service chains based on the same service node contained in each basic service chain to obtain a service knowledge graph.
Optionally, obtaining a service log corresponding to the target service includes: grouping and analyzing the service flow based on the user identification to obtain a service log corresponding to at least one user identification; and searching the initiation time of the target service in the service log corresponding to each user identifier, and extracting the service log corresponding to the target service based on the initiation time.
Optionally, constructing a basic service chain corresponding to the target service according to the service log includes: constructing a service execution chain corresponding to the target service based on the service log; and performing chain segmentation on the service execution chain to obtain a basic service chain corresponding to at least one target service.
Optionally, the building of the service execution chain corresponding to the target service based on the service log includes: grouping service logs corresponding to a target service based on the user identification to obtain at least one service log corresponding to the user identification; determining a group of node data contained in the service log aiming at the service log corresponding to each user identifier; and constructing a service execution chain corresponding to the target service based on each group of node data.
Optionally, the node data includes a service node and a trigger sequence of the service node; establishing a service execution chain corresponding to the target service based on each group of node data, wherein the method comprises the following steps: clustering the node data according to service nodes included in the node data to obtain at least one type of node data; determining a triggering position corresponding to a service node based on a triggering sequence of the service node contained in the node data aiming at each type of node data; and constructing a service execution chain corresponding to the target service based on the service nodes contained in each type of service data and the trigger positions corresponding to the service nodes.
Optionally, the chain segmentation is performed on the service execution chain to obtain a basic service chain corresponding to at least one target service, including: and performing chain segmentation on the service execution chain by taking a preset service node as a segmentation node to obtain at least one basic service chain.
Optionally, after obtaining the service log corresponding to the target service, the method for constructing the service knowledge graph further includes: processing data of the service log; the data processing comprises at least one of the following processing procedures: and carrying out data deduplication on the service log, deleting error data in the service log, and completely filling incomplete data in the service log.
According to a second aspect of the present disclosure, there is provided a service knowledge graph constructing apparatus, including: the chain construction module is used for acquiring a service log corresponding to the target service and constructing a basic service chain corresponding to the target service according to the service log; and the map construction module is used for aggregating the basic service chains based on the same service node contained in each basic service chain to obtain the service knowledge map.
According to a third aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory for storing executable instructions for the processor; wherein the processor is configured to perform the method of any of the above via execution of the executable instructions.
According to a fourth aspect of the disclosure, there is provided a computer readable medium having stored thereon a computer program which, when executed by a processor, implements the method of any one of the above.
Exemplary embodiments of the present disclosure may have some or all of the following benefits:
in the method for constructing a service knowledge graph provided in an exemplary embodiment of the present disclosure, a service knowledge graph corresponding to a target service may be obtained by obtaining a service log corresponding to the target service, then constructing a basic service chain corresponding to the target service according to the service log, and finally aggregating the basic service chains according to the same service node included between the basic service chains. The method can extract the service knowledge system from related knowledge islands such as continuous service logs, scattered service logs, error service logs and the like through the basic service chain, and perform fine classification and combination to obtain the service knowledge graph with strong relevance and inclusion. And the service data storage is carried out based on the service knowledge map, which is beneficial to classifying and storing the service knowledge so as to read and query the corresponding service knowledge.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
FIG. 1 is a diagram illustrating an exemplary system architecture to which an image fusion method and apparatus of the disclosed embodiments may be applied;
FIG. 2 illustrates a schematic structural diagram of a computer system suitable for use with the electronic device used to implement embodiments of the present disclosure;
FIG. 3 schematically illustrates a flow chart of a method of building a business knowledge graph in an exemplary embodiment of the disclosure;
FIG. 4 schematically illustrates a schematic view of a business chain in an exemplary embodiment of the disclosure;
FIG. 5 schematically illustrates a schematic view of another business chain in an exemplary embodiment of the disclosure;
fig. 6 is a flowchart schematically illustrating a method for obtaining a service log corresponding to a target service in an exemplary embodiment of the disclosure;
FIG. 7 schematically illustrates a traffic flow diagram in an exemplary embodiment of the disclosure;
FIG. 8 schematically illustrates a flow chart of a method of building a base business chain in an exemplary embodiment of the disclosure;
FIG. 9 schematically illustrates a business knowledge graph in an exemplary embodiment of the disclosure;
FIG. 10 schematically illustrates another business knowledge graph in an exemplary embodiment of the disclosure;
FIG. 11 schematically illustrates a global knowledge-graph in an exemplary embodiment of the present disclosure;
FIG. 12 schematically illustrates a business system architecture flow diagram in an exemplary embodiment of the disclosure;
fig. 13 schematically shows a composition diagram of a business knowledge graph constructing apparatus in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
Fig. 1 is a schematic diagram illustrating a system architecture of an exemplary application environment to which a method and apparatus for building a business knowledge graph according to an embodiment of the present disclosure may be applied.
As shown in fig. 1, the system architecture 100 may include one or more of terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few. The terminal devices 101, 102, 103 may be various electronic devices having a display screen, including but not limited to desktop computers, portable computers, smart phones, tablet computers, and the like. It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. For example, server 105 may be a server cluster comprised of multiple servers, or the like.
The method for constructing the service knowledge graph provided by the embodiment of the present disclosure is generally executed by the server 105, and accordingly, the device for constructing the service knowledge graph is generally disposed in the server 105. However, it is easily understood by those skilled in the art that the method for constructing the service knowledge graph provided in the embodiment of the present disclosure may also be executed by the terminal devices 101, 102, and 103, and accordingly, the device for constructing the service knowledge graph may also be disposed in the terminal devices 101, 102, and 103, which is not particularly limited in this exemplary embodiment. For example, in an exemplary embodiment, a user may upload a service log to the server 105 through the terminal devices 101, 102, and 103, and the server 105 constructs a service knowledge graph through the method for constructing a service knowledge graph provided in the embodiments of the present disclosure, so that when the terminal devices 101, 102, and 103 perform query, the server 105 may query corresponding results through the service knowledge graph and feed back the query results to the terminal devices 101, 102, and 103.
FIG. 2 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present disclosure.
It should be noted that the computer system 200 of the electronic device shown in fig. 2 is only an example, and should not bring any limitation to the functions and the scope of the application of the embodiments of the present disclosure.
As shown in fig. 2, the computer system 200 includes a Central Processing Unit (CPU)201 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)202 or a program loaded from a storage section 208 into a Random Access Memory (RAM) 203. In the RAM 203, various programs and data necessary for system operation are also stored. The CPU 201, ROM 202, and RAM 203 are connected to each other via a bus 204. An input/output (I/O) interface 205 is also connected to bus 204.
The following components are connected to the I/O interface 205: an input portion 206 including a keyboard, a mouse, and the like; an output section 207 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 208 including a hard disk and the like; and a communication section 209 including a network interface card such as a LAN card, a modem, or the like. The communication section 209 performs communication processing via a network such as the internet. A drive 210 is also connected to the I/O interface 205 as needed. A removable medium 211 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 210 as necessary, so that a computer program read out therefrom is mounted into the storage section 208 as necessary.
In particular, the processes described below with reference to the flowcharts may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 209 and/or installed from the removable medium 211. The computer program, when executed by a Central Processing Unit (CPU)201, performs various functions defined in the methods and apparatus of the present application. In some embodiments, the computer system 200 may further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.
It should be noted that the computer readable media shown in the present disclosure may be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method in the embodiments described below. For example, the electronic device may implement the various steps shown in fig. 3, 6, 8, etc.
The technical solution of the embodiment of the present disclosure is explained in detail below:
based on one or more of the problems described above, the present example embodiment provides a method for constructing a business knowledge graph. The method for constructing the service knowledge graph may be applied to the server 105, and may also be applied to one or more of the terminal devices 101, 102, and 103, which is not particularly limited in this exemplary embodiment. Referring to fig. 3, the method for constructing the business knowledge graph may include the following steps S310 and S320:
step S310, acquiring a service log corresponding to the target service, and constructing a basic service chain corresponding to the target service according to the service log;
step S320, aggregating the basic service chains based on the same service node included in each basic service chain, to obtain a service knowledge graph.
In the method for constructing a service knowledge graph provided by the exemplary embodiment, a service knowledge system can be extracted from related knowledge islands such as continuous service logs, scattered service logs and error service logs through a basic service chain, and is subjected to fine classification and combination, so that a service knowledge graph with strong relevance and inclusion is obtained. And the service data storage is carried out based on the service knowledge map, which is beneficial to classifying and storing the service knowledge so as to read and query the corresponding service knowledge. Under the condition, non-target business related personnel can inquire the business in a cross-domain mode, and the condition that the manpower and material resources are wasted due to the fact that the target business related personnel need to inquire and explain when the target business related business knowledge is inquired is avoided.
The above steps of the present exemplary embodiment will be described in more detail below.
In step S310, a service log corresponding to the target service is obtained, and a basic service chain corresponding to the target service is constructed according to the service log.
The service refers to a target to be completed by a user through operation of the system. For example, a loan transaction that the user implements over a network system; the service log corresponding to the target service is a log used for recording specific conditions of executing the target service, and the log may include all execution information related to the target service execution, such as time for executing the target service, specific service processes performed during execution, and the like. The basic service chain corresponding to the target service is the shortest chain for the service system to finish or restart execution when the service system executes the target service.
For example, referring to the service chain shown in fig. 4, the service system may end the execution of the service through 4 nodes of service start, service admission, successful admission, and abnormal admission; the service system can also finish the execution of the service by 5 nodes of service start, service admission, successful admission, normal service and service end; the service system can also finish the execution of the service through service start, service admission, admission success, admission failure and 5 nodes visited by the client. Thus, a service chain as in fig. 4 may comprise the 3 basic service chains described above. For another example, assuming that the predetermined node a is a traffic start node, refer to the traffic chain a → B → C → D → a → B → E shown in fig. 5, wherein the sub-chain a → B → C → D can make the traffic resume execution (i.e. the traffic start node a is re-executed), and the sub-chain a → B → E can end the execution of the traffic, so that the sub-chain a → B → C → D and the sub-chain a → B → E are both basic traffic chains.
In an exemplary embodiment, after a certain user initiates a target service, within a period of time after the initiation time, service nodes correspondingly executed by the user in a service system generally all belong to a flow corresponding to the target service. Therefore, when acquiring the service log corresponding to the target service, as shown in fig. 6, the following steps S610 and S620 may be included:
in step S610, the service flow is grouped and analyzed based on the user identifier to obtain a service log corresponding to at least one user identifier.
In an exemplary embodiment, after a certain user initiates a target service, when a service system executes the target service, a service flow usually carries a user identifier. For example. Referring to fig. 7, in the service flow, a service flow a, a service flow B, a service flow C, and a service flow D at a certain time are executed by a user, and these service flows simultaneously include service flows and execution information of the service flows, and these execution information may have a user id corresponding to a user who calls the service flow.
Based on this, in order to obtain a service log of each service initiated by a certain user, service flow can be grouped based on the user identifier, and all data contained in the service flow is classified to obtain the service flow corresponding to each user identifier. Then, the service flow corresponding to each user identifier can be analyzed to obtain a corresponding service log.
In step S620, the initiation time of the target service is searched in the service log corresponding to each user identifier, and the service log corresponding to the target service is extracted based on the initiation time.
In an exemplary embodiment, after the service log corresponding to each user identifier is obtained, since after a user triggers a certain service, the service logs corresponding to the user identifiers of the user are generally corresponding service logs when the service is triggered within a period of time after the triggering. Therefore, the initiation time of the target service can be respectively searched in the service log corresponding to each user identifier, and then the service log in the preset time period is extracted from the initiation time as a starting point to serve as the service log corresponding to the target service.
In an exemplary embodiment, after the service log corresponding to the target service is obtained, a basic service chain corresponding to the target service may be constructed according to the service log. It should be noted that, when a target service is executed, various situations, such as automatic re-triggering after a certain service fails to be executed, may occur. Therefore, the chain generated directly based on the traffic log is likely not the underlying traffic chain. In this case, referring to fig. 8, the basic service chain may be constructed by the following steps S810 and S820:
in step S810, a service execution chain corresponding to the target service is constructed based on the service log.
In an exemplary embodiment, after the service log is obtained, a service execution chain when the service is executed may be constructed according to the service log. The service execution chain corresponding to the target service may include service nodes sequentially triggered in the service system according to a sequence when the service system executes the target service. For example, referring to fig. 5, the chain of service execution of a certain target service is shown as a → B → C → D → a → B → E.
In an exemplary embodiment, when a service execution chain corresponding to a target service is constructed based on a service log, service logs corresponding to the target service may be grouped based on a user identifier to obtain a service log corresponding to at least one user identifier. And then, for the service log corresponding to each user identifier, determining node data contained in the service log, and then constructing a service execution chain corresponding to the target service according to the node data. The node data included in the service log may generally include service nodes triggered by the service system when the service is executed, time for triggering each service node, attribute data of the service node, and the like.
In an exemplary embodiment, the node data may include the service node and the trigger order of the service node. Because the service execution chains may be different under different conditions during service execution, clustering may be performed according to the service nodes included in each group of node data, and each group of node data including the same service node may be classified into one class, so as to obtain at least one class of node data. Then, for each type of node data, the triggering positions of the service nodes can be determined according to the triggering sequence of the service nodes contained in the node data, and then a service execution chain corresponding to the target service is constructed according to the determined triggering positions of the service nodes contained in each type of node data.
When the trigger position of the service node is determined, since service logs generated by some service pipelines are not completely ordered, unordered data may exist. Thus, in determining the trigger position of a service node, the determination may be based on a large amount of service data. Specifically, since the service nodes included in the same type of node data are the same, the triggering position of the service node in the service execution chain can be determined according to the triggering sequence of each service node in each group of node data in the same type of node data.
For example, assume that a class of node data includes a total of 5 sets of node data, each set of node data including 26 service nodes, service node a, service node B, and service node C …, service node Z. The triggering sequence of the service node C in the 5 groups of node data is respectively 22 th bit, 23 th bit, 22 nd bit and 22 nd bit. At this time, the trigger position may be determined by calculating the variance and standard deviation of the order of the service node C and according to the magnitudes of the variance and standard deviation. Specifically, in the above example, based on 22, 23, 22, and 22, a variance may be calculated to be 0.16, and a standard deviation may be calculated to be 0.4, and if both the variance and the standard deviation are smaller than a preset value, it may be considered that the data quality is better, and therefore, the mode 22 may be taken as the final trigger position; on the contrary, if the triggering sequence is 21, 19, 32, 21, 44, the calculated variance and standard deviation are 89.84 and 9.48, respectively, and if the variance and standard deviation are greater than the preset value, the data quality may be considered to be poor, and therefore, the data needs to be discarded, and other data is selected to recalculate the triggering position of the service node.
It should be noted that, if the service system changes, that is, the service nodes are increased or decreased during the execution of the target service, the sequence of the whole service chain changes. However, since the data volume of the service log is usually very large, the standard deviation and variance change of the data are small even after moving, and therefore the trigger position can be determined by means of the variance and the standard deviation.
In step S820, a chain of service execution chains is divided to obtain a basic service chain corresponding to at least one target service.
In an exemplary embodiment, as mentioned above, the chain directly generated based on the service log is likely not the basic service chain, and thus after the service execution chain is obtained, the service execution chain may be segmented to obtain the basic service chain corresponding to the at least one target service. Specifically, a preset service node may be set in advance for the target service, and when the preset service node appears in the basic service chain, the preset service node is used as a partition node to perform chain partition on the service execution chain, so as to obtain at least one basic service chain.
The preset service node may be a start node and/or an end node. Specifically, when the preset service node is a start node, the start node may be used as a partition node, and the partition may be performed between the start node and a previous node. For example, where the starting node is a, the traffic may be divided between traffic node a and its preceding traffic node D, resulting in a base traffic chain a → B → C → D and a base traffic chain a → B → E; when the preset service node is the end node, the end node may be used as a partition node, and the partition may be performed between the end node and a subsequent node. For example, the end nodes may be set as D and E, and a chain may be performed for the above-mentioned traffic, that is, a partition may be performed between the traffic node D and the traffic node a following the traffic node D, and a partition may be performed after the traffic node E. In addition, the start node and the service node may be set at the same time, and the division may be performed from before the start node and after the end node.
In step S320, each basic service chain is aggregated based on the same service node included in each basic service chain, so as to obtain a service knowledge graph.
In an exemplary embodiment, after obtaining the basic service chain, each basic service chain may be aggregated based on the same service node included in the basic service chain, so as to obtain the service knowledge graph. During aggregation, the same service nodes included in the basic service chain may be merged into one, and the rest of the service nodes are taken as branches to generate a service knowledge graph. For example, after the service execution chain shown in fig. 5 is divided into the basic service chain a → B → C → D and the basic service chain a → B → E, the basic service chain a → B → C → D and the basic service chain a → B → E may be aggregated to obtain the service knowledge map shown in fig. 9.
It should be noted that, in the actual operation process, the target service may include many sub-services, and the corresponding service knowledge graph may be very complex to obtain. For example, as shown in the service knowledge graph of fig. 10, a certain target service includes a plurality of sub-services, and a basic service chain corresponding to the plurality of sub-services is aggregated, so that a more complex service knowledge graph can be obtained.
In addition, in an exemplary embodiment, in order to better show the service knowledge, the service nodes triggered when the service system executes the service flow may be monitored to obtain the attribute information of each service node. The attribute information may include basic information, usage information, and the like of the service node. For example, information of a node name of a service node, a node role, a node use frequency, whether an associated node exists, and the like may be included. Upon obtaining the attribute information, the service node may be energized so that the attribute information may be exposed in a service knowledge graph. For example, the attribute information may be marked on the service node, and the corresponding attribute information is presented when the user clicks or moves a cursor to the service node.
In an exemplary embodiment, there may be a problem of poor quality of service data, so after obtaining the service log, the service log may be subjected to data processing. Specifically, the processes of data cleaning, data extraction, data alignment and the like can be performed. Through the data processing process, data duplication removal can be carried out on the service log, error data in the service log are deleted, and incomplete data in the service log are completely filled.
In addition, in an exemplary embodiment, after a service knowledge graph corresponding to a target service is obtained, there may be an association relationship between another target service and the target service, and therefore, knowledge graphs corresponding to multiple target services may be combined into a global knowledge graph according to the association relationship. For example, a global knowledge graph, such as that shown in fig. 11, may include a plurality of target services having an association relationship. By clicking the node corresponding to each target service, the service knowledge graph corresponding to the target service can be opened or hidden.
It should be noted that after the service knowledge graph is obtained, as the service data in the service system continuously increases, the service knowledge graph can be updated and synchronized in real time according to new service flow machines continuously increasing in the service system, so as to ensure the inheritance of the service system knowledge. For example, the service nodes needing to be processed in the service system can also be known through the service system architecture flow shown in fig. 12.
It should be noted that although the various steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that these steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Further, in the present exemplary embodiment, an apparatus for constructing a service knowledge graph is also provided. The construction device of the service knowledge graph can be applied to a server or terminal equipment. Referring to fig. 13, the business knowledge graph building apparatus 1300 may include a chain building module 1310 and a graph building module 1320. Wherein:
a chain construction module 1310, configured to obtain a service log corresponding to a target service, and construct a basic service chain corresponding to the target service according to the service log;
the map building module 1320 is configured to aggregate the basic service chains based on the same service node included in each basic service chain, so as to obtain a service knowledge map.
In an exemplary embodiment, the chain construction module 1310 may be configured to group and parse the service flow based on the user identifier to obtain a service log corresponding to at least one user identifier; and searching the initiation time of the target service in the service log corresponding to each user identifier, and extracting the service log corresponding to the target service based on the initiation time.
In an exemplary embodiment, the chain construction module 1310 may be configured to construct a service execution chain corresponding to the target service based on the service log; and performing chain segmentation on the service execution chain to obtain a basic service chain corresponding to at least one target service.
In an exemplary embodiment, the chain construction module 1310 may be configured to group service logs corresponding to a target service based on a user identifier to obtain a service log corresponding to at least one user identifier; determining a group of node data contained in the service log aiming at the service log corresponding to each user identifier; and constructing a service execution chain corresponding to the target service based on each group of node data.
In an exemplary embodiment, the chain construction module 1310 may be configured to cluster node data according to service nodes included in the node data, so as to obtain at least one type of node data; determining a triggering position corresponding to a service node based on a triggering sequence of the service node contained in the node data aiming at each type of node data; and constructing a service execution chain corresponding to the target service based on the service nodes contained in each type of service data and the trigger positions corresponding to the service nodes.
In an exemplary embodiment, the chain construction module 1310 may be configured to perform chain division on the service execution chain by using a preset service node as a division node, so as to obtain at least one basic service chain.
In an exemplary embodiment, the chain construction module 1310 can be used for data processing of the service log; the data processing comprises at least one of the following processing procedures: and carrying out data deduplication on the service log, deleting error data in the service log, and completely filling incomplete data in the service log.
The specific details of each module or unit in the apparatus for constructing a service knowledge graph have been described in detail in the method for constructing a corresponding service knowledge graph, and therefore are not described herein again.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A method for constructing a business knowledge graph is characterized by comprising the following steps:
acquiring a service log corresponding to a target service, and constructing a basic service chain corresponding to the target service according to the service log;
and aggregating the basic service chains based on the same service node contained in the basic service chains to obtain a service knowledge graph.
2. The method of claim 1, wherein the obtaining the service log corresponding to the target service comprises:
grouping and analyzing the service flow based on the user identification to obtain a service log corresponding to at least one user identification;
and searching the initiation time of the target service in the service log corresponding to each user identifier, and extracting the service log corresponding to the target service based on the initiation time.
3. The method of claim 1, wherein the constructing a basic service chain corresponding to the target service according to the service log comprises:
constructing a service execution chain corresponding to the target service based on the service log;
and performing chain segmentation on the service execution chain to obtain at least one basic service chain corresponding to the target service.
4. The method of claim 3, wherein the building of the service execution chain corresponding to the target service based on the service log comprises:
grouping the service logs corresponding to the target service based on the user identification to obtain at least one service log corresponding to the user identification;
determining a group of node data contained in the service log aiming at the service log corresponding to each user identifier;
and constructing a service execution chain corresponding to the target service based on each group of the node data.
5. The method of claim 4, wherein the node data comprises a service node and a trigger order of the service node;
the building of the service execution chain corresponding to the target service based on each group of the node data includes:
clustering the node data according to service nodes included in the node data to obtain at least one type of node data;
for each type of node data, determining a trigger position corresponding to the service node based on a trigger sequence of the service node contained in the node data;
and constructing a service execution chain corresponding to the target service based on the service node contained in each type of service data and the trigger position corresponding to the service node.
6. The method of claim 3, wherein the performing chain segmentation on the service execution chain to obtain at least one basic service chain corresponding to the target service comprises:
and performing chain segmentation on the service execution chain by taking a preset service node as a segmentation node to obtain at least one basic service chain.
7. The method according to claim 1, wherein after the obtaining of the service log corresponding to the target service, the method further comprises:
processing data of the service log; the data processing comprises at least one of the following processing procedures: and carrying out data deduplication on the service log, deleting error data in the service log, and completely filling incomplete data in the service log.
8. An apparatus for building a business knowledge graph, comprising:
the chain construction module is used for acquiring a service log corresponding to a target service and constructing a basic service chain corresponding to the target service according to the service log;
and the map construction module is used for aggregating the basic service chains based on the same service node contained in the basic service chains to obtain a service knowledge map.
9. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1 to 7.
10. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the method of any of claims 1-7 via execution of the executable instructions.
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