CN111597355A - Information processing method and device - Google Patents
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Abstract
The application provides an information processing method and device, wherein the method comprises the following steps: determining a knowledge graph ontology graph of the target service scene according to a plurality of service characteristic types of the target service scene and the relationship between each service characteristic type; determining an information processing mode corresponding to each service feature type node in the knowledge map ontology graph according to the target service scene; constructing a knowledge graph according to a plurality of target service information corresponding to the target service scene and a knowledge graph ontology graph of the target service scene; and respectively processing the service characteristic information corresponding to each service characteristic type node in the knowledge graph by using an information processing mode corresponding to each service characteristic type node in the knowledge graph ontology graph. By establishing the knowledge graph, the method and the device can process multiple information with incidence relation at the same time, improve the dimensionality of information analysis and enable the information analysis result to have reference value.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to an information processing method and apparatus.
Background
Various industries increasingly rely on information technology to improve production efficiency, and information systems generate more and more information, which often need to process the information, extract useful information and guide business development. Through information processing, functions of statistics, comparison, prediction and the like can be provided for decision-making personnel so as to obtain a better decision-making result. For example, the market value of the commodity is found through sales statistics, and the commodity yield is adjusted; through sales region statistics, the demand difference of different regions for commodities is found, and then the product form of the commodities is adjusted; and comparing the advertisement putting trend with the sales volume trend to find the service promotion effect of the advertisement putting.
With the market competition getting worse, business knowledge of the industry becomes more and more complex. Statistical analysis of a single item of information, even comparative analysis of multiple similar information, has not provided good decision support, and thus, a method capable of processing multiple different types of related information is needed.
Disclosure of Invention
In view of the above, an object of the present application is to provide an information processing method and apparatus, which can process multiple kinds of information having association relationships at the same time, thereby improving the dimensionality of information analysis and making the information analysis result have a reference value.
The embodiment of the application provides an information processing method, which comprises the following steps:
determining a knowledge graph ontology graph of the target service scene according to a plurality of service characteristic types of the target service scene and the relationship between each service characteristic type; wherein the knowledge-graph ontology graph comprises a plurality of service feature type nodes;
determining an information processing mode corresponding to each service feature type node in the knowledge map ontology graph according to the target service scene;
constructing a knowledge graph according to a plurality of target service information corresponding to the target service scene and a knowledge graph ontology graph of the target service scene; the knowledge graph comprises a plurality of service characteristic nodes, and each service characteristic node comprises service characteristic information corresponding to the service characteristic node;
and respectively processing the service characteristic information corresponding to each service characteristic type node in the knowledge graph by using an information processing mode corresponding to each service characteristic type node in the knowledge graph ontology graph.
In a possible implementation manner, the constructing a knowledge graph according to a plurality of target service information corresponding to the target service scenario and a knowledge graph ontology graph of the target service scenario includes:
determining a plurality of entities in the plurality of target business information, attribute information of each entity and a relationship between each entity by using a natural language processing model;
according to the entities, the attribute information of each entity and the relationship among the entities, executing entity link processing of a preset entity corresponding to the entity in a knowledge base on each entity;
updating the knowledge base according to the result of the entity link processing and the target service information;
and constructing the knowledge graph according to the knowledge base and the knowledge graph ontology graph.
In one possible embodiment, the method further comprises:
and if the preset entity corresponding to the entity is not found in the knowledge base, creating the preset entity corresponding to the entity in the knowledge base.
In one possible embodiment, the method further comprises:
determining the visual effect of the service characteristic node corresponding to the service characteristic information in the knowledge graph according to the processing result of each service characteristic information;
and displaying the knowledge graph according to the visual effect.
In one possible embodiment, the method further comprises:
when the statistical query operation aiming at least one service characteristic node is detected, performing statistical processing on the processing result of the service characteristic information of each service characteristic node corresponding to the query operation;
and displaying the result of the statistical treatment.
In one possible embodiment, the method further comprises:
when a statistical signal aiming at a target theme is detected, determining the importance of each service characteristic node relative to the target theme according to the processing result of the service characteristic information corresponding to each service characteristic node and the connection relation between each service characteristic node;
determining at least one target service characteristic node corresponding to the statistical signal aiming at the target theme according to the importance;
performing statistical processing on the processing result of the service characteristic information of the at least one target service characteristic node;
and displaying the statistical processing result.
An embodiment of the present application further provides an information processing apparatus, including:
the first determining module is used for determining a knowledge graph ontology graph of the target service scene according to a plurality of service characteristic types of the target service scene and the relationship between each service characteristic type; wherein the knowledge-graph ontology graph comprises a plurality of service feature type nodes;
the second determining module is used for determining an information processing mode corresponding to each service feature type node in the knowledge map ontology graph according to the target service scene;
the construction module is used for constructing a knowledge graph according to a plurality of target service information corresponding to the target service scene and a knowledge graph ontology graph of the target service scene; the knowledge graph comprises a plurality of service characteristic nodes, and each service characteristic node comprises service characteristic information corresponding to the service characteristic node;
and the information processing module is used for respectively processing the service characteristic information corresponding to each service characteristic type node in the knowledge graph by using the information processing mode corresponding to each service characteristic type node in the knowledge graph ontology graph.
In a possible implementation, the building module is specifically configured to:
determining a plurality of entities in the plurality of target business information, attribute information of each entity and a relationship between each entity by using a natural language processing model;
according to the entities, the attribute information of each entity and the relationship among the entities, executing entity link processing of a preset entity corresponding to the entity in a knowledge base on each entity;
updating the knowledge base according to the result of the entity link processing and the target service information;
and constructing the knowledge graph according to the knowledge base and the knowledge graph ontology graph.
In one possible embodiment, the building module is further configured to:
and if the preset entity corresponding to the entity is not found in the knowledge base, creating the preset entity corresponding to the entity in the knowledge base.
In a possible embodiment, the apparatus further comprises a first display module for:
when the statistical query operation aiming at least one service characteristic node is detected, performing statistical processing on the processing result of the service characteristic information of each service characteristic node corresponding to the query operation;
and displaying the result of the statistical treatment.
In a possible embodiment, the apparatus further comprises a second display module for:
when a statistical signal aiming at a target theme is detected, determining the importance of each service characteristic node relative to the target theme according to the processing result of the service characteristic information corresponding to each service characteristic node and the connection relation between each service characteristic node;
determining at least one target service characteristic node corresponding to the statistical signal aiming at the target theme according to the importance;
performing statistical processing on the processing result of the service characteristic information of the at least one target service characteristic node;
and displaying the statistical processing result.
In a possible implementation manner, the information processing apparatus further includes a third presentation module configured to:
determining the visual effect of the service characteristic node corresponding to the service characteristic information in the knowledge graph according to the processing result of each service characteristic information;
and displaying the knowledge graph according to the visual effect.
An embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the machine-readable instructions being executable by the processor to perform the steps of the information processing method as described above.
Embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the information processing method as described above.
According to the information processing method and the information processing device, firstly, a knowledge graph ontology graph of a target service scene is determined according to a plurality of service feature types of the target service scene and the relation between each service feature type; wherein the knowledge-graph ontology graph comprises a plurality of service feature type nodes; secondly, determining an information processing mode corresponding to each service feature type node in the knowledge map ontology graph according to the target service scene; then, constructing a knowledge graph according to a plurality of target service information corresponding to the target service scenes and a knowledge graph ontology graph of the target service scenes; the knowledge graph comprises a plurality of service characteristic nodes, and each service characteristic node comprises service characteristic information corresponding to the service characteristic node; and finally, respectively processing the service characteristic information corresponding to each service characteristic type node in the knowledge graph by using an information processing mode corresponding to each service characteristic type node in the knowledge graph ontology graph. Compared with the information processing method in the prior art, the method and the device have the advantages that the knowledge graph is established, various information with incidence relation can be processed at the same time, the dimensionality of information analysis is improved, and the information analysis result has a reference value.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a flowchart illustrating an information processing method provided in an embodiment of the present application;
FIG. 2 is a schematic diagram illustrating an ontology graph of a knowledge graph in an information processing method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram illustrating an information processing apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of another information processing apparatus provided in an embodiment of the present application;
fig. 5 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. Every other embodiment that can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present application falls within the protection scope of the present application.
At present, various industries increasingly rely on information technology to improve production efficiency, and information systems generate more and more information, which often need to process the information, extract useful information and guide business development. Through information processing, functions of statistics, comparison, prediction and the like can be provided for decision-making personnel so as to obtain a better decision-making result. For example, the market value of the commodity is found through sales statistics, and the commodity yield is adjusted; through sales region statistics, the demand difference of different regions for commodities is found, and then the product form of the commodities is adjusted; and comparing the advertisement putting trend with the sales volume trend to find the service promotion effect of the advertisement putting.
With the market competition getting worse, business knowledge of the industry becomes more and more complex. Statistical analysis of a single item of information, even comparative analysis of multiple similar information, has not provided good decision support, and thus, a method capable of processing multiple different types of related information is needed.
Based on this, the embodiment of the application provides an information processing method, so as to process multiple kinds of information with association relations at the same time, improve the dimensionality of information analysis, and enable the information analysis result to have a reference value.
Referring to fig. 1, fig. 1 is a flowchart of an information processing method according to an embodiment of the present disclosure. As shown in fig. 1, an information processing method provided in an embodiment of the present application includes:
s101, determining a knowledge graph ontology graph of the target service scene according to a plurality of service characteristic types of the target service scene and the relationship between each service characteristic type.
Wherein the knowledge-graph ontology graph comprises a plurality of service feature type nodes.
In this step, the target business scenario may be determined according to the motivation of information processing, for example, if the user wants to know vulnerable parts of various automobiles on the market, the target business scenario may be "how a failure occurs in which part of which automobile type is referred to by the network review".
After the target service scenario is determined, a knowledge graph ontology graph of the target service scenario may be determined according to a plurality of service feature types of the target service scenario and a relationship between each service feature type. The service feature types are types of each service feature in a target service scene, and a certain association may exist between each service feature type. Specifically, each service feature type can be used as a service feature type node in the knowledge graph ontology graph, different service feature type nodes with a relationship are connected through a line segment, and the relationship between the service feature type nodes corresponding to the two end points is stored in the line segment.
Referring to fig. 2, fig. 2 is a schematic diagram of a knowledge graph ontology diagram in an information processing method according to an embodiment of the present application. As shown in fig. 2, when the target service scenario is "network comment which part of which vehicle type is referred to for what kind of fault occurs", the service feature types may include network comment, vehicle type, part, fault phenomenon, etc., the network comment refers to the vehicle type, part, fault phenomenon, and the vehicle type is provided with the part, and the part has the fault phenomenon.
Each business feature type is an entity, and different entities can also be associated with different attribute information, for example, the entity of the network comment can be associated with attribute information such as comment text content, comment source, whether the comment is a negative comment, and the attribute information can be used as a business feature type node.
After the knowledge-graph ontology graph is obtained, a database can be used for storage, or services such as remote query and modification are provided, and the storage format can be an OWL2 format.
S102, determining an information processing mode corresponding to each service feature type node in the knowledge graph ontology graph according to the target service scene.
In this step, the information processing mode corresponding to each service feature type node may be determined according to the specific content and the specific requirements of the target service scenario. For example, when the target service scenario is "network comment mentions which part of which vehicle type has what kind of fault", the information processing manner of the service feature type node "part" may include one or more of counting the number of occurrences of the part in the network comment, determining a histogram of the number of associated fault classifications, counting the stock trend of the part within the latest preset time period, counting the sales trend of the part within the latest preset time period, and the like.
Further, the information processing mode corresponding to each service feature type node can be stored as a template, and can be directly called in subsequent use. Moreover, different information processing modes can be defined for different entities of the same type, and corresponding steps can be increased or decreased.
The template of the information processing mode can be organized in a structured data format, such as an xml or json format, and the template can be associated with the corresponding service feature type node and stored together with the knowledge graph ontology graph.
Furthermore, labels can be marked on the templates of all information processing modes, and the corresponding templates are directly applied when the same labels are detected.
S103, constructing a knowledge graph according to a plurality of target service information corresponding to the target service scenes and the knowledge graph ontology graph of the target service scenes.
The knowledge graph comprises a plurality of service characteristic nodes, and each service characteristic node comprises service characteristic information corresponding to the service characteristic node.
For example, in a target service scenario of "which part of which vehicle type is referred to by the network comment has what kind of fault", the target service information may be "service information related to the vehicle type, the part, and the fault, which are referred to by the network comment and the network comment", and the target service information may be one or more pieces of information including multiple types of service feature information.
In the step, natural language processing can be carried out on the text information in the target service information, each entity in the target service information, the attribute of each entity and the relation between each entity are extracted, and the relation and the existing data in a knowledge base of a knowledge graph form an information source for constructing the knowledge graph, wherein each entity corresponds to one service characteristic, and the service characteristic corresponds to one service characteristic type; and for the structured data with the same type in the knowledge base, the data can be directly converted and updated into the knowledge base.
After the information is extracted, the extracted entity and other entities in the knowledge base of the knowledge graph can be subjected to entity link processing, the extracted information is updated to the knowledge base, and the knowledge graph is constructed according to the knowledge base and the knowledge graph body diagram.
Specifically, entity linking operation can be performed on the context of the extracted information together, during the entity linking operation, data associated with the target service information can be searched in a knowledge base of a knowledge graph, after the data is searched, the entity can be associated with the searched preset entity, a unique identifier corresponding to the entity is determined, if the data is not searched, the preset entity corresponding to the entity is established in the knowledge base, and then the entity is associated with the established preset entity. For structured data, the association can be made directly from the unique identification of the entity.
After entity linking is carried out, target service information can be updated to the knowledge base according to the result of the entity linking, and then the knowledge base and the knowledge map body graph after updating are utilized to construct the knowledge map.
And S104, respectively processing the service characteristic information corresponding to each service characteristic type node in the knowledge graph by using an information processing mode corresponding to each service characteristic type node in the knowledge graph ontology graph.
In this step, the service characteristic information corresponding to each service characteristic type node may be processed by using the information processing manner corresponding to the service characteristic type node, and the processing may be performed in real time or triggered.
Specifically, in a real-time implementation manner, whenever new target service information is associated to the knowledge graph, the new target service information can be processed, and the original processing result is updated; in the triggering manner, when receiving the query operation for the service feature type, the processing may be performed according to the information processing manner corresponding to the service feature type node.
In a possible implementation manner, the constructing a knowledge graph according to a plurality of target service information corresponding to the target service scenario and a knowledge graph ontology graph of the target service scenario includes:
determining a plurality of entities in the plurality of target business information, attribute information of each entity and a relationship between each entity by using a natural language processing model;
according to the entities, the attribute information of each entity and the relationship among the entities, executing entity link processing of a preset entity corresponding to the entity in a knowledge base on each entity;
updating the knowledge base according to the result of the entity link processing and the target service information;
and constructing the knowledge graph according to the knowledge base and the knowledge graph ontology graph.
In one possible embodiment, the method further comprises:
and if the preset entity corresponding to the entity is not found in the knowledge base, creating the preset entity corresponding to the entity in the knowledge base.
In one possible embodiment, the method further comprises:
determining the visual effect of the service characteristic node corresponding to the service characteristic information in the knowledge graph according to the processing result of each service characteristic information;
and displaying the knowledge graph according to the visual effect.
In this step, the visual effect of the service feature node corresponding to each service feature information in the knowledge graph may be determined according to the magnitude relationship between the processing result of each service feature information and the normal processing result, for example, in a target service scenario of "which part of which vehicle type is referred to by the network comment about what fault occurs" and if the number of occurrences in the network comment exceeds a preset threshold in the processing result of the service feature node of "oil leakage", the service feature node may be highlighted and/or the model of the service feature node may be enlarged.
In one possible embodiment, the method further comprises:
when the statistical query operation aiming at least one service characteristic node is detected, performing statistical processing on the processing result of the service characteristic information of each service characteristic node corresponding to the query operation;
and displaying the result of the statistical treatment.
In this step, a user can perform data analysis by using a knowledge graph according to own requirements, select one or more target service feature nodes, obtain the latest target service information when obtaining a query request of the user, execute operations of S103 and S104, perform statistical processing on a processing result of the service feature information of each service feature node corresponding to the query operation, and display the statistical processing result to the user; or directly counting the processing results of the previously stored service characteristic information without updating the target service information, and displaying the counting processing results.
In one possible embodiment, the method further comprises:
when a statistical signal aiming at a target theme is detected, determining the importance of each service characteristic node relative to the target theme according to the processing result of the service characteristic information corresponding to each service characteristic node and the connection relation between each service characteristic node;
determining at least one target service characteristic node corresponding to the statistical signal aiming at the target theme according to the importance;
performing statistical processing on the processing result of the service characteristic information of the at least one target service characteristic node;
and displaying the statistical processing result.
In this step, a service feature node related to a target topic in a knowledge graph may be determined according to the target topic, an importance of the service feature node with respect to the target topic is determined according to a processing result of the determined service feature node, if the importance is higher than a preset threshold, the service feature node is selected as the target service feature node, a processing result of service feature information of the target service feature node is subjected to statistical processing, and then the statistical processing result is displayed.
The information processing method provided by the embodiment of the application comprises the steps of firstly determining a knowledge graph ontology graph of a target service scene according to a plurality of service characteristic types of the target service scene and the relationship between each service characteristic type; wherein the knowledge-graph ontology graph comprises a plurality of service feature type nodes; secondly, determining an information processing mode corresponding to each service feature type node in the knowledge map ontology graph according to the target service scene; then, constructing a knowledge graph according to a plurality of target service information corresponding to the target service scenes and a knowledge graph ontology graph of the target service scenes; the knowledge graph comprises a plurality of service characteristic nodes, and each service characteristic node comprises service characteristic information corresponding to the service characteristic node; and finally, respectively processing the service characteristic information corresponding to each service characteristic type node in the knowledge graph by using an information processing mode corresponding to each service characteristic type node in the knowledge graph ontology graph. Compared with the information processing method in the prior art, the method and the device have the advantages that the knowledge graph is established, various information with incidence relation can be processed at the same time, the dimensionality of information analysis is improved, and the information analysis result has a reference value.
Referring to fig. 3 and 4, fig. 3 is a schematic structural diagram of an information processing apparatus according to an embodiment of the present disclosure, and fig. 4 is a schematic structural diagram of another information processing apparatus according to an embodiment of the present disclosure. As shown in fig. 3, the information processing apparatus 300 includes:
a first determining module 310, configured to determine a knowledge graph ontology graph of a target service scenario according to multiple service feature types of the target service scenario and a relationship between each service feature type; wherein the knowledge-graph ontology graph comprises a plurality of service feature type nodes;
a second determining module 320, configured to determine, according to the target service scenario, an information processing manner corresponding to each service feature type node in the knowledge graph ontology graph;
a constructing module 330, configured to construct a knowledge graph according to a plurality of target service information corresponding to the target service scene and a knowledge graph ontology graph of the target service scene; the knowledge graph comprises a plurality of service characteristic nodes, and each service characteristic node comprises service characteristic information corresponding to the service characteristic node;
the information processing module 340 is configured to respectively process the service feature information corresponding to each service feature type node in the knowledge graph by using an information processing manner corresponding to each service feature type node in the knowledge graph ontology graph.
In a possible implementation, the building module 330 is specifically configured to:
determining a plurality of entities in the plurality of target business information, attribute information of each entity and a relationship between each entity by using a natural language processing model;
according to the entities, the attribute information of each entity and the relationship among the entities, executing entity link processing of a preset entity corresponding to the entity in a knowledge base on each entity;
updating the knowledge base according to the result of the entity link processing and the target service information;
and constructing the knowledge graph according to the knowledge base and the knowledge graph ontology graph.
In a possible implementation, the building module 330 is further configured to:
and if the preset entity corresponding to the entity is not found in the knowledge base, creating the preset entity corresponding to the entity in the knowledge base.
As shown in fig. 4, in a possible implementation, the information processing apparatus 400 includes a first determining module 410, a second determining module 420, a constructing module 430, an information processing module 440, and a first presenting module 450, where the first presenting module 450 is configured to:
when the statistical query operation aiming at least one service characteristic node is detected, performing statistical processing on the processing result of the service characteristic information of each service characteristic node corresponding to the query operation;
and displaying the result of the statistical treatment.
In a possible implementation, the information processing apparatus 400 further includes a second presentation module 460, configured to:
when a statistical signal aiming at a target theme is detected, determining the importance of each service characteristic node relative to the target theme according to the processing result of the service characteristic information corresponding to each service characteristic node and the connection relation between each service characteristic node;
determining at least one target service characteristic node corresponding to the statistical signal aiming at the target theme according to the importance;
performing statistical processing on the processing result of the service characteristic information of the at least one target service characteristic node;
and displaying the statistical processing result.
In a possible implementation manner, the information processing apparatus 400 further includes a third presentation module 470, configured to:
determining the visual effect of the service characteristic node corresponding to the service characteristic information in the knowledge graph according to the processing result of each service characteristic information;
and displaying the knowledge graph according to the visual effect.
The information processing device provided by the embodiment of the application determines a knowledge graph ontology graph of a target service scene according to a plurality of service feature types of the target service scene and the relationship between each service feature type; wherein the knowledge-graph ontology graph comprises a plurality of service feature type nodes; secondly, determining an information processing mode corresponding to each service feature type node in the knowledge map ontology graph according to the target service scene; then, constructing a knowledge graph according to a plurality of target service information corresponding to the target service scenes and a knowledge graph ontology graph of the target service scenes; the knowledge graph comprises a plurality of service characteristic nodes, and each service characteristic node comprises service characteristic information corresponding to the service characteristic node; and finally, respectively processing the service characteristic information corresponding to each service characteristic type node in the knowledge graph by using an information processing mode corresponding to each service characteristic type node in the knowledge graph ontology graph. Compared with the information processing method in the prior art, the method and the device have the advantages that the knowledge graph is established, various information with incidence relation can be processed at the same time, the dimensionality of information analysis is improved, and the information analysis result has a reference value.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 5, the electronic device 500 includes a processor 510, a memory 520, and a bus 530.
The memory 520 stores machine-readable instructions executable by the processor 510, when the electronic device 500 runs, the processor 510 communicates with the memory 520 through the bus 530, and when the machine-readable instructions are executed by the processor 510, the steps of the information processing method in the embodiment of the method shown in fig. 1 may be executed.
An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the information processing method in the method embodiment shown in fig. 1 may be executed.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the exemplary embodiments of the present application, and are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. An information processing method, characterized in that the method comprises:
determining a knowledge graph ontology graph of the target service scene according to a plurality of service characteristic types of the target service scene and the relationship between each service characteristic type; wherein the knowledge-graph ontology graph comprises a plurality of service feature type nodes;
determining an information processing mode corresponding to each service feature type node in the knowledge map ontology graph according to the target service scene;
constructing a knowledge graph according to a plurality of target service information corresponding to the target service scene and a knowledge graph ontology graph of the target service scene; the knowledge graph comprises a plurality of service characteristic nodes, and each service characteristic node comprises service characteristic information corresponding to the service characteristic node;
and respectively processing the service characteristic information corresponding to each service characteristic type node in the knowledge graph by using an information processing mode corresponding to each service characteristic type node in the knowledge graph ontology graph.
2. The method according to claim 1, wherein the constructing a knowledge graph according to a plurality of target service information corresponding to the target service scenario and a knowledge graph ontology graph of the target service scenario includes:
determining a plurality of entities in the plurality of target business information, attribute information of each entity and a relationship between each entity by using a natural language processing model;
according to the entities, the attribute information of each entity and the relationship among the entities, executing entity link processing of a preset entity corresponding to the entity in a knowledge base on each entity;
updating the knowledge base according to the result of the entity link processing and the target service information;
and constructing the knowledge graph according to the knowledge base and the knowledge graph ontology graph.
3. The method of claim 2, further comprising:
and if the preset entity corresponding to the entity is not found in the knowledge base, creating the preset entity corresponding to the entity in the knowledge base.
4. The method of claim 1, further comprising:
determining the visual effect of the service characteristic node corresponding to the service characteristic information in the knowledge graph according to the processing result of each service characteristic information;
and displaying the knowledge graph according to the visual effect.
5. The method of claim 1, further comprising:
when the statistical query operation aiming at least one service characteristic node is detected, performing statistical processing on the processing result of the service characteristic information of each service characteristic node corresponding to the query operation;
and displaying the result of the statistical treatment.
6. The method of claim 1, further comprising:
when a statistical signal aiming at a target theme is detected, determining the importance of each service characteristic node relative to the target theme according to the processing result of the service characteristic information corresponding to each service characteristic node and the connection relation between each service characteristic node;
determining at least one target service characteristic node corresponding to the statistical signal aiming at the target theme according to the importance;
performing statistical processing on the processing result of the service characteristic information of the at least one target service characteristic node;
and displaying the result of the statistical treatment.
7. An information processing apparatus characterized in that the apparatus comprises:
the first determining module is used for determining a knowledge graph ontology graph of the target service scene according to a plurality of service characteristic types of the target service scene and the relationship between each service characteristic type; wherein the knowledge-graph ontology graph comprises a plurality of service feature type nodes;
the second determining module is used for determining an information processing mode corresponding to each service feature type node in the knowledge map ontology graph according to the target service scene;
the construction module is used for constructing a knowledge graph according to a plurality of target service information corresponding to the target service scene and a knowledge graph ontology graph of the target service scene; the knowledge graph comprises a plurality of service characteristic nodes, and each service characteristic node comprises service characteristic information corresponding to the service characteristic node;
and the information processing module is used for respectively processing the service characteristic information corresponding to each service characteristic type node in the knowledge graph by using the information processing mode corresponding to each service characteristic type node in the knowledge graph ontology graph.
8. The apparatus of claim 7, wherein the building module is specifically configured to:
constructing a knowledge graph framework according to the knowledge graph ontology graph;
determining a plurality of entities in the plurality of target business information, attribute information of each entity and a relationship between each entity by using a natural language processing model;
according to the entities, the attribute information of each entity and the relationship among the entities, carrying out entity link processing on each entity and a service characteristic node corresponding to the entity in the knowledge graph framework;
and determining the knowledge graph according to the knowledge graph framework, the plurality of entities, the attribute information of each entity, the relationship between each entity and the entity link processing result.
9. An electronic device, comprising: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the electronic device is operating, the processor executing the machine-readable instructions to perform the steps of the information processing method according to any one of claims 1 to 6.
10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which, when being executed by a processor, performs the steps of the information processing method according to any one of claims 1 to 6.
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