CN115858810A - Method, system, computer device and storage medium for automatically constructing knowledge graph - Google Patents
Method, system, computer device and storage medium for automatically constructing knowledge graph Download PDFInfo
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Abstract
The invention provides a method, a system, computer equipment and a storage medium for automatically constructing a knowledge graph, which relate to the technical field of data processing, wherein the method comprises the following steps: inputting query information according to business requirements; automatically screening relational data required for constructing a knowledge graph from a relational database according to the query information; and automatically converting the screened relational data into graph data so as to construct a knowledge graph. The technical scheme provided by the invention automatically constructs the knowledge graph by taking the service requirement as a core, reduces the dependence on professionals in the construction process of the knowledge graph, improves the construction efficiency and reduces the construction cost.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method for automatically constructing a knowledge graph, a system for automatically constructing a knowledge graph, a computer device, and a computer-readable storage medium.
Background
The basic design concept of the relational database is to model by taking entities as a core, lacks direct support for the relationship between the entities, and needs to indirectly maintain the relationship between the entities through an association table. This makes the relational database have the problem of the associated form is various, the operation execution efficiency such as inquiry is low when the business support is actually used.
The graph database represents entities and relations by points and edges respectively, and complex data models can be more clearly revealed. With the rapid development of services such as mobile internet, internet of things and the like, the invisible association value between data is increasingly prominent, and as a bottom application scene with a graph database as a foundation, the knowledge graph makes full use of the advantages of a graph data model in the aspects of storage and query to provide a large-scale knowledge base for a plurality of industries so as to support service application. Relational databases have been the mainstream of the development of the database field since the last 80 th century, and various industries accumulate a large amount of relational data, so that how to quickly and effectively construct a knowledge graph becomes the problem to be solved in knowledge graph application for industries with natural graph topological structures, such as telecommunication, traffic, internet social contact and the like.
The traditional knowledge graph construction mode mostly depends on manual experience, a knowledge graph schema needs to be designed manually, node data, relationship data and attribute data prepared in advance are mapped with the knowledge graph schema, and then data are led in to complete the construction of the knowledge graph. The traditional construction mode has heavy dependence on professionals, low construction efficiency, weak generalization of constructed maps and difficult effective reuse.
Disclosure of Invention
The invention is completed in order to at least partially solve the technical problems of heavy dependence on professionals, low construction efficiency, weak generalization of constructed maps and difficult effective reuse in the prior art.
According to an aspect of the present invention, there is provided a method of automatically constructing a knowledge-graph, which may include the steps of: s1, inputting query information according to business requirements; s2, automatically screening relational data required by construction of a knowledge graph from a relational database according to the query information; and S3, automatically converting the screened relational data into graph data so as to construct a knowledge graph.
Optionally, the following steps may also be included: s4, executing graph analysis service according to the constructed knowledge graph; and S5, outputting the query result related to the service requirement.
Optionally, step S2 may comprise the steps of: s21, collecting corpus information related to the service requirement; s22, performing natural language processing modeling according to the corpus information to obtain a natural language processing model; s23, analyzing the query information through the natural language processing model to obtain the semantics of business operation; s24, supplementing and confirming detailed information required by the business operation; and S25, screening the relational data required for constructing the knowledge graph from the relational database according to the detailed information.
Optionally, the corpus information may include one or more of a service management specification, a data model specification, an application scenario white paper, and a service development work guide.
Optionally, the modeling for natural language processing may include cleaning the corpus information, and performing natural language processing model training using a large-scale pre-training model based on the cleaned corpus information.
Optionally, step S3 may include the steps of: s31, visually presenting the screened relational data into a conceptual data model; s32, self-defining and adjusting the conceptual data model; s33, selecting a mapping rule of the relational data to the graph data; s34, mapping the adjusted conceptual data model according to the mapping rule to obtain a schema of the knowledge graph; and S35, constructing the knowledge graph according to the schema of the knowledge graph.
Optionally, the conceptual data model may be configured to: presenting table information of the screened relational data, including an entity table and a relational table; the data table of the relational data only displays names, field names and data types, and marks out a main key and an external key; and the data tables are displayed through connecting lines based on the association relationship between the main keys and the foreign keys.
Optionally, step S32 may include the following adjustments of the conceptual data model by the user through dragging, pulling, dragging visualization operations as required: adding and deleting the data table, the field information and the connecting line; and splitting and merging the data tables.
Optionally, the mapping rule may include: converting each row in the screened data table of the relational data into a node in the graph data; the name of each data table is converted into a label of a node in the graph data; each field in the data table is converted into the attribute of the node in the graph data; each foreign key translates to a relationship in the graph data, and the field name of the foreign key translates to a label for the relationship; or each row in the entity table of the screened relational data is converted into a node in the graph data; converting the name of each entity table into a label of a node in the graph data; each field in the entity table is converted into the attribute of the node in the graph data; each row in the relationship table is converted into a relationship in the graph data; the name of each relationship table is converted into a label of the relationship in the graph data; each field in the relationship table is converted into an attribute of a relationship in the graph data; each foreign key translates to a relationship in the graph data, and the field name of the foreign key translates to a label for the relationship; or a part of relation tables are designated in the data tables of the screened relation data, the part of relation tables are set to be in relation with the conversion type, and the conversion types of other relation tables and entity tables are set to be nodes; for the data table with the conversion type of the node, converting each row in the data table into one node in the graph data, converting the name of each data table into a label of the node in the graph data, and converting each field in the data table into the attribute of the node in the graph data; for the relationship table with the conversion type as the relationship, converting each row in the relationship table into one relationship in the graph data, converting the name of each relationship table into a label of the relationship in the graph data, and converting each field in the relationship table into an attribute of the relationship in the graph data; each foreign key translates to a relationship in the graph data, and the field name of the foreign key translates to a label for the relationship.
According to another aspect of the present invention, there is provided a system for automatically constructing a knowledge-graph, which may include: the input module is set to input query information according to business requirements; a screening module configured to automatically screen relational data required for constructing the knowledge graph from a relational database (e.g., according to step S2 in the aforementioned method of automatically constructing a knowledge graph) based on the query information; a construction module configured to automatically convert the screened relational data into graph data, thereby constructing a knowledge graph (e.g., constructing a knowledge graph according to step S3 of the aforementioned method of automatically constructing a knowledge graph); an execution module configured to execute a graph analysis service based on the constructed knowledge graph; and an output module configured to output a query result related to the business requirement.
According to yet another aspect of the present invention, there is provided a computer apparatus comprising a memory and a processor, the memory having a computer program stored therein, the processor performing the aforementioned method of automatically constructing a knowledge-graph when the processor executes the computer program stored in the memory.
According to yet another aspect of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the aforementioned community partitioning method.
The technical scheme provided by the invention can have the following beneficial effects:
the method for automatically constructing the knowledge graph provided by the invention provides a more efficient knowledge graph construction mode, realizes the automatic construction of the knowledge graph by taking service requirements as a core, reduces the dependence on professionals in the construction process of the knowledge graph, improves the construction efficiency and reduces the construction cost. In addition, the automatic construction mode of the knowledge graph guided by the service requirements has stronger universality, can be copied and popularized in various industries, is convenient for enterprises to associate the service with data resources, and further mines the application value of the data.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a schematic flow chart of a method for automatically constructing a knowledge graph according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a screening step (automatically acquiring relational data) in a method for automatically constructing a knowledge graph according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart illustrating the construction steps (automatic conversion of relational data into graph data) in the method for automatically constructing a knowledge graph according to the embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a system for automatically constructing a knowledge graph according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the following detailed description of the embodiments of the present invention is provided with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
The invention provides a method for automatically constructing a knowledge graph by taking business requirements as guidance, which comprises the following steps with reference to a figure 1:
s1, inputting query information (inputting query information) according to business requirements;
s2, automatically screening relational data (automatically acquiring the relational data) required by construction of the knowledge graph from a relational database (such as a traditional relational database) according to the query information; and
and S3, automatically converting the screened relational data into graph data, and accordingly constructing the knowledge graph (the relational data are automatically converted into the graph data).
Optionally, the method for automatically constructing a knowledge-graph may further comprise the following steps after step S3:
s4, executing graph analysis service (executing graph analysis service) according to the constructed knowledge graph; and
and S5, outputting the query result (outputting query information) related to the service requirement.
Steps S1 to S3 are a knowledge-graph automatic construction process oriented to business needs, and steps S4 and S5 are a knowledge-graph application process.
Referring to fig. 2, the step S2 is data screening guided by service requirement, and specifically may include the following steps:
and S21, collecting corpus information related to the service requirement. For example, the corpus information may be related standards, guidelines, white papers, etc. of the enterprise. More specifically, the corpus information may include, but is not limited to, one or more of a business management specification, a data model specification, an application scenario white paper, and business development work directions.
And S22, performing natural language processing modeling according to the corpus information to obtain a natural language processing model. For example, the modeling of natural language processing may include cleaning corpus information (mainly including abnormal symbol processing, etc.), and training a natural language processing model using a large-scale pre-training model (e.g., bert) based on the cleaned corpus information.
And S23, analyzing the query information (business operation information) through a natural language processing model to obtain the semantics of business operation (for example, enterprise-related business operation semantics).
And S24, supplementing and confirming detailed information required by business operation. For example, the service scenario template is matched according to the semantics of the service operation, and the user supplements and confirms the detailed information required by the relevant service operation on the basis of the service scenario template.
And S25, screening relational data required for constructing the knowledge graph from a relational database (such as a multi-domain traditional relational database of an enterprise) according to the detailed information. The relational data includes, for example, a database, a data table, field information, and the like.
Referring to fig. 3, the step S3 mentioned above is to automatically construct a knowledge graph from the screened relational data, and specifically may include the following steps:
and S31, visually presenting the screened relational data into a conceptual data model. For example, the conceptual data model may be constructed as: presenting table information of the screened relational data, including an entity table and a relational table; the data table of the relational data only displays names, field names and data types, and marks out a main key and an external key; and the data tables are displayed through connecting lines based on the association relationship between the main keys and the foreign keys.
And S32, self-defining and adjusting the conceptual data model. For example, the user can perform the following adjustments to the conceptual data model as needed through simple visualization operations such as dragging, pulling, dragging, etc.: adding and deleting the data table, the field information and the connecting line; and splitting and merging data tables (described in detail below).
S33, a mapping rule of the relational data to the graph data is selected (which will be described in detail below).
And S34, mapping the adjusted conceptual data model according to the mapping rule to obtain the schema of the knowledge graph.
And S35, constructing the knowledge graph according to the schema of the knowledge graph. Specifically, the required knowledge graph is constructed according to the schema import data of the created knowledge graph.
As described above, in step S32, the user adjusts the conceptual data model as needed by a simple visualization operation such as dragging, pulling, or dragging. The addition and deletion of the data table, the field information and the connecting line can be increased to the extent of the traditional relational database in multiple fields of enterprises. For splitting and merging the data table, the splitting operation is a selected field range, the data table is split into a main table and a slave table, the slave table multiplexes a main key of the main table, and an external key is added to the main table to match with a main key of the slave table. And the merging operation is to select the fields with the same property of different data tables, create a new data table, delete the related fields in the original data table, set the main keys in the new data table in a user-defined manner, and add external keys to match the main keys of the original data table. It should be noted that if the selected field is a foreign key in the original data table, its foreign key properties and associations are preserved in the new data table.
As described above, in step S33, a mapping rule of relational data to graph data is selected, which may optionally include one of the following mapping rules 1 to 3.
Mapping rule 1: converting each row in the data table of the screened relational data into a node in the graph data; the name of each data table is converted into a label of a node in the graph data; each field in the data table is converted into the attribute of the node in the graph data; each foreign key translates to a relationship in the graph data, and the field name of the foreign key translates to a label for the relationship.
Mapping rule 2: converting each row in the entity table of the screened relational data into a node in the graph data; converting the name of each entity table into a label of a node in the graph data; each field in the entity table is converted into the attribute of the node in the graph data; each row in the relationship table is converted into a relationship in the graph data; the name of each relationship table is converted into a label of the relationship in the graph data; each field in the relationship table is converted into an attribute of a relationship in the graph data; each foreign key translates to a relationship in the graph data, and the field name of the foreign key translates to a label for the relationship.
Mapping rule 3: designating a part of relation tables in the data tables of the screened relation data, setting the conversion types of the part of relation tables as relations, and setting the conversion types of other relation tables and entity tables as nodes; for the data table with the conversion type of the node, converting each row in the data table into one node in the graph data, converting the name of each data table into a label of the node in the graph data, and converting each field in the data table into the attribute of the node in the graph data; for the relation table with the conversion type as the relation, converting each row in the relation table into one relation in the graph data, converting the name of each relation table into a label of the relation in the graph data, and converting each field in the relation table into an attribute of the relation in the graph data; each foreign key translates to a relationship in the graph data, and the field name of the foreign key translates to a label for the relationship.
The present invention also provides a system 10 for automatically constructing a knowledge graph, as shown in fig. 4, the system 10 comprising: an input module 11 configured to input query information according to a service requirement; a screening module 12 configured to automatically screen relational data required for the construction of the knowledge graph according to step S2 in the aforementioned method of automatically constructing a knowledge graph; a construction module 13 arranged to construct a knowledge-graph according to step S3 of the aforementioned method of automatically constructing a knowledge-graph; an execution module 14 configured to execute a graph analysis service based on the constructed knowledge-graph; and an output module 15 arranged to output a query result relating to the business requirement.
Based on the same technical concept, the embodiment of the present invention correspondingly provides a computer device 20, as shown in fig. 5, the computer device 20 includes a memory 21 and a processor 22, the memory 21 stores a computer program, and when the processor 22 runs the computer program stored in the memory 21, the processor 22 executes the aforementioned method for automatically constructing the knowledge graph.
Based on the same technical concept, the embodiment of the invention correspondingly provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the processor executes the aforementioned method for automatically constructing the knowledge graph.
The method for automatically constructing the knowledge graph by taking the service requirement as the guide has stronger universality, and is particularly suitable for industries with natural graph topological structures, such as telecommunication, traffic, internet social contact and the like. The method is mainly different from the method in the practical application of different enterprises in corpus collection and training of a business operation semantic analysis model and setting of a business scene template. Taking the telecommunication industry as an example, an operator owns multidimensional data such as a B domain, an O domain and the like, and the multidimensional data is stored in a relational database, and the data range can comprise clients, products, services, resources, marketing, enterprise management and the like. If knowledge maps such as network infrastructure topology, communication behavior topology of users and the like are constructed by taking a map database as a carrier and are taken as a data model basis, the latent value in mass data can be more conveniently mined, and typical service application scenes can comprise communication link inquiry of a resource domain, a high-value user maintenance system of a client domain and the like.
In summary, the method, the system, the computer device and the storage medium for automatically constructing the knowledge graph provided by the embodiments of the present invention provide a more efficient knowledge graph construction method, so as to automatically construct the knowledge graph with service requirements as a core, reduce the dependency on professionals in the knowledge graph construction process, improve the construction efficiency and reduce the construction cost. In addition, the automatic construction mode of the knowledge graph guided by the service requirement has stronger universality and can be copied and popularized in various industries.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (12)
1. A method for automatically constructing a knowledge graph, comprising the steps of:
s1, inputting query information according to business requirements;
s2, automatically screening relational data required by construction of a knowledge graph from a relational database according to the query information; and
and S3, automatically converting the screened relational data into graph data so as to construct a knowledge graph.
2. The method of claim 1, further comprising the steps of:
s4, executing graph analysis service according to the constructed knowledge graph; and
and S5, outputting a query result related to the service requirement.
3. Method according to claim 1 or 2, characterized in that step S2 comprises the steps of:
s21, collecting corpus information related to the service requirement;
s22, performing natural language processing modeling according to the corpus information to obtain a natural language processing model;
s23, analyzing the query information through the natural language processing model to obtain the semantics of business operation;
s24, supplementing and confirming detailed information required by the business operation; and
and S25, screening the relational data required for constructing the knowledge graph from the relational database according to the detailed information.
4. The method of claim 3,
the corpus information comprises one or more of service management specifications, data model specifications, application scene white papers and service development work guide.
5. The method of claim 3,
and the natural language processing modeling comprises the steps of cleaning the corpus information and training a natural language processing model by adopting a large-scale pre-training model on the basis of the cleaned corpus information.
6. Method according to claim 1 or 2, characterized in that step S3 comprises the steps of:
s31, visually presenting the screened relational data into a conceptual data model;
s32, self-defining and adjusting the conceptual data model;
s33, selecting a mapping rule of the relational data to the graph data;
s34, mapping the adjusted conceptual data model according to the mapping rule to obtain a schema of the knowledge graph; and
and S35, constructing the knowledge graph according to the schema of the knowledge graph.
7. The method of claim 6, wherein the conceptual data model is structured to: presenting table information of the screened relational data, including an entity table and a relational table; the data table of the relational data only displays names, field names and data types, and marks a main key and an external key; and the data tables are displayed through connecting lines based on the association relationship between the main keys and the foreign keys.
8. The method according to claim 7, wherein step S32 includes the following adjustments of the conceptual data model by the user through the visualization operation of dragging, pulling and dragging as required: adding and deleting the data table, the field information and the connecting line; and splitting and merging the data tables.
9. The method of claim 7, wherein the mapping rule comprises:
converting each row in the screened data table of the relational data into a node in the graph data; the name of each data table is converted into a label of a node in the graph data; each field in the data table is converted into the attribute of the node in the graph data; each foreign key translates to a relationship in the graph data, and the field name of the foreign key translates to a label for the relationship; or
Converting each row in the entity table of the screened relational data into a node in the graph data; converting the name of each entity table into a label of a node in the graph data; each field in the entity table is converted into the attribute of the node in the graph data; each row in the relationship table is converted into a relationship in the graph data; the name of each relationship table is converted into a label of the relationship in the graph data; each field in the relational table is converted into the attribute of the relation in the graph data; each foreign key translates to a relationship in the graph data, and the field name of the foreign key translates to a label for the relationship; or
Designating a part of relation tables in the data tables of the screened relation data, wherein the part of relation tables are set to be conversion types as relations, and the conversion types of other relation tables and entity tables are set as nodes; for the data table with the conversion type of the node, converting each row in the data table into one node in the graph data, converting the name of each data table into a label of the node in the graph data, and converting each field in the data table into the attribute of the node in the graph data; for the relationship table with the conversion type as the relationship, converting each row in the relationship table into one relationship in the graph data, converting the name of each relationship table into a label of the relationship in the graph data, and converting each field in the relationship table into an attribute of the relationship in the graph data; each foreign key translates to a relationship in the graph data, and the field name of the foreign key translates to a label for the relationship.
10. A system for automatically constructing a knowledge graph, comprising:
the input module is set to input query information according to business requirements;
a screening module configured to automatically screen relational data required for construction of a knowledge graph from a relational database according to the query information;
a construction module configured to automatically convert the screened relational data into graph data, thereby constructing a knowledge graph;
an execution module configured to execute a graph analysis service based on the constructed knowledge graph; and
and the output module is set to output the query result related to the service requirement.
11. A computer arrangement comprising a memory having a computer program stored therein and a processor that, when executing the computer program stored by the memory, performs the method of automatically constructing a knowledge-graph according to any one of claims 1 to 9.
12. A computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, performs the method of automatically constructing a knowledge-graph according to any one of claims 1 to 9.
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| CN116303516A (en) * | 2023-04-21 | 2023-06-23 | 中信证券股份有限公司 | Method, device and related equipment for updating knowledge graph |
| CN118093599A (en) * | 2024-04-23 | 2024-05-28 | 浙江大华技术股份有限公司 | Knowledge graph construction method and device and computer readable storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116303516A (en) * | 2023-04-21 | 2023-06-23 | 中信证券股份有限公司 | Method, device and related equipment for updating knowledge graph |
| CN118093599A (en) * | 2024-04-23 | 2024-05-28 | 浙江大华技术股份有限公司 | Knowledge graph construction method and device and computer readable storage medium |
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