CN116795995A - Knowledge graph construction method, device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a knowledge graph construction method, a knowledge graph construction device, computer equipment and a storage medium. Belongs to the technical field of artificial intelligence, and the method comprises the following steps: according to the table content of each data table, the main key entity and the attribute entity corresponding to each data table are determined, according to the relation between the main key entity of each data table and the attribute entities of other data tables except the data table in each data table, the association relation between the main key entities of each data table is determined, according to the main key entity and the attribute entity of the data table corresponding to the main key entity with the association relation, the relation information between the main key entities with the association relation is determined, and according to the main key entity, the attribute entity corresponding to each data table, the association relation between the main key entities of each data table and the relation information between the main key entities with the association relation, the knowledge graph can be automatically constructed, so that the construction efficiency is higher, and the constructed knowledge graph is more accurate.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to a knowledge graph construction method, a knowledge graph construction device, a computer device, and a storage medium.
Background
With the rapid development of informatization, historical data precipitation with different magnitudes exists in various industries. The use of existing data resources to convert accumulated data into a high quality knowledge base is an important means of improving competitiveness. The knowledge graph describes entity objects and interrelationships thereof in the form of graph data structures, and can intuitively present complex entity relations in the real world. The method based on the knowledge graph can effectively utilize the relation between the data, improves the utilization level of the data, and is widely applied to the vertical fields of finance, electronic commerce and the like at present.
At present, when the knowledge graph is constructed aiming at the structured data, each data table needs to be manually determined and screened, the entity data in the data table is acquired, and then the knowledge graph is manually constructed based on the relation among the entity data, so that the efficiency is low, and the accuracy of the constructed knowledge graph is low.
Disclosure of Invention
Based on the foregoing, it is necessary to provide a knowledge graph construction method, apparatus, computer device and storage medium capable of automatically and accurately constructing a knowledge graph.
In a first aspect, the present application provides a knowledge graph construction method. The method comprises the following steps:
Determining a main key entity and an attribute entity corresponding to each data table according to the table content of each data table;
determining the association relationship between the main key entities of each data table according to the relationship between the main key entity of each data table and the attribute entities of other data tables except the data table in each data table;
determining relation information between the main key entities with the association relation according to the main key entities and the attribute entities of the data table corresponding to the main key entities with the association relation;
and constructing a knowledge graph according to the main key entity corresponding to each data table, the attribute entity, the association relation among the main key entities of each data table and the relation information among the main key entities with the association relation.
In one embodiment, determining the primary key entity and the attribute entity corresponding to each data table according to the table content of each data table includes:
determining candidate key fields of each data table according to the table content of each data table;
determining a main key entity of each data table from candidate key fields of each data table;
and taking other candidate key fields except the main key entity in the candidate key fields of each data table as attribute entities of each data table.
In one embodiment, determining the association relationship between the primary key entities of the data tables according to the relationship between the primary key entity of each data table and the attribute entities of the other data tables except the data table, includes:
judging whether related data tables with attribute entities consistent with the main key entities of the data tables exist in other data tables according to each data table;
if the data table exists, determining that the main key entity of the data table has an association relationship with the main key entity of the associated data table.
In one embodiment, the construction of the knowledge graph according to the main key entity corresponding to each data table, the attribute entity, the association relationship between the main key entities of each data table, and the relationship information between the main key entities with the association relationship includes:
creating an entity relation data table according to the corresponding main key entities of the data tables, the attribute entities, the incidence relation among the main key entities of the data tables and the relation information among the main key entities with the incidence relation;
and constructing a knowledge graph according to the entity relation data table.
In one embodiment, creating an entity relationship data table according to the primary key entity corresponding to each data table, the attribute entity, the association relationship between the primary key entities of each data table, and the relationship information between the primary key entities having the association relationship, includes:
Creating an entity data table according to the main key entity and the attribute entity corresponding to each data table;
creating a relation data table according to the association relation between the main key entities of each data table and the relation information between the main key entities with the association relation;
and creating an entity relation data table according to the entity data table and the relation data table.
In one embodiment, the association information includes a relationship type, a relationship name, a relationship attribute, and an association entity.
In one embodiment, the construction of the knowledge graph according to the main key entity corresponding to each data table, the attribute entity, the association relationship between the main key entities of each data table, and the relationship information between the main key entities with the association relationship includes:
determining node names and node attributes of the knowledge graph nodes according to the main key entities and the attribute entities corresponding to the data tables;
determining topological relation and relation information between knowledge graph nodes according to the association relation between the main key entities of each data table and the relation information between the main key entities with the association relation;
and constructing a knowledge graph according to the node names and the node attributes of the knowledge graph nodes and the topological relation and relation information between the knowledge graph nodes.
In a second aspect, the application further provides a knowledge graph construction device. The device comprises:
the first determining module is used for determining a main key entity and an attribute entity corresponding to each data table according to the table content of each data table;
the second determining module is used for determining the association relationship between the main key entities of each data table according to the relationship between the main key entity of each data table and the attribute entities of other data tables except the data table;
the third determining module is used for determining relation information between the main key entities with the association relation according to the main key entities and the attribute entities of the data table corresponding to the main key entities with the association relation;
the construction module is used for constructing a knowledge graph according to the corresponding main key entities of the data tables, the attribute entities, the incidence relations among the main key entities of the data tables and the relation information among the main key entities with the incidence relations.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
determining a main key entity and an attribute entity corresponding to each data table according to the table content of each data table;
Determining the association relationship between the main key entities of each data table according to the relationship between the main key entity of each data table and the attribute entities of other data tables except the data table in each data table;
determining relation information between the main key entities with the association relation according to the main key entities and the attribute entities of the data table corresponding to the main key entities with the association relation;
and constructing a knowledge graph according to the main key entity corresponding to each data table, the attribute entity, the association relation among the main key entities of each data table and the relation information among the main key entities with the association relation.
In a fourth aspect, the present application also provides a computer-readable storage medium. A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
determining a main key entity and an attribute entity corresponding to each data table according to the table content of each data table;
determining the association relationship between the main key entities of each data table according to the relationship between the main key entity of each data table and the attribute entities of other data tables except the data table in each data table;
determining relation information between the main key entities with the association relation according to the main key entities and the attribute entities of the data table corresponding to the main key entities with the association relation;
And constructing a knowledge graph according to the main key entity corresponding to each data table, the attribute entity, the association relation among the main key entities of each data table and the relation information among the main key entities with the association relation.
In a fifth aspect, the present application also provides a computer program product. Computer program product comprising a computer program which, when executed by a processor, realizes the steps of:
determining a main key entity and an attribute entity corresponding to each data table according to the table content of each data table;
determining the association relationship between the main key entities of each data table according to the relationship between the main key entity of each data table and the attribute entities of other data tables except the data table in each data table;
determining relation information between the main key entities with the association relation according to the main key entities and the attribute entities of the data table corresponding to the main key entities with the association relation;
and constructing a knowledge graph according to the main key entity corresponding to each data table, the attribute entity, the association relation among the main key entities of each data table and the relation information among the main key entities with the association relation.
According to the knowledge graph construction method, the knowledge graph construction device, the computer equipment and the storage medium, the primary key entity and the attribute entity corresponding to each data table can be determined according to the table content of each data table, then according to the relation between the primary key entity of each data table and the attribute entity of each data table except the data table, the association relation between the primary key entities of each data table can be determined, according to the primary key entity and the attribute entity of the data table corresponding to the primary key entity with the association relation, the relation information between the primary key entities with the association relation is determined, and finally according to the primary key entity, the attribute entity and the association relation information between the primary key entities with the association relation, the knowledge graph construction can be automatically realized.
Drawings
Fig. 1 is an application environment diagram of a knowledge graph construction method provided in this embodiment;
fig. 2 is a flow chart of a first knowledge graph construction method provided in this embodiment;
fig. 3 is a schematic flow chart of knowledge graph construction provided in the present embodiment;
fig. 4 is a schematic flow chart of creating an entity relationship data table according to the present embodiment;
FIG. 5 is a diagram of an entity data table structure according to the present embodiment;
FIG. 6 is a schematic diagram of a relational data table according to the present embodiment;
fig. 7 is a flow chart of a second knowledge graph construction method according to the present embodiment;
fig. 8 is a block diagram of the knowledge graph construction apparatus according to the present embodiment;
fig. 9 is an internal structural diagram of the computer device provided in the present embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in FIG. 1. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing acquired data of the abnormal data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a knowledge graph construction method.
In one embodiment, a knowledge graph construction method is provided, as shown in fig. 2, including the following steps:
s201, determining a main key entity and an attribute entity corresponding to each data table according to the table contents of each data table.
Wherein the data table refers to a structured data table. Table contents refer to the contents in the structured data table. The primary key entity is an entity which is obtained by analyzing based on table contents in the structured data table and determining; the primary key data is used for uniquely identifying each piece of data in the table, and the primary key cannot be empty and cannot be repeated. The attribute entity is a field in the data table that characterizes the attribute of the primary key entity.
An alternative implementation manner of this embodiment is as follows: and carrying out feature analysis on the table contents of each data table, and determining a main key entity and an attribute entity corresponding to each data table according to the feature analysis result. For example, a certain data table is analyzed, and the contents in the data table are mainly the name, the number, the score, the age and the like of the student. The student number may be used as a primary key entity and the student name, the score of each family, the age, etc. may be used as attribute entities.
Another alternative implementation of this embodiment is: and determining candidate key fields of each data table according to the table content of each data table, determining a main key entity of each data table from the candidate key fields of each data table, and taking other candidate key fields except the main key entity in the candidate key fields of each data table as attribute entities of each data table. The candidate key field is a content field in the data table, for example, a header field in the data table.
According to the table content of each data table, candidate key fields of each data table are determined, and one optional implementation mode is as follows: and acquiring the header fields in each data table as candidate key fields of each data table according to the table contents of each data table.
According to the table content of each data table, candidate key fields of each data table are determined, and another alternative implementation mode is as follows: according to the table content of each data table, acquiring the data fields of each row or each column in each data table, and carrying out feature analysis on the data fields of each row or each column in each data table to obtain the feature semantic field corresponding to the data fields of each row or each column as the candidate key field of each data table. For example, for performing feature analysis on a data field (for example 120103 xxxxxxxxxxx) in a certain column in the data table, if the data field is an identification card number as a result of feature analysis, the identification card number is used as a feature semantic field of the column, namely, a candidate key field.
An alternative implementation manner of determining the primary key entity of each data table from the candidate key fields of each data table is as follows: and carrying out semantic analysis on candidate key fields of each data table, and determining a header field capable of uniquely identifying one record as a primary key entity.
Another alternative implementation of determining the primary key entity of each data table from the candidate key fields of each data table is: and inputting the candidate key fields of each data table into a trained neural network model, and outputting a header field capable of uniquely identifying one record by the trained neural network model as a main key entity.
S202, according to the relation between the main key entity of each data table and the attribute entities of other data tables except the data table, determining the association relation between the main key entities of the data tables.
Optionally, in this embodiment, for each data table, it is determined whether there is an associated data table in which attribute entities are consistent with primary key entities of the data table in other data tables, if there is an associated relationship between a primary key entity of the data table and a primary key entity of the associated data table, if there is no associated relationship between a primary key entity of the data table and a primary key entity of the associated data table, it is determined that there is no associated relationship between the primary key entity of the data table and the primary key entity of the associated data table. The associated data table means that the main key entity of one data table is consistent with the attribute entity of the other data table, and the two data tables are indicated to belong to the associated data table.
S203, determining relation information between the primary key entities with the association relation according to the primary key entities and the attribute entities of the data table corresponding to the primary key entities with the association relation.
The relationship information refers to the related information of the relationship between the primary keys of the two data tables with the association relationship, such as relationship type, relationship attribute, relationship name, and the like.
An alternative implementation manner of this embodiment is as follows: and according to the analysis and comparison result, determining the relation information between the main key entities with the association relation. For example, a student information table and a teacher information table having an associated relationship, through comparison analysis (such as comparing the grades, courses, classes, etc. in the table contents), relationship information (such as a relationship name being a teacher-student relationship, a relationship type being an educational relationship, and relationship attributes including the grades, courses, classes, etc.) can be determined.
Another alternative implementation of this embodiment is: inputting the main key entity and the attribute entity of the data table corresponding to the main key entity with the association relationship into a first model (such as a clustering model), obtaining the entity label corresponding to the data table with the association relationship (each data table corresponds to one entity label), inputting the entity labels of different data tables with the association relationship into a second model (such as a semantic analysis model), and analyzing the relationship information among different entity labels by the second model based on the entity labels. For example, the primary key entity and the attribute entity of the student data table and the teacher data table corresponding to the primary key entity with the association relationship are input into a first model, the first model is used as a student label according to the entity label output by the primary key entity and the attribute entity of the student data table, the first model is used as a teacher label according to the entity label output by the primary key entity and the attribute entity of the teacher data table, then the student label and the teacher label are input into a second model, semantic analysis is performed on the student label and the teacher label by the second label, and relationship information among the primary key entities with the association relationship is determined.
It should be noted that, when the knowledge graph is constructed, the entity tag in this embodiment may be used as a node name of the data table in the knowledge graph.
S204, constructing a knowledge graph according to the corresponding main key entities of the data tables, the attribute entities, the association relations among the main key entities of the data tables and the relation information among the main key entities with the association relations.
The knowledge graph refers to a semantic network, is a graph-based data structure, and comprises entity nodes and relations among the entity nodes.
Optionally, in this embodiment, the node name and the node attribute of the knowledge graph node may be determined according to the primary key entity and the attribute entity corresponding to each data table, the topology relationship and the relationship information between the knowledge graph nodes may be determined according to the association relationship between the primary key entities of each data table and the relationship information between the primary key entities having the association relationship, and the knowledge graph may be constructed according to the node name and the topology relationship and the relationship information between the node attribute of the knowledge graph node and the knowledge graph node. Specifically, node names are determined according to the primary key entities corresponding to the data tables, node attributes are determined according to the attribute entities corresponding to the data tables, a knowledge graph is constructed according to the association relationship between the primary key entities of the data tables and the relationship information between the primary key entities with the association relationship (the association relationship can be regarded as a connecting line between nodes in the knowledge graph and the relationship information is used for defining the related information of the connecting line), the topological relationship between the nodes of the knowledge graph and the relationship information are determined, and the knowledge graph is constructed according to the node names of the nodes of the knowledge graph and the topological relationship and the relationship information between the node attributes and the nodes of the knowledge graph.
According to the table contents of the data tables, the primary key entity and the attribute entity corresponding to the data tables can be determined, then according to the relationship between the primary key entity of each data table and the attribute entities of other data tables except the data table in the data tables, the association relationship between the primary key entities of the data tables can be determined, according to the primary key entity and the attribute entity of the data table corresponding to the primary key entity with the association relationship, the relationship information between the primary key entities with the association relationship is determined, and finally according to the association relationship between the primary key entity corresponding to the data tables, the attribute entity, the primary key entity with the association relationship and the relationship information between the primary key entities with the association relationship, the knowledge graph can be automatically constructed.
In one embodiment, in order to construct a knowledge graph more quickly and accurately, as shown in fig. 3, in S204, an alternative implementation manner includes:
s301, creating an entity relation data table according to the corresponding main key entity of each data table, the attribute entity, the association relation among the main key entities of each data table and the relation information among the main key entities with the association relation.
The entity relation data table refers to a data table which can embody attribute information of a main key entity and also can embody association information and relation information of a main key entity key, and the format of the entity relation data table can be, but is not limited to, xls format, csv format, a format corresponding to a MySQL database file and the like.
An alternative implementation manner of this embodiment is as follows: and inputting the main key entity corresponding to each data table, the attribute entity, the association relation among the main key entities of each data table, the relation information among the main key entities with the association relation and the like into the neural network model, and outputting the entity relation data table by the neural network model.
Another alternative implementation of this embodiment is: determining entity nodes according to the main key entities and attribute entities corresponding to the data tables, determining attribute information of the entity nodes according to the attribute entities, determining edges between the entity nodes according to the association relation between the main key entities of the data tables, determining edge attributes according to the relation information between the main key entities with the association relation, and creating an entity relation data table according to the entity nodes, the attribute information of the entity nodes, the edges between the entity nodes and the edge attributes between the entity nodes.
S302, constructing a knowledge graph according to the entity relation data table.
An alternative implementation manner of this embodiment is as follows: and extracting the internal entity data and the relation data according to the entity relation data table, comprehensively analyzing the entity data and the relation data, and constructing a knowledge graph. The entity data comprises a main key entity and an attribute entity corresponding to each data table, and the relationship data comprises the association relationship between the main key entities of each data table and the relationship information between the main key entities with the association relationship.
Another alternative implementation of this embodiment is: and (3) entering the entity relation data table into a graph database, and constructing a knowledge graph by the graph database based on the entity relation data table.
In this embodiment, an entity relationship data table is created according to the corresponding primary key entities of the data tables, attribute entities, association relationships among the primary key entities of the data tables, and relationship information among the primary key entities having the association relationships, and a knowledge graph can be quickly constructed according to the entity relationship data table.
In one embodiment, in order to quickly construct the entity relationship data table, as shown in fig. 4, an alternative implementation manner of S301 is as follows:
S401, creating an entity data table according to the main key entity and the attribute entity corresponding to each data table.
The entity data table refers to a topological structure table created based on the main key entity and the attribute entity.
Optionally, in this embodiment, according to the primary key entity of each data table, the name and the type of the entity node in the entity data table are determined, and according to the attribute entity, the entity attribute of the entity node is determined. Specifically, as shown in fig. 5, the primary node of the entity data table is an entity subject node, the secondary node is an entity name (each entity has a unique name, the knowledge graph generally has a plurality of entities, that is, the number of entity names is generally a plurality of entity names), the tertiary node is an entity type and an entity attribute corresponding to the entity name of the secondary node, the quaternary node is an attribute branch of the entity attribute of the tertiary node, and the five-level node is an attribute name and a data source corresponding to the attribute branch of the quaternary node. The data source refers to a data source of a data table to which the entity attribute belongs, for example, a XX database, a XX platform, etc.
S402, creating a relation data table according to the association relation between the main key entities of each data table and the relation information between the main key entities with the association relation.
The relationship data table is a topology structure table created based on the association relationship between the entity primary keys and the relationship information between the entity primary keys with the association relationship.
Optionally, in this embodiment, according to the association relationship between the primary key entities of each data table, the association entity may be determined, and according to the relationship information between the primary key entities having the association relationship, the relationship name, the relationship type and the relationship attribute in the relationship information may be determined. The associated entity refers to a main key entity with an associated relation. Specifically, as shown in fig. 6, a primary node of the entity data table is a relationship subject node, a secondary node is a relationship name, a tertiary node is a relationship type, an entity attribute and an associated entity corresponding to the relationship name of the secondary node, a quaternary node is an attribute branch of the relationship attribute of the tertiary node, and a five-stage node is an attribute name corresponding to the attribute branch of the quaternary node.
S403, creating an entity relation data table according to the entity data table and the relation data table.
Alternatively, the entity data table and the relationship data table may be imported into the same data table, thereby obtaining the entity relationship data table. The entity data table and the relationship data table may also be stored as a data table group as entity relationship data tables. That is, the entity data table and the relationship data table which are associated with each other are stored separately in the same file as the data table group.
According to the embodiment, the entity data table is created according to the main key entity and the attribute entity corresponding to each data table, the relation data table is created according to the association relation between the main key entities of each data table and the relation information between the main key entities with the association relation, and the entity relation data table can be quickly created according to the entity data table and the relation data table.
In one embodiment, as shown in fig. 7, an alternative implementation manner of the knowledge graph construction method includes:
s701, determining candidate key fields of each data table according to the table contents of each data table.
S702, determining the main key entity of each data table from the candidate key fields of each data table.
S703, using other candidate key fields except the main key entity in the candidate key fields of each data table as attribute entities of each data table.
S704, judging whether the associated data tables with the attribute entities consistent with the main key entities of the data tables exist in other data tables according to each data table.
And S705, if the data table exists, determining that the main key entity of the data table has an association relationship with the main key entity of the associated data table.
S706, determining the relation information between the main key entities with the association relation according to the main key entities and the attribute entities of the data table corresponding to the main key entities with the association relation. The association information comprises a relationship type, a relationship name, a relationship attribute and an association entity.
S707, creating an entity data table according to the main key entity and the attribute entity corresponding to each data table.
S708, creating a relation data table according to the association relation between the main key entities of each data table and the relation information between the main key entities with the association relation.
S709, creating an entity relation data table according to the entity data table and the relation data table.
S7010, constructing a knowledge graph according to the entity relation data table.
According to the table contents of the data tables, the primary key entity and the attribute entity corresponding to the data tables can be determined, then according to the relationship between the primary key entity of each data table and the attribute entities of other data tables except the data table in the data tables, the association relationship between the primary key entities of the data tables can be determined, according to the primary key entity and the attribute entity of the data table corresponding to the primary key entity with the association relationship, the relationship information between the primary key entities with the association relationship is determined, and finally according to the association relationship between the primary key entity corresponding to the data tables, the attribute entity, the primary key entity with the association relationship and the relationship information between the primary key entities with the association relationship, the knowledge graph can be automatically constructed.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a knowledge graph construction device for realizing the knowledge graph construction method. The implementation scheme of the solution provided by the device is similar to the implementation scheme described in the above method, so the specific limitation in the embodiment of one or more knowledge graph construction devices provided below may refer to the limitation of the knowledge graph construction method hereinabove, and will not be described herein.
In one embodiment, as shown in fig. 8, there is provided a knowledge graph construction apparatus 1, including: a first determination module 10, a first determination module 20, a third determination module 30, and a build module 40, wherein:
the first determining module 10 is configured to determine, according to table contents of each data table, a primary key entity and an attribute entity corresponding to each data table.
And the second determining module 20 is configured to determine an association relationship between the primary key entities of each data table according to the relationship between the primary key entity of each data table and the attribute entities of other data tables except the data table.
The third determining module 30 is configured to determine relationship information between the primary key entities having the association relationship according to the primary key entities and the attribute entities of the data table corresponding to the primary key entities having the association relationship.
The construction module 40 is configured to construct a knowledge graph according to the primary key entities corresponding to the data tables, the attribute entities, the association relationships between the primary key entities of the data tables, and the relationship information between the primary key entities having the association relationships.
In one embodiment, the first determining module 10 in fig. 8 above further includes:
and the first determining unit is used for determining candidate key fields of each data table according to the table contents of each data table.
And the second determining unit is used for determining the main key entity of each data table from the candidate key fields of each data table.
And the third determining unit is used for taking other candidate key fields except the main key entity in the candidate key fields of each data table as attribute entities of each data table.
In one embodiment, the second determining module 20 in fig. 8 above, further includes:
and the judging unit is used for judging whether the associated data tables with the attribute entities consistent with the main key entity of the data table exist in other data tables according to each data table.
And the fourth determining unit is used for determining that the main key entity of the data table and the main key entity of the associated data table have an association relationship if the main key entity exists.
In one embodiment, building block 40 of FIG. 8 above, further comprises:
the creation unit is used for creating the entity relation data table according to the corresponding main key entity of each data table, the attribute entity, the association relation among the main key entities of each data table and the relation information among the main key entities with the association relation.
And the construction unit is used for constructing a knowledge graph according to the entity relation data table.
In one embodiment, the creating unit in the above embodiment is further specifically configured to: creating an entity data table according to the main key entity and the attribute entity corresponding to each data table; creating a relation data table according to the association relation between the main key entities of each data table and the relation information between the main key entities with the association relation; and creating an entity relation data table according to the entity data table and the relation data table.
In one embodiment, the building module in the foregoing embodiment is further specifically configured to: determining node names and node attributes of the knowledge graph nodes according to the main key entities and the attribute entities corresponding to the data tables; determining topological relation and relation information between knowledge graph nodes according to the association relation between the main key entities of each data table and the relation information between the main key entities with the association relation; and constructing a knowledge graph according to the node names and the node attributes of the knowledge graph nodes and the topological relation and relation information between the knowledge graph nodes.
The modules in the knowledge graph construction device can be realized in whole or in part by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 9. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store the relevant content and data of each data table. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a knowledge graph construction method.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 9 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
determining a main key entity and an attribute entity corresponding to each data table according to the table content of each data table;
determining the association relationship between the main key entities of each data table according to the relationship between the main key entity of each data table and the attribute entities of other data tables except the data table in each data table;
determining relation information between the main key entities with the association relation according to the main key entities and the attribute entities of the data table corresponding to the main key entities with the association relation;
and constructing a knowledge graph according to the main key entity corresponding to each data table, the attribute entity, the association relation among the main key entities of each data table and the relation information among the main key entities with the association relation.
In one embodiment, the processor when executing the computer program further performs the steps of: determining a main key entity and an attribute entity corresponding to each data table according to the table content of each data table, wherein the main key entity and the attribute entity comprise:
determining candidate key fields of each data table according to the table content of each data table;
determining a main key entity of each data table from candidate key fields of each data table;
and taking other candidate key fields except the main key entity in the candidate key fields of each data table as attribute entities of each data table.
In one embodiment, the processor when executing the computer program further performs the steps of: determining the association relationship between the primary key entities of the data tables according to the relationship between the primary key entity of each data table and the attribute entities of other data tables except the data table, wherein the association relationship comprises the following steps:
judging whether related data tables with attribute entities consistent with the main key entities of the data tables exist in other data tables according to each data table;
if the data table exists, determining that the main key entity of the data table has an association relationship with the main key entity of the associated data table.
In one embodiment, the processor when executing the computer program further performs the steps of: according to the corresponding main key entity, attribute entity and the association relation between the main key entities of the data tables and the relation information between the main key entities with the association relation, constructing a knowledge graph, which comprises the following steps:
Creating an entity relation data table according to the corresponding main key entities of the data tables, the attribute entities, the incidence relation among the main key entities of the data tables and the relation information among the main key entities with the incidence relation;
and constructing a knowledge graph according to the entity relation data table.
In one embodiment, the processor when executing the computer program further performs the steps of: creating an entity relationship data table according to the corresponding main key entities of the data tables, attribute entities, the association relationship among the main key entities of the data tables and the relationship information among the main key entities with the association relationship, wherein the entity relationship data table comprises:
creating an entity data table according to the main key entity and the attribute entity corresponding to each data table;
creating a relation data table according to the association relation between the main key entities of each data table and the relation information between the main key entities with the association relation;
and creating an entity relation data table according to the entity data table and the relation data table.
In one embodiment, the processor when executing the computer program further performs the steps of: the association information includes a relationship type, a relationship name, a relationship attribute, and an association entity.
In one embodiment, the processor when executing the computer program further performs the steps of: according to the corresponding main key entity, attribute entity and the association relation between the main key entities of the data tables and the relation information between the main key entities with the association relation, constructing a knowledge graph, which comprises the following steps:
Determining node names and node attributes of the knowledge graph nodes according to the main key entities and the attribute entities corresponding to the data tables;
determining topological relation and relation information between knowledge graph nodes according to the association relation between the main key entities of each data table and the relation information between the main key entities with the association relation;
and constructing a knowledge graph according to the node names and the node attributes of the knowledge graph nodes and the topological relation and relation information between the knowledge graph nodes.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
determining a main key entity and an attribute entity corresponding to each data table according to the table content of each data table;
determining the association relationship between the main key entities of each data table according to the relationship between the main key entity of each data table and the attribute entities of other data tables except the data table in each data table;
determining relation information between the main key entities with the association relation according to the main key entities and the attribute entities of the data table corresponding to the main key entities with the association relation;
And constructing a knowledge graph according to the main key entity corresponding to each data table, the attribute entity, the association relation among the main key entities of each data table and the relation information among the main key entities with the association relation.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining a main key entity and an attribute entity corresponding to each data table according to the table content of each data table, wherein the main key entity and the attribute entity comprise:
determining candidate key fields of each data table according to the table content of each data table;
determining a main key entity of each data table from candidate key fields of each data table;
and taking other candidate key fields except the main key entity in the candidate key fields of each data table as attribute entities of each data table.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining the association relationship between the primary key entities of the data tables according to the relationship between the primary key entity of each data table and the attribute entities of other data tables except the data table, wherein the association relationship comprises the following steps:
judging whether related data tables with attribute entities consistent with the main key entities of the data tables exist in other data tables according to each data table;
If the data table exists, determining that the main key entity of the data table has an association relationship with the main key entity of the associated data table.
In one embodiment, the computer program when executed by the processor further performs the steps of: according to the corresponding main key entity, attribute entity and the association relation between the main key entities of the data tables and the relation information between the main key entities with the association relation, constructing a knowledge graph, which comprises the following steps:
creating an entity relation data table according to the corresponding main key entities of the data tables, the attribute entities, the incidence relation among the main key entities of the data tables and the relation information among the main key entities with the incidence relation;
and constructing a knowledge graph according to the entity relation data table.
In one embodiment, the computer program when executed by the processor further performs the steps of: creating an entity relationship data table according to the corresponding main key entities of the data tables, attribute entities, the association relationship among the main key entities of the data tables and the relationship information among the main key entities with the association relationship, wherein the entity relationship data table comprises:
creating an entity data table according to the main key entity and the attribute entity corresponding to each data table;
Creating a relation data table according to the association relation between the main key entities of each data table and the relation information between the main key entities with the association relation;
and creating an entity relation data table according to the entity data table and the relation data table.
In one embodiment, the computer program when executed by the processor further performs the steps of: the association information includes a relationship type, a relationship name, a relationship attribute, and an association entity.
In one embodiment, the computer program when executed by the processor further performs the steps of: according to the corresponding main key entity, attribute entity and the association relation between the main key entities of the data tables and the relation information between the main key entities with the association relation, constructing a knowledge graph, which comprises the following steps:
determining node names and node attributes of the knowledge graph nodes according to the main key entities and the attribute entities corresponding to the data tables;
determining topological relation and relation information between knowledge graph nodes according to the association relation between the main key entities of each data table and the relation information between the main key entities with the association relation;
and constructing a knowledge graph according to the node names and the node attributes of the knowledge graph nodes and the topological relation and relation information between the knowledge graph nodes.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
determining a main key entity and an attribute entity corresponding to each data table according to the table content of each data table;
determining the association relationship between the main key entities of each data table according to the relationship between the main key entity of each data table and the attribute entities of other data tables except the data table in each data table;
determining relation information between the main key entities with the association relation according to the main key entities and the attribute entities of the data table corresponding to the main key entities with the association relation;
and constructing a knowledge graph according to the main key entity corresponding to each data table, the attribute entity, the association relation among the main key entities of each data table and the relation information among the main key entities with the association relation.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining a main key entity and an attribute entity corresponding to each data table according to the table content of each data table, wherein the main key entity and the attribute entity comprise:
determining candidate key fields of each data table according to the table content of each data table;
Determining a main key entity of each data table from candidate key fields of each data table;
and taking other candidate key fields except the main key entity in the candidate key fields of each data table as attribute entities of each data table.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining the association relationship between the primary key entities of the data tables according to the relationship between the primary key entity of each data table and the attribute entities of other data tables except the data table, wherein the association relationship comprises the following steps:
judging whether related data tables with attribute entities consistent with the main key entities of the data tables exist in other data tables according to each data table;
if the data table exists, determining that the main key entity of the data table has an association relationship with the main key entity of the associated data table.
In one embodiment, the computer program when executed by the processor further performs the steps of: according to the corresponding main key entity, attribute entity and the association relation between the main key entities of the data tables and the relation information between the main key entities with the association relation, constructing a knowledge graph, which comprises the following steps:
creating an entity relation data table according to the corresponding main key entities of the data tables, the attribute entities, the incidence relation among the main key entities of the data tables and the relation information among the main key entities with the incidence relation;
And constructing a knowledge graph according to the entity relation data table.
In one embodiment, the computer program when executed by the processor further performs the steps of: creating an entity relationship data table according to the corresponding main key entities of the data tables, attribute entities, the association relationship among the main key entities of the data tables and the relationship information among the main key entities with the association relationship, wherein the entity relationship data table comprises:
creating an entity data table according to the main key entity and the attribute entity corresponding to each data table;
creating a relation data table according to the association relation between the main key entities of each data table and the relation information between the main key entities with the association relation;
and creating an entity relation data table according to the entity data table and the relation data table.
In one embodiment, the computer program when executed by the processor further performs the steps of: the association information includes a relationship type, a relationship name, a relationship attribute, and an association entity.
In one embodiment, the construction of the knowledge graph according to the main key entity corresponding to each data table, the attribute entity, the association relationship between the main key entities of each data table, and the relationship information between the main key entities with the association relationship includes:
Determining node names and node attributes of the knowledge graph nodes according to the main key entities and the attribute entities corresponding to the data tables;
determining topological relation and relation information between knowledge graph nodes according to the association relation between the main key entities of each data table and the relation information between the main key entities with the association relation;
and constructing a knowledge graph according to the node names and the node attributes of the knowledge graph nodes and the topological relation and relation information between the knowledge graph nodes.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (11)
1. The knowledge graph construction method is characterized by comprising the following steps of:
determining a main key entity and an attribute entity corresponding to each data table according to the table content of each data table;
determining the association relationship between the main key entities of each data table according to the relationship between the main key entity of each data table and the attribute entities of other data tables except the data table in each data table;
Determining relation information between the main key entities with the association relation according to the main key entities and the attribute entities of the data table corresponding to the main key entities with the association relation;
and constructing a knowledge graph according to the main key entity corresponding to each data table, the attribute entity, the association relation among the main key entities of each data table and the relation information among the main key entities with the association relation.
2. The method according to claim 1, wherein determining the primary key entity and the attribute entity corresponding to each data table according to the table contents of each data table comprises:
determining candidate key fields of each data table according to the table content of each data table;
determining a main key entity of each data table from the candidate key fields of each data table;
and taking other candidate key fields except the main key entity in the candidate key fields of each data table as attribute entities of each data table.
3. The method according to claim 1, wherein determining the association relationship between the primary key entities of the data tables according to the relationship between the primary key entity of each data table and the attribute entities of the other data tables except the data table, comprises:
Judging whether related data tables with attribute entities consistent with the main key entities of the data tables exist in other data tables according to each data table;
if the data table exists, determining that the main key entity of the data table has an association relationship with the main key entity of the associated data table.
4. The method according to claim 1, wherein the constructing a knowledge graph according to the main key entity corresponding to each data table, the attribute entity, the association relationship between the main key entities of each data table, and the relationship information between the main key entities having the association relationship includes:
creating an entity relation data table according to the corresponding main key entities of the data tables, the attribute entities, the incidence relation among the main key entities of the data tables and the relation information among the main key entities with the incidence relation;
and constructing a knowledge graph according to the entity relation data table.
5. The method of claim 4, wherein creating the entity relationship data table according to the main key entity corresponding to each data table, the attribute entity, the association relationship between the main key entities of each data table, and the relationship information between the main key entities having the association relationship, comprises:
Creating an entity data table according to the main key entity and the attribute entity corresponding to each data table;
creating a relation data table according to the association relation between the main key entities of each data table and the relation information between the main key entities with the association relation;
and creating an entity relation data table according to the entity data table and the relation data table.
6. The method of any of claims 1-5, wherein the association information includes a relationship type, a relationship name, a relationship attribute, and an association entity.
7. The method according to claim 1, wherein the constructing a knowledge graph according to the main key entity corresponding to each data table, the attribute entity, the association relationship between the main key entities of each data table, and the relationship information between the main key entities having the association relationship includes:
determining node names and node attributes of the knowledge graph nodes according to the main key entities and the attribute entities corresponding to the data tables;
determining topological relation and relation information between knowledge graph nodes according to the association relation between the main key entities of each data table and the relation information between the main key entities with the association relation;
and constructing a knowledge graph according to the node names and the node attributes of the knowledge graph nodes and the topological relation and relation information between the knowledge graph nodes.
8. The knowledge graph construction device is characterized by comprising:
the first determining module is used for determining a main key entity and an attribute entity corresponding to each data table according to the table content of each data table;
the second determining module is used for determining the association relation between the main key entities of each data table according to the relation between the main key entity of each data table and the attribute entities of other data tables except the data table;
the third determining module is used for determining relation information between the main key entities with the association relation according to the main key entities and the attribute entities of the data table corresponding to the main key entities with the association relation;
the construction module is used for constructing a knowledge graph according to the corresponding main key entities of the data tables, the attribute entities, the incidence relations among the main key entities of the data tables and the relation information among the main key entities with the incidence relations.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the knowledge graph construction method of any one of claims 1 to 7 when the computer program is executed.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the knowledge-graph construction method of any one of claims 1 to 7.
11. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the knowledge-graph construction method of any one of claims 1 to 7.
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