CN111797243A - Knowledge graph data system construction method, system, terminal and readable storage medium - Google Patents
Knowledge graph data system construction method, system, terminal and readable storage medium Download PDFInfo
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- CN111797243A CN111797243A CN202010631236.0A CN202010631236A CN111797243A CN 111797243 A CN111797243 A CN 111797243A CN 202010631236 A CN202010631236 A CN 202010631236A CN 111797243 A CN111797243 A CN 111797243A
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/31—Indexing; Data structures therefor; Storage structures
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
Abstract
The invention discloses a method, a system, a terminal and a readable storage medium for constructing a knowledge graph data system, wherein the method for constructing the knowledge graph data system comprises the following steps: obtaining sample tobacco commercial data, analyzing the sample tobacco commercial data and constructing a data model; constructing a mapping file capable of mapping relational data and resource description type framework data according to the data model; obtaining relational data in a preset tobacco business database according to the mapping file, and converting the relational data into resource description framework data; and storing the resource description framework type data into a preset database to construct a knowledge graph data system. The data in the knowledge map data system can be deeply mined by using the knowledge map data system, so that better data service is provided for tobacco business.
Description
Technical Field
The invention relates to the field of data system construction, in particular to a method, a system, a terminal and a readable storage medium for constructing a knowledge graph data system.
Background
At present, the data center of tobacco business is mainly based on the traditional database due to the constraint of traditional informatization construction, provides data storage, management and data analysis services, and meets the data requirements of certain businesses. However, with the continuous acceleration of the informatization process and the continuous increase of the intelligent data demand, data in the traditional data system are mutually independent, the management and integration of semantic relations among the data are lacked, good data service cannot be provided for tobacco business, and the current business demand cannot be met.
Therefore, there is a need to provide a method for constructing a knowledge-graph data system to solve the above technical problems.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a method, a system, a terminal and a readable storage medium for constructing a knowledge graph data system, and aims to solve the problems that data in a data system of tobacco business lacks management and integration of semantic relations among data and cannot provide good data service for tobacco business.
In order to achieve the purpose, the invention provides a method for constructing a knowledge graph data system, which comprises the following steps:
obtaining sample tobacco commercial data, analyzing the sample tobacco commercial data and constructing a data model;
constructing a mapping file capable of mapping relational data and resource description type framework data according to the data model;
obtaining relational data in a preset tobacco business database according to the mapping file, and converting the relational data into resource description framework data;
and storing the resource description framework type data into a preset database to construct a knowledge graph data system.
Preferably, the steps of obtaining sample tobacco business data, analyzing the sample tobacco business data and constructing a data model comprise:
acquiring sample tobacco business data, analyzing the structure and the characteristics of the sample tobacco business data through a preset identification model, and identifying concept class data and attribute class data of the sample tobacco business data and relational data between the concept class data and the attribute class data;
and constructing a data model according to the concept class data, the attribute class data and the relational data.
Preferably, the step of storing the resource description framework type data into a preset database and constructing a knowledge graph data system includes:
determining the type of the resource description framework type data;
if the resource description type frame data are batch data, storing the resource description type frame data into a preset database through a preset distributed computing engine;
if the resource description type frame data is instant data, storing the resource description type frame data into the preset database in batch through a preset online transaction processing system;
and constructing a knowledge graph data system according to the preset database.
Preferably, after the step of storing the resource description framework type data in a preset database and constructing a knowledge graph data system, the method includes:
and constructing a retrieval system so that a user can perform data retrieval on the knowledge graph data system through the retrieval system.
Preferably, the step of constructing a retrieval system for enabling a user to perform data retrieval at the knowledge-graph data system through the retrieval system comprises:
the method comprises the steps of constructing a search engine cluster comprising a plurality of search engines, establishing an individual index between the preset database and each search engine, and establishing and storing a composite index according to the individual indexes.
Preferably, the step of constructing a retrieval system to enable a user to perform data retrieval on the knowledge-graph data system through the retrieval system further comprises:
and setting a preset query language and an application program interface in the preset database so that a user can carry out data retrieval according to the preset query language and the application program corresponding to the application program interface.
Preferably, after the step of storing the resource description framework type data in a preset database and constructing a knowledge graph data system, the method includes:
acquiring invalid data and repeated data in the preset database;
deleting the invalid data and the duplicate data.
The invention also provides a system for constructing the knowledge graph data system, which comprises the following components:
the knowledge modeling module is used for acquiring sample tobacco business data, analyzing the sample tobacco business data and constructing a data model;
the knowledge acquisition module is used for constructing a mapping file capable of mapping relational data and resource description type frame data according to the data model;
the knowledge acquisition module is also used for acquiring relational data in a preset tobacco business database according to the mapping file and converting the relational data into resource description framework data;
and the knowledge storage module is used for storing the resource description framework type data into a preset database to construct a knowledge map data system.
The invention also proposes a terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method for constructing a knowledge-graph data system as described above when executing the computer program.
The invention also proposes a readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the method of construction of a knowledge-graph data system as described above.
According to the technical scheme, sample tobacco commercial data are obtained, analyzed and a data model is constructed; constructing a mapping file capable of mapping relational data and resource description type framework data according to the data model; obtaining relational data in a preset tobacco business database according to the mapping file, and converting the relational data into resource description framework data; and storing the resource description framework type data into a preset database to construct a knowledge graph data system. The data in the knowledge graph data system can be deeply mined by utilizing the knowledge graph data system, and better data service is provided for tobacco business.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
Fig. 1 is a schematic terminal structure diagram of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart diagram of a first embodiment of a method for constructing a knowledge-graph data system according to the invention;
FIG. 3 is a flow chart diagram of a second embodiment of the method for constructing a knowledge-graph data system of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that all the directional indicators (such as up, down, left, right, front, and rear … …) in the embodiment of the present invention are only used to explain the relative position relationship between the components, the movement situation, etc. in a specific posture (as shown in the drawing), and if the specific posture is changed, the directional indicator is changed accordingly.
In addition, the descriptions related to "first", "second", etc. in the present invention are only for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "connected," "secured," and the like are to be construed broadly, and for example, "secured" may be a fixed connection, a removable connection, or an integral part; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In addition, the technical solutions in the embodiments of the present invention may be combined with each other, but it must be based on the realization of those skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination of technical solutions should not be considered to exist, and is not within the protection scope of the present invention. It should be noted that the strong current hook and the weak current hook mentioned in the following embodiments are not provided for the type of the wire to which the hooks can be mounted, and are only for convenience of description.
The embodiment of the invention provides a method for constructing a knowledge graph data system, a terminal and a readable storage medium.
As shown in fig. 1, the method of the present invention is applicable to a terminal, and the terminal may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may comprise a touch-sensitive pad, touch screen, keyboard, and the optional user interface 1003 may also comprise a standard wired, wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the terminal structure shown in fig. 1 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a type of computer storage medium, may include an operating system, a network communication module, a user interface module, and a knowledge-graph data system builder.
The processor 1001 may be configured to invoke the knowledge-graph data system builder stored in the memory 1005 and perform the following operations:
obtaining sample tobacco commercial data, analyzing the sample tobacco commercial data and constructing a data model;
constructing a mapping file capable of mapping relational data and resource description type framework data according to the data model;
obtaining relational data in a preset tobacco business database according to the mapping file, and converting the relational data into resource description framework data;
and storing the resource description framework type data into a preset database to construct a knowledge graph data system.
Further, the processor 1001 may be configured to invoke the knowledge-graph data system builder stored in the memory 1005 to also perform the following operations:
acquiring sample tobacco business data, analyzing the structure and the characteristics of the sample tobacco business data through a preset identification model, and identifying concept class data and attribute class data of the sample tobacco business data and relational data between the concept class data and the attribute class data;
and constructing a data model according to the concept class data, the attribute class data and the relational data.
Further, the processor 1001 may be configured to invoke the knowledge-graph data system builder stored in the memory 1005 to also perform the following operations:
determining the type of the resource description framework type data;
if the resource description type frame data are batch data, storing the resource description type frame data into a preset database through a preset distributed computing engine;
if the resource description type frame data is instant data, storing the resource description type frame data into the preset database in batch through a preset online transaction processing system;
and constructing a knowledge graph data system according to the preset database.
Further, the processor 1001 may be configured to invoke the knowledge-graph data system builder stored in the memory 1005 to also perform the following operations:
and constructing a retrieval system so that a user can perform data retrieval on the knowledge graph data system through the retrieval system.
Further, the processor 1001 may be configured to invoke the knowledge-graph data system builder stored in the memory 1005 to also perform the following operations:
the method comprises the steps of constructing a search engine cluster comprising a plurality of search engines, establishing an individual index between the preset database and each search engine, and establishing and storing a composite index according to the individual indexes.
Further, the processor 1001 may be configured to invoke the knowledge-graph data system builder stored in the memory 1005 to also perform the following operations:
and setting a preset query language and an application program interface in the preset database so that a user can carry out data retrieval according to the preset query language and the application program corresponding to the application program interface.
Further, the processor 1001 may be configured to invoke the knowledge-graph data system builder stored in the memory 1005 to also perform the following operations:
acquiring invalid data and repeated data in the preset database;
deleting the invalid data and the duplicate data.
Based on the hardware structure, the invention provides various embodiments of the knowledge map data system construction method and the refrigerator control method.
Fig. 2 is a schematic flow chart of a method for constructing a knowledge-graph data system according to a first embodiment of the present invention. The construction method of the knowledge graph data system comprises the following steps:
s100, obtaining sample tobacco commercial data, analyzing the sample tobacco commercial data and constructing a data model;
in particular, the sample tobacco business data may be order data that includes information such as an order amount, an order retailer, an order number, and the like. Analyzing the sample tobacco business data and constructing a data model, and forming a data frame by analyzing the structure and the characteristics of the sample tobacco business data, wherein the data frame is used as the data model. And during construction of the data model, calling Prot é software to construct concept class data and attribute class data of the sample tobacco business data and relational data between the concept class data and the attribute class data, extracting to obtain relational data, and converting the relational data into a Resource Description Framework (RDF) representation format to obtain resource description framework data. The construction of the data model realizes the construction of a knowledge system in the knowledge graph, namely the construction of a mode layer of the knowledge graph.
Step S110, constructing a mapping file capable of mapping the relational data and the resource description type frame data according to the data model;
specifically, a mapping file capable of mapping relational data and resource description type framework data is constructed according to the data model, and the mapping file can be obtained by configuring relevant parameters in a D2RQ platform: jdbccurl (URL of JDBC link database), username (login name of database user), password (login password of database), drivercoss (name of database driving class), outfile.n3 (output file of mapping file), base uri (namespace of vocabulary), where the namespace of vocabulary is http:// localhost:2020/, and the prefix of vocabulary is generated according to http:// baseURI/vocab/resource/mode. base uri needs to be consistent with server uri.
For example, there is a mysql database named Academic, and a mapping file can be generated according to the Schema of Academic and stored in Academic. n3 by executing the following commands, and the specific parameter setting forms are as follows:
generate-Mapping-o Academic.n3-d com.mysql.jdbc.Driver-u test-p testjdbc:mysql://localhost/Academic
step S120, obtaining relational data in a preset tobacco business database according to the mapping file, and converting the relational data into resource description framework data;
specifically, after the mapping file is constructed, the relational database in the preset tobacco business database can be converted and accessed, and D2RQ Engine can be used by means of Jena API. Specifically, D2RQ, Jena, and ARQ related jar packets may be referenced and then operated directly through SPARQL query language. The preset tobacco business database can be a tobacco monopoly system database, a tobacco marketing system database, a tobacco logistics system database and a tobacco inner pipe system database.
Steps S110 and S120 implement automatic conversion of each relational data in the preset tobacco business database into resource description framework data.
And step S130, storing the resource description framework type data into a preset database, and constructing a knowledge graph data system.
Specifically, when the resource description framework type data is stored in a preset database, the data in the knowledge Graph data system can be persisted through a Graph database (Janus Graph). And aiming at the rear-end storage of the resource description framework type data, a distributed storage system (Hbase, Hadoop Database) is adopted. When the knowledge graph data system is constructed, a distributed computing engine (Apache Spark and Apache Hadoop) is mainly integrated for analyzing a graph database, and an ElasticSearch engine is adopted for full-text indexing. Storing resource description framework type data in the form of a graph is achieved.
Preferably, the resource description framework type data includes: batch data and instant data. The storage of batch data can be divided into: data uploading and data importing. The data upload is mainly a data file generated according to the resource description framework data obtained in step S120 and is imported to a distributed file system (HDFS). For data import, uploaded data is processed in batch by using a distributed computing engine, and the uploaded data is integrated with a preset tobacco business database through an OLAP (online analytical processing) interface of JanusGraph. Then directly storing the data into Hbase; the processing mode of the instant data is the same as that of the traditional database, and the interaction of the data is completed through a TinkerPop API and a Gremlin query language by an OLTP (online transaction processing) interface. Namely, the resource description framework type data is directly sent to the JanusGraph Server to finish the storage of the data.
In the embodiment, by constructing the knowledge-graph data system related to tobacco business, semantic relation among tobacco data is strengthened, and better data service can be provided for tobacco business.
Further, a second embodiment is proposed based on the first embodiment, and referring to fig. 3, in this embodiment, the step S100 includes:
s200, obtaining sample tobacco business data, analyzing the structure and the characteristics of the sample tobacco business data through a preset identification model, and identifying concept data and attribute data of the sample tobacco business data and relational data between the concept data and the attribute data;
step S210, constructing a data model according to the concept class data, the attribute class data and the relational data.
In this embodiment, the preset identification model may be an identification model based on a neural network, and the preset identification model is used to analyze the structure and characteristics of the sample tobacco business data, identify the concept class data and the attribute class data of the sample tobacco business data, and identify the relationship type data between the concept class data and the attribute class data, so as to construct the concept class, the relationship, the attribute and the example, and implement the data model construction of the sample tobacco business data.
Further, a third embodiment is proposed based on the first embodiment, and in this embodiment, the step S130 includes:
determining the type of the resource description framework type data;
specifically, the resource description framework data has two types: batch data and instant data.
If the resource description type frame data are batch data, storing the resource description type frame data into a preset database through a preset distributed computing engine;
specifically, when the resource description type frame data is batch data, the storage of the batch data may be divided into: data uploading and data importing. Data uploading is mainly to import the resource description type framework data into a distributed file system (HDFS). For data import, uploaded data are processed in batch by using a preset distributed computing engine, and are integrated with a preset tobacco business database through an OLAP (online analytical processing) interface of JanusGraph. The resource descriptive framework data is then directly stored into Hbase.
If the resource description type frame data is instant data, storing the resource description type frame data into the preset database in batch through a preset online transaction processing system;
specifically, when the resource description type frame data is instant data, the processing mode of the instant data is the same as that of a traditional database, and data interaction is completed through a TinkerPop API and a Gremlin query language by an OLTP (online transaction processing) interface. Namely, the resource description framework type data is directly sent to the JanusGraph Server to finish the storage of the data.
And constructing a knowledge graph data system according to the preset database.
Specifically, after the resource description type frame data is stored in the preset database, the data is stored in a graph form, and a knowledge graph data system can be constructed according to the preset database.
In this embodiment, by determining the type of the resource description framework type data, different types of resource description framework type data are stored in the preset database in different ways, which is beneficial to improving the storage efficiency of data.
Further, a fourth embodiment is proposed based on the third embodiment, and in this embodiment, after the step S130, the method includes:
and constructing a retrieval system so that a user can perform data retrieval on the knowledge graph data system through the retrieval system.
In this embodiment, for the constructed knowledge graph data system, in order to enable a user to query from the preset database, a retrieval system may be constructed, and the user may perform data retrieval through the retrieval system.
Further, a fifth embodiment and a sixth embodiment are proposed based on the fourth embodiment, and in the fifth embodiment and the sixth embodiment, the step of constructing a retrieval system to enable a user to perform data retrieval on the knowledge-graph data system through the retrieval system includes:
the method comprises the steps of constructing a search engine cluster comprising a plurality of search engines, establishing an individual index between the preset database and each search engine, and establishing and storing a composite index according to the individual indexes.
And setting a preset query language and an application program interface in the preset database so that a user can carry out data retrieval according to the preset query language and the application program corresponding to the application program interface.
Specifically, the two ways are to construct an index retrieval based on a search engine and set a query based on a preset query language. Specifically, the index retrieval based on the search engine may be to establish an ElasticSearch engine cluster, and establish an index relationship between the graph data in the preset database and the ElasticSearch. And then analyzing the attribute or class needing to be indexed in the graph data, enabling the index and rebuilding the index after creating the composite index of the graph through an index module of the JanusGraph, and automatically mapping the index into a search engine Elasticsearch by a program according to configuration to realize the efficient retrieval of the Elasticsearch data. The query based on the preset query language may specifically be a graph query based on the Gremlin query language: gremlin is the query language of JanusGraph, and is used for retrieving and modifying graph data, and graph queries are realized in a mode of embedding and calling API. The user may implement the query by submitting the Gremlin language to the JanusGraph Server.
In the fifth embodiment and the sixth embodiment, by constructing a composite index and combining a search engine cluster, efficient retrieval of data by a user can be realized; through the Gremlin graph query language, retrieval and modification of graph data by a user can be achieved.
Further, a seventh embodiment is proposed based on the first embodiment, and in this embodiment, after the step S130, the method includes:
acquiring invalid data and repeated data in the preset database;
deleting the invalid data and the duplicate data.
In this embodiment, the invalid data and the repeated data are deleted, that is, the preset database is cleaned. By cleaning the preset database, a user can be ensured to obtain more accurate and effective information when the user utilizes a knowledge graph data system to inquire data.
The invention also provides a system for constructing the knowledge graph data system, which comprises the following components:
the knowledge modeling module is used for acquiring sample tobacco business data, analyzing the sample tobacco business data and constructing a data model;
the knowledge acquisition module is used for constructing a mapping file capable of mapping relational data and resource description type frame data according to the data model;
the knowledge acquisition module is also used for acquiring relational data in a preset tobacco business database according to the mapping file and converting the relational data into resource description framework data;
and the knowledge storage module is used for storing the resource description framework type data into a preset database to construct a knowledge map data system.
The present invention also provides a readable storage medium having stored thereon a computer program which, when being executed by a processing unit, carries out the steps of the method of constructing a knowledge-graph data system as set out above.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method for constructing a knowledge-graph data system is characterized by comprising the following steps:
obtaining sample tobacco commercial data, analyzing the sample tobacco commercial data and constructing a data model;
constructing a mapping file capable of mapping relational data and resource description type framework data according to the data model;
obtaining relational data in a preset tobacco business database according to the mapping file, and converting the relational data into resource description framework data;
and storing the resource description framework type data into a preset database to construct a knowledge graph data system.
2. The method of constructing a knowledge-graph data system of claim 1, wherein the steps of obtaining sample tobacco business data, analyzing the sample tobacco business data, and constructing a data model comprise:
acquiring sample tobacco business data, analyzing the structure and the characteristics of the sample tobacco business data through a preset identification model, and identifying concept class data and attribute class data of the sample tobacco business data and relational data between the concept class data and the attribute class data;
and constructing a data model according to the concept class data, the attribute class data and the relational data.
3. The method of constructing a knowledge-graph data system of claim 1, wherein the step of storing the resource description framework type data in a predetermined database to construct a knowledge-graph data system comprises:
determining the type of the resource description framework type data;
if the resource description type frame data are batch data, storing the resource description type frame data into a preset database through a preset distributed computing engine;
if the resource description type frame data is instant data, storing the resource description type frame data into the preset database in batch through a preset online transaction processing system;
and constructing a knowledge graph data system according to the preset database.
4. The method of constructing a knowledge-graph data system of claim 3, wherein the step of storing the resource description framework type data in a predetermined database, after the step of constructing a knowledge-graph data system, comprises:
and constructing a retrieval system so that a user can perform data retrieval on the knowledge graph data system through the retrieval system.
5. The method of constructing a knowledge-graph data system of claim 4, wherein the step of constructing a retrieval system for enabling a user to perform data retrieval at the knowledge-graph data system through the retrieval system comprises:
the method comprises the steps of constructing a search engine cluster comprising a plurality of search engines, establishing an individual index between the preset database and each search engine, and establishing and storing a composite index according to the individual indexes.
6. The method of constructing a knowledge-graph data system of claim 5, wherein the step of constructing a retrieval system for enabling a user to perform a data retrieval at the knowledge-graph data system through the retrieval system further comprises:
and setting a preset query language and an application program interface in the preset database so that a user can carry out data retrieval according to the preset query language and the application program corresponding to the application program interface.
7. The method of constructing a knowledge-graph data system of claim 1, wherein the step of storing the resource description framework type data in a predetermined database, after the step of constructing a knowledge-graph data system, comprises:
acquiring invalid data and repeated data in the preset database;
deleting the invalid data and the duplicate data.
8. A knowledge-graph data system construction system, the data system construction system comprising:
the knowledge modeling module is used for acquiring sample tobacco business data, analyzing the sample tobacco business data and constructing a data model;
the knowledge acquisition module is used for constructing a mapping file capable of mapping relational data and resource description type frame data according to the data model;
the knowledge acquisition module is also used for acquiring relational data in a preset tobacco business database according to the mapping file and converting the relational data into resource description framework data;
and the knowledge storage module is used for storing the resource description framework type data into a preset database to construct a knowledge map data system.
9. A terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the computer program implementing the steps of the method of constructing a knowledge-graph data system according to any one of claims 1 to 8.
10. A readable storage medium, having stored thereon a computer program which, when executed by a processor, carries out the steps of the method of constructing a knowledge-graph data system according to any one of claims 1 to 8.
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