CN113220907B - Construction method and device of business knowledge graph, medium and electronic equipment - Google Patents
Construction method and device of business knowledge graph, medium and electronic equipment Download PDFInfo
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Abstract
The disclosure provides a construction method of a business knowledge graph, a construction device of the business knowledge graph, a computer readable medium and electronic equipment; relates to the technical field of data processing. The construction method of the business 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 nodes contained in the basic service chains to obtain a service knowledge graph. According to the method, a business knowledge system is extracted from relevant knowledge islands such as continuous business logs, scattered business logs and error business logs through a basic business chain, fine classification and combination are carried out, and then a business knowledge map with strong relevance and strong inclusion is obtained. And the service data storage is carried out based on the service knowledge graph, so that the service knowledge can be classified and stored, and the corresponding service knowledge can be read and inquired conveniently.
Description
Technical Field
The disclosure relates to the technical field of data processing, and in particular relates to a construction method of a business knowledge graph, a construction device of the business knowledge graph, a computer readable medium and electronic equipment.
Background
Data mining refers to a nontrivial process of revealing implicit, previously unknown, and potentially valuable information from a vast amount of data in a database, which is based primarily on artificial intelligence, machine learning, pattern recognition, statistics, databases, visualization techniques, etc., to analyze enterprise data with high automation, making generalized inferences. For example, in the related art, a knowledge graph for 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, mouth-to-mouth transmission and the like. However, the mouth-to-mouth transmission cannot enable business of a company to be inherited, and the document can solve the knowledge learning problem while being kept, but the document cannot track the current flow of the system in real time, and if the document is not updated in time, serious errors are caused.
It should be noted that the information disclosed in the above background section is only for enhancing 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 disclosure aims to provide a construction method of a business knowledge graph, a construction device of the business knowledge graph, a computer readable medium and electronic equipment, wherein knowledge related to business can be connected in series through the business knowledge graph to obtain a business knowledge system with stronger relevance.
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 nodes contained in the basic service chains to obtain a service knowledge graph.
Optionally, obtaining a service log corresponding to the target service includes: grouping and analyzing the service flowing water based on the user identification to obtain a service log corresponding to at least one user identification; 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 carrying out chain segmentation on the service execution chain to obtain a basic service chain corresponding to at least one target service.
Optionally, constructing a service execution chain corresponding to the target service based on the service log includes: 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 the data of each group of nodes.
Optionally, the node data includes a service node and a trigger sequence of the service node; constructing a service execution chain corresponding to the target service based on the data of each group of nodes, comprising: 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 the service node based on the triggering sequence of the service node contained in the node data aiming at each type of the node data; and constructing a service execution chain corresponding to the target service based on the service node and the triggering position corresponding to the service node contained in each type of service data.
Optionally, chain segmentation is performed on the service execution chain to obtain at least one basic service chain corresponding to the target service, including: and carrying out chain segmentation on the service execution chain by taking the 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: carrying out data processing on the service log; the data processing includes at least one of the following processes: and performing data deduplication on the service log, deleting error data in the service log, and filling incomplete data in the service log completely.
According to a second aspect of the present disclosure, there is provided a device for constructing a business knowledge graph, 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 nodes contained in the basic service chains to obtain a 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 of the processor; wherein the processor is configured to perform the method of any of the above via execution of executable instructions.
According to a fourth aspect of the present disclosure, there is provided a computer readable medium having stored thereon a computer program which, when executed by a processor, implements a method of any of the above.
Exemplary embodiments of the present disclosure may have some or all of the following advantages:
in the method for constructing the service knowledge graph provided by the exemplary embodiment of the present disclosure, the service knowledge graph corresponding to the target service may be obtained by acquiring the service log corresponding to the target service, then constructing the basic service chains corresponding to the target service according to the service log, and finally aggregating the basic service chains according to the same service nodes included between the basic service chains. The business knowledge system can be extracted from relevant knowledge islands such as continuous business logs, scattered business logs and error business logs through a basic business chain, fine classification and combination are carried out, and then the business knowledge map with strong relevance and strong inclusion is obtained. And the service data storage is carried out based on the service knowledge graph, so that the service knowledge can be classified and stored, and the corresponding service knowledge can be read and inquired conveniently.
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 disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 illustrates a schematic diagram of an exemplary system architecture to which the image fusion method and apparatus of embodiments of the present disclosure may be applied;
FIG. 2 illustrates a schematic diagram of a computer system suitable for use in implementing embodiments of the present disclosure;
fig. 3 schematically illustrates a flowchart of a method for constructing a business knowledge graph in an exemplary embodiment of the present disclosure;
fig. 4 schematically illustrates a schematic diagram of a service chain in an exemplary embodiment of the present disclosure;
fig. 5 schematically illustrates a schematic diagram of another service chain in an exemplary embodiment of the present disclosure;
FIG. 6 schematically illustrates a flowchart of a method for obtaining a service log corresponding to a target service in an exemplary embodiment of the disclosure;
FIG. 7 schematically illustrates a business flow diagram in an exemplary embodiment of the present disclosure;
FIG. 8 schematically illustrates a flowchart of a method of constructing a basic business chain in an exemplary embodiment of the present disclosure;
fig. 9 schematically illustrates a business knowledge graph in an exemplary embodiment of the present disclosure;
FIG. 10 schematically illustrates another business knowledge graph in an exemplary embodiment of the present 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 present disclosure;
fig. 13 schematically illustrates a composition diagram of a construction apparatus of a business knowledge graph in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many 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 the 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 present disclosure. However, those skilled in the art will recognize that the aspects of the present disclosure may be practiced with one or more of the specific details, or with other methods, components, devices, steps, etc. 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 a repetitive description thereof 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 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 of a system architecture of an exemplary application environment to which a method and an apparatus for constructing 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 the terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others. The terminal devices 101, 102, 103 may be various electronic devices with display screens 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, the server 105 may be a server cluster formed by a plurality of servers.
The method for constructing a business knowledge graph provided in the embodiments of the present disclosure is generally executed by the server 105, and accordingly, the device for constructing a business 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 a service knowledge graph provided in the embodiment of the present disclosure may be performed by the terminal devices 101, 102, 103, and accordingly, the apparatus for constructing a service knowledge graph may also be provided in the terminal devices 101, 102, 103, which is not particularly limited in the present 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, 103, and the server 105 constructs a service knowledge graph through the method for constructing a service knowledge graph provided by the embodiment of the present disclosure, so that when the terminal devices 101, 102, 103, etc. perform a query, the server 105 may query a corresponding result through the service knowledge graph, and feedback the query result to the terminal devices 101, 102, 103, etc.
Fig. 2 shows a schematic diagram of a computer system suitable for use in implementing embodiments 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 impose any limitation on the functions and the application scope of the embodiments of the present disclosure.
As shown in fig. 2, the computer system 200 includes a Central Processing Unit (CPU) 201, which can perform various appropriate actions and processes according to 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 required for the system operation are also stored. The CPU 201, ROM 202, and RAM 203 are connected to each other through 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 section 206 including a keyboard, a mouse, and the like; an output portion 207 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage section 208 including a hard disk or the like; and a communication section 209 including a network interface card such as a LAN card, a modem, and the like. The communication section 209 performs communication processing via a network such as the internet. The 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 installed on the drive 210 as needed, so that a computer program read out therefrom is installed into the storage section 208 as needed.
In particular, according to embodiments of the present disclosure, the processes described below with reference to flowcharts may be implemented as computer software programs. 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 shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 209, and/or installed from the removable medium 211. The computer program, when executed by a Central Processing Unit (CPU) 201, performs the various functions defined in the methods and apparatus of the present application. In some embodiments, the computer system 200 may also include an AI (Artificial Intelligence ) processor for processing computing operations related to machine learning.
It should be noted that the computer readable medium shown in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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 context of this 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 the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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 flowcharts 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 involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
As another aspect, the present application also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer-readable medium carries one or more programs which, when executed by one of the electronic devices, cause the electronic device to implement the methods in the embodiments described below. For example, the electronic device may implement the various steps shown in fig. 3, 6, 8, etc.
The following describes the technical scheme of the embodiments of the present disclosure in detail:
based on one or more of the above problems, the present exemplary 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, or may be applied to one or more of the terminal devices 101, 102, 103, which is not particularly limited in this exemplary embodiment. Referring to fig. 3, the construction method of the business knowledge graph may include the following steps S310 and S320:
step S310, obtaining a service log corresponding to a target service, and constructing a basic service chain corresponding to the target service according to the service log;
step S320, the basic service chains are aggregated based on the same service nodes contained in the basic service chains, and a service knowledge graph is obtained.
In the construction method of the business knowledge graph provided by the present exemplary embodiment, the business knowledge system may be extracted from the relevant knowledge islands such as continuous business logs, scattered business logs, and error business logs through the basic business chain, and fine classification and combination are performed, so as to obtain the business knowledge graph with strong relevance and strong inclusion. And the service data storage is carried out based on the service knowledge graph, so that the service knowledge can be classified and stored, and the corresponding service knowledge can be read and inquired conveniently. In this case, the non-target service related personnel can query the service across fields, so that the situation that the target service related personnel are required to query and explain the related service knowledge of the target service and waste manpower and material resources is avoided.
Next, the above steps of the present exemplary embodiment will be described in more detail.
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.
Wherein, the business refers to the target which is completed by the user through the operation of the system. For example, a loan service implemented by a user through a network system; the service log corresponding to the target service is a log for recording specific conditions of executing the target service, and the log may include time of executing the target service, and all execution information related to the execution of the target service, such as which service flows are specifically executed during the execution. The basic service chain corresponding to the target service refers to the shortest chain which enables the service system to execute the target service to end or restart executing 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 by 4 nodes of service start, service admission, admission success, and admission exception; the service system can also finish the execution of the service through 5 nodes of service start, service admission, successful admission, normal service and service finish; the service system can also finish the execution of the service through 5 nodes of service start, service admission, admission success, admission failure and client return visit. Thus, a service chain as in fig. 4 may include the above-described 3 basic service chains. For another example, assuming that the preset node a is a service start node, referring to a service chain a→b→c→d→a→b→e shown in fig. 5, wherein the sub-chain a→b→c→d may cause the service to be restarted (i.e., the service start node a is restarted), and the sub-chain a→b→e may end the service execution, so that the sub-chain a→b→c→d and the sub-chain a→b→e are all basic service chains.
In an exemplary embodiment, after a user initiates a target service, a service node corresponding to the user in a service system usually belongs to a flow corresponding to the target service within a period of time after the initiation time. Accordingly, when acquiring the service log corresponding to the target service, referring to fig. 6, the following steps S610 and S620 may be included:
in step S610, the service flows are grouped and parsed based on the user identifiers to obtain service logs corresponding to at least one user identifier.
In an exemplary embodiment, after a user initiates a target service, the service system typically carries a user identifier in the service flow when executing the target service. 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 moment are executed by a user, where the service flows simultaneously include service flows and execution information of the service flows, and the execution information may include user ids corresponding to users for calling the service flows.
Based on this, in order to obtain a service log of each service initiated by a certain user, the service flows may be grouped based on the user identifiers, and all data included in the service flows may be classified to obtain the service flows corresponding to each user identifier. And then analyzing the service flow corresponding to each user identifier 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 obtaining the service log corresponding to each user identifier, since after a user triggers a service, the service log corresponding to the user identifier of the user is usually the service log corresponding to the time of triggering service execution in a period of time after triggering. Therefore, the initiation time of the target service can be 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 be used as the service log corresponding to the target service.
In an exemplary embodiment, after obtaining the service log corresponding to the target service, a basic service chain corresponding to the target service may be constructed according to the service log. It should be noted that, when the target service is executed, various situations such as automatic re-triggering after a failure of executing a certain service may occur. Thus, the chain directly generated 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 obtaining the service log, a service execution chain for executing the service may be first constructed according to the service log. The service execution chain corresponding to the target service may include service nodes that are sequentially triggered in the service system according to the order when the service system executes the target service. For example, referring to fig. 5, the diagram shows that the service execution chain of a certain target service is 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 service logs, service logs corresponding to the target service may be first grouped based on user identifiers to obtain at least one service log corresponding to the user identifier. Then, the node data contained in the service log is determined according to the service log corresponding to each user identifier, and then a service execution chain corresponding to the target service is constructed according to the node data. The node data included in the service log may generally include a service node triggered by the service system when the service is executed, a time when each service node is triggered, attribute data of the service node, and the like.
In an exemplary embodiment, the node data may include the service node and the trigger sequence of the service node. Because the service execution chains may be different when the service is executed, the service nodes included in each group of node data may be clustered according to the service nodes, and each group of node data including the same service node may be classified into one type to obtain at least one type of node data. Then, for each type of node data, the triggering position of the service node can be determined according to the triggering sequence of the service node contained in the node data, and then a service execution chain corresponding to the target service is constructed according to the determined triggering position of each service node according to the service node contained in each type of node data.
When determining the triggering position of the service node, because the service logs generated by some service flows are not completely ordered, unordered data may exist. Thus, in determining the trigger position of the service node, it may be determined based on a large amount of service data. Specifically, since service nodes included in the same class of node data are the same, the trigger position of the service node in the service execution chain can be determined according to the trigger sequence of each service node in each group of node data in the same class of node data.
For example, assume that a class of node data includes 5 sets of node data in total, each set of node data including 26 service nodes in total of service node a, service node B, service node C … service node Z. The triggering sequence of the service node C in the 5 groups of node data is 22 th bit, 23 rd bit, 22 nd bit and 22 nd bit respectively. At this time, the trigger position may be determined by calculating the variance and standard deviation of the order of the service nodes C and according to the magnitudes of the variance and standard deviation. Specifically, in the above example, the variance is 0.16 and the standard deviation is 0.4 can be calculated based on 22, 23, 22, and if both the variance and the standard deviation are smaller than the preset values, the data quality can be considered to be better, so that the mode 22 can be taken as the final trigger position; on the contrary, if the triggering sequence is 21, 19, 32, 21, 44, the variance and standard deviation are 89.84 and 9.48 respectively, and if the variance and standard deviation are larger than the preset value, the data quality can be considered to be poor, so that the data needs to be discarded, and the triggering positions of the service nodes are recalculated by other data.
It should be noted that, if a service node is added or subtracted in the execution of the target service, which is the service system change, the sequence of the whole service chain will change. However, since the data volume of the service log is usually very large, even after the movement, the standard deviation and variance change of the data are relatively small, and thus the trigger position can be determined by using the variance and standard deviation.
In step S820, the service execution chain is subjected to chain splitting, so as to obtain at least one basic service chain corresponding to the target service.
In an exemplary embodiment, as described 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 split to obtain a basic service chain corresponding to at least one target service. Specifically, a preset service node can be set in advance for a target service, and when the preset service node appears in a basic service chain, the preset service node is used as a partition node to perform chain partition on a 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 the previous node. For example, when the starting node is a, the service execution chain a, B, C, D, a, B, E may be split between the service node a and the service node D before it to obtain the basic service chain a, B, C, D and the basic service chain a, B, E; when the preset service node is an end node, the end node may be used as a partition node, and the partition may be performed between the end node and the subsequent node. For example, the end nodes may be set to D and E, and the chain may be executed for the above-mentioned traffic, that is, the division may be performed between the traffic node D and the traffic node a following it, and the division 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 segmentation may be performed from before the start node and after the end node.
In step S320, the basic service chains are aggregated based on the same service nodes included in the basic service chains, to obtain a service knowledge graph.
In an exemplary embodiment, after obtaining the basic service chains, the service knowledge graph may be obtained by aggregating each basic service chain based on the same service node included in the basic service chains. When in aggregation, the same service nodes included in the basic service chain can be combined into one, and the rest 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 graph 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 obtained service knowledge graph may be very complex. For example, as shown in fig. 10, a service knowledge graph of a certain target service includes a plurality of sub-services, and a more complex service knowledge graph can be obtained by aggregating basic service chains corresponding to the plurality of sub-services.
In addition, in an exemplary embodiment, in order to better show service knowledge, service nodes triggered when the service system executes a service flow may be monitored to obtain attribute information of each service node. The attribute information may include basic information, usage information, etc. of the service node. For example, information may be included such as node name of the service node, node role, frequency of node usage, whether there is an associated node, etc. After obtaining the attribute information, the service node may be energized so that the attribute information may be presented in a service knowledge graph. For example, attribute information may be marked on the service node, with corresponding attribute information being revealed 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 that after obtaining the service log, the service log may be subjected to data processing. Specifically, the data cleaning, data extraction, data alignment and other processes can be performed. Through the data processing process, the data deduplication can be performed on the service log, error data in the service log are deleted, and incomplete data in the service log are filled completely.
In addition, in an exemplary embodiment, after the service knowledge graph corresponding to a target service is obtained, there may be an association relationship between other target services and the target service, so that knowledge graphs corresponding to multiple target services may be combined into one global knowledge graph according to the association relationship. For example, the global knowledge graph as 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 is continuously increased, the service knowledge graph can be updated and synchronized in real time according to the new service pipelining machine which is continuously increased in the service system, so as to ensure the inheritance of the service system knowledge. For example, the service nodes in the service system that need to be processed can also be known through the service system architecture flow shown in fig. 12.
It should be noted that although the steps of the methods in the present disclosure are depicted in the accompanying drawings in a particular order, this does not require or imply that the steps must be performed in that particular order, or that all illustrated steps be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
Further, in this example embodiment, a device for constructing a business knowledge graph is also provided. The construction device of the business knowledge graph can be applied to a server or terminal equipment. Referring to fig. 13, the construction apparatus 1300 of a business knowledge graph may include a chain construction module 1310 and a graph construction module 1320. Wherein:
the chain construction module 1310 is configured to obtain a service log corresponding to the target service, and construct a basic service chain corresponding to the target service according to the service log;
the graph construction module 1320 is configured to aggregate each basic service chain based on the same service node included in each basic service chain, so as to obtain a service knowledge graph.
In an exemplary embodiment, the chain construction module 1310 may be configured to group and parse the service flowing water based on the user identifier, so as to obtain a service log corresponding to at least one user identifier; 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 carrying out 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 the target service based on the user identifier, so as to obtain at least one service log corresponding to the 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 the data of each group of nodes.
In an exemplary embodiment, the chain construction module 1310 may be configured to cluster the node data according to the service nodes included in the node data to obtain at least one type of node data; determining a triggering position corresponding to the service node based on the triggering sequence of the service node contained in the node data aiming at each type of the node data; and constructing a service execution chain corresponding to the target service based on the service node and the triggering position corresponding to the service node contained in each type of service data.
In an exemplary embodiment, the chain construction module 1310 may be configured to perform chain splitting on the service execution chain with the preset service node as a splitting node, so as to obtain at least one basic service chain.
In an exemplary embodiment, the chain construction module 1310 may be configured to perform data processing on the service log; the data processing includes at least one of the following processes: and performing data deduplication on the service log, deleting error data in the service log, and filling incomplete data in the service log completely.
The specific details of each module or unit in the above-mentioned construction device of the business knowledge graph are described in detail in the corresponding construction method of the business knowledge graph, so that the details are not repeated here.
It should be noted that although in the above detailed description several modules or units of a 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 in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
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 adaptations, 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 is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (7)
1. The construction method of the 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;
the 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; performing chain segmentation on the service execution chain to obtain at least one basic service chain corresponding to the target service;
the construction of the service execution chain corresponding to the target service based on the service log comprises the following steps: 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; constructing a service execution chain corresponding to the target service based on each group of node data;
the node data comprises service nodes and a triggering sequence of the service nodes; the construction of the service execution chain corresponding to the target service based on the node data of each group 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 the service node based on the triggering sequence of the service node contained in the node data aiming at each type of node data; constructing a service execution chain corresponding to the target service based on the service node and the triggering position corresponding to the service node contained in each type of service data;
and aggregating the basic service chains based on the same service nodes 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 includes:
grouping and analyzing the service flowing water based on the user identification to obtain a service log corresponding to at least one user identification;
searching the initiation time of the target service in the service logs corresponding to the user identifiers, and extracting the service log corresponding to the target service based on the initiation time.
3. The method of claim 1, wherein the performing chain splitting on the service execution chain to obtain at least one basic service chain corresponding to the target service includes:
and carrying out 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.
4. The method of claim 1, wherein after the obtaining the service log corresponding to the target service, the method further comprises:
performing data processing on the service log; the data processing includes at least one of the following: and performing data deduplication on the service log, deleting error data in the service log, and filling incomplete data in the service log completely.
5. The device for constructing the business knowledge graph is characterized by comprising the following components:
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;
the 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; performing chain segmentation on the service execution chain to obtain at least one basic service chain corresponding to the target service;
the construction of the service execution chain corresponding to the target service based on the service log comprises the following steps: 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; constructing a service execution chain corresponding to the target service based on each group of node data;
the node data comprises service nodes and a triggering sequence of the service nodes; the construction of the service execution chain corresponding to the target service based on the node data of each group 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 the service node based on the triggering sequence of the service node contained in the node data aiming at each type of node data; constructing a service execution chain corresponding to the target service based on the service node and the triggering position corresponding to the service node contained in each type of service data;
and the map construction module is used for aggregating the basic service chains based on the same service nodes contained in the basic service chains to obtain a service knowledge map.
6. A computer readable medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of any of claims 1-4.
7. 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 1-4 via execution of the executable instructions.
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