CN111930778A - Knowledge query method and device - Google Patents

Knowledge query method and device Download PDF

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CN111930778A
CN111930778A CN202010807304.4A CN202010807304A CN111930778A CN 111930778 A CN111930778 A CN 111930778A CN 202010807304 A CN202010807304 A CN 202010807304A CN 111930778 A CN111930778 A CN 111930778A
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query
knowledge
information
statement
converting
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CN111930778B (en
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申亚坤
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Bank of China Ltd
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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
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/242—Query formulation
    • G06F16/2433—Query languages
    • 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
    • G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
    • G06F16/367—Ontology
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Animal Behavior & Ethology (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The invention provides a knowledge query method and a knowledge query device, which relate to the technical field of data processing, and the method comprises the following steps: acquiring query information; determining query information according to the knowledge graph; generating a knowledge graph according to a relational knowledge base; converting the query information into a structured query statement by using a first-order query logic language according to the relational knowledge base; and querying the relational knowledge base by using the structured query statement to obtain a knowledge query result. The invention can automatically convert the query information into the structured query statement, thereby completing the multi-source query of knowledge, reducing the query times of the agent, improving the query efficiency and improving the query experience of the user.

Description

Knowledge query method and device
Technical Field
The invention relates to the technical field of data processing, in particular to a knowledge query method and a knowledge query device.
Background
In the field of intelligent customer service, a relational database based on full-text retrieval is usually used by an agent, and when the database is queried, query sentences need to be manually written for query according to the query requirements of a user. The existing query method depends on manual work, has certain requirements on the professional level of the manual work, has low query efficiency, and influences the query experience of a user to a certain extent.
Disclosure of Invention
The invention provides a knowledge query method and a knowledge query device, which can reduce the query times of an agent, improve the query efficiency and improve the query experience of a user.
In a first aspect, an embodiment of the present invention provides a knowledge query method, where the method includes: acquiring query information; the query information is determined according to a knowledge graph; the knowledge graph is generated according to a relational knowledge base; converting the query information into a structured query statement by using a first-order query logic language according to the relational knowledge base; and querying the relational knowledge base by using the structured query statement to obtain a knowledge query result.
In a second aspect, an embodiment of the present invention further provides a knowledge query apparatus, where the apparatus includes: the acquisition module is used for acquiring query information; the query information is determined according to a knowledge graph; the knowledge graph is generated according to a relational knowledge base; the conversion module is used for converting the query information into a structured query statement by utilizing a first-order query logic language according to the relational knowledge base; and the query module is used for querying the relational knowledge base by using the structured query statement to obtain a knowledge query result.
In a third aspect, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the above knowledge query method when executing the computer program.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where a computer program for executing the above knowledge query method is stored in the computer-readable storage medium.
The embodiment of the invention has the following beneficial effects: the embodiment of the invention provides a knowledge query scheme, which comprises the steps of firstly obtaining query information sent by a user, determining the query information according to a knowledge graph, generating the knowledge graph according to a relational database, then converting the query information into a structured query statement by utilizing a first-order query logic language according to the relational database, and finally querying the relational database by utilizing the structured query statement to obtain a knowledge query result. The embodiment of the invention can automatically convert the query information into the structured query statement, thereby completing the multi-source query of knowledge, reducing the query times of the agent, improving the query efficiency and improving the query experience of the user.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
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, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a knowledge query method provided by an embodiment of the invention;
FIG. 2 is a schematic diagram illustrating steps of a knowledge query method according to an embodiment of the present invention;
FIG. 3 is a block diagram of a knowledge query apparatus according to an embodiment of the present invention;
FIG. 4 is a block diagram of another knowledge query device according to an embodiment of the present invention;
fig. 5 is a block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. 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.
In the field of intelligent customer service, a relational database based on full-text retrieval is usually used by an agent, the query intelligence of the knowledge base in the form is insufficient, the most accurate answer cannot be inferred according to the intention of a user, and the service experience of the user is influenced to a certain extent.
Based on the knowledge query method and the knowledge query device, the relational database can be mapped into the knowledge map, query based on the ontology layer is converted and rewritten on the knowledge map, details of the type of the bottom database are hidden for the seat, and the multi-source knowledge query function is achieved.
For the understanding of the embodiment, a detailed description will be given to a knowledge query method disclosed in the embodiment of the present invention.
The embodiment of the invention provides a knowledge query method, which is shown in a flow chart of the knowledge query method shown in figure 1 and comprises the following steps:
step S102, obtaining query information.
In the embodiment of the invention, the query information is the information which needs to be queried by the user. The query information is determined by the user based on the knowledge-graph, which is generated based on the relational knowledge-base.
And step S104, converting the query information into a structured query statement by using a first-order query logic language according to the relational knowledge base.
In embodiments of the present invention, the first order query logic language may be a Datalog (data store) language. And converting the Query information by using a first-order Query logic Language in combination with the relational knowledge base to obtain an SQL (Structured Query Language) statement.
And step S106, querying the relational knowledge base by using the structured query statement to obtain a knowledge query result.
In the embodiment of the invention, the structured query statement is obtained by conversion according to the query information without manual writing, so that a user can only operate on a knowledge map layer, dependence on manual work and professional requirements on manual work are reduced, and query efficiency and user experience are improved.
The embodiment of the invention provides a knowledge query scheme, which comprises the steps of firstly obtaining query information sent by a user, determining the query information according to a knowledge graph, generating the knowledge graph according to a relational database, then converting the query information into a structured query statement by utilizing a first-order query logic language according to the relational database, and finally querying the relational database by utilizing the structured query statement to obtain a knowledge query result. The embodiment of the invention can automatically convert the query information into the structured query statement, thereby completing the multi-source query of knowledge, reducing the query times of the agent, improving the query efficiency and improving the query experience of the user.
Considering that in order to improve the accuracy of query information conversion, the first-order query logic language is used to convert the query information into the structured query statement according to the relational knowledge base, and the method can be performed according to the following steps:
converting the query information into an ontology query statement; converting the ontology query statement into a first-order query logic statement; describing the relational knowledge base by using a first-order query logic language to obtain a knowledge base conversion result; and determining a structured query statement corresponding to the query information according to the knowledge base conversion result and the first-order query logic statement.
In the embodiment of the invention, the original question input by the user in the library is input, and the background system converts the Query information into a statement represented by ontology Query Language (SPARQL) according to the Query logic. For example, the query message "query all gold credit cards and annual fee information" may be converted into an ontology query statement "SELECT? creatitcard? rate WHERE {? creaticard rdf: type exp: jinka } ".
The ontology query statement is rewritten into a structured query statement through the following steps: 1. rewritten to a first order query logic language (e.g., Datalog); 2. mapping the database relation into a Datalog substructure; and step 3: and jointly querying Datalog rewritten from the SPARQL and the database to finally generate a query result based on the knowledge graph.
The following describes a step of rewriting an ontology query statement into a structured query statement in a specific embodiment.
For the relational repository, assume that there are two tables, including the Table 1 financial services table (Finan), and the Table 2 Business type table (YType):
ID Name Explain Type
1 period of life Access at any time 1
2 Stool for both standing and moving Personal deposit with one-time deposit and one-time withdrawal without appointment of deposit period 1
3 On a regular basis Contract lifetime 1
4 Informing public units of deposit Advance notice when not appointing the period of existence and getting 2
TABLE 1
ID YName Explain
1 Private to business Personal transacting business
2 Public service Business to enterprise
TABLE 2
Take the above two tables as examples:
for an ontology query statement "SELECT? Name? Explain WHERE {? Type rdf Type exp YNAme } -) "
Step 1, rewriting into a first-order query logic statement (such as Datalog).
q(x)←Finan(x)
q(x)←YType(x)
q(x)←Finan(x,_)
q(x)←BelongTo(x,y),YType(y)
For the 4 rules above, finan (x), it is indicated that the business category can be looked up from the financial business table of table 1. The same type (x) indicates that the current service can be queried from the service type table in table 2, such as the current service is a public service or a private service. Finan (x, _) indicates that the specific attributes of the current service, such as "access anytime" can be queried. BelongTo (x, y) is a relational query, used to query the contents of both Table 1 and Table 2.
And 2, mapping the database relation into a Datalog substructure, and converting the table structure of the database in the process to obtain a conversion result of the knowledge base.
Finan(x)←Finan(x,_,_,_)
YType(x)←YType(x,_)
BelongTo(x,y)←Finan(x,_,_,y)
And 3, integrating and querying Datalog rules rewritten from the SPARQL and the database:
SELECT? Name? Explain WHERE {? Type rdf, Type exp, YNAme, converted to:
BelongTo (x, y), ytype (x), converts to:
Finan(x,_,_,y),YType(x,_)。
in the above formula, Finan (x, _, y) and YType (x, _) are the structured query statements generated for the underlying database with x and y as variables.
The rewriting method has the advantages that a user can only operate on the knowledge graph layer and the data format of the bottom layer is transparent, namely, the bottom layer data can be represented by a relational database and can also be conveniently converted by adopting other forms of data sources according to the datalog.
In order to improve the application range and the use experience of a user, the query information is voice information; converting the query information into an ontology query statement, which can be performed according to the following steps:
converting the voice information into character information; and converting the text information into an ontology query statement.
In the embodiment of the invention, the voice information is processed to obtain the corresponding text information, and then the text information is expressed by the ontology query sentence.
In order to improve the information processing efficiency, before the query information is acquired, the following steps may be further performed:
and converting the relational knowledge base into a knowledge graph.
In the embodiment of the invention, the knowledge base stored in the existing relational Database is converted into the knowledge graph, the mapping can generate a mapping (high-precision map) file of the knowledge base through D2RQ (Database to RDF), an entity relational model in the mapping file is modified, and finally the conversion from the relational Database to the knowledge graph is completed according to the mapping file.
In the embodiment of the invention, the knowledge graph query is based on the ontology layer, the used query information is ontology query voice, the ontology query is converted into SQL query, the query of the bottom data is completed, and the knowledge query result is obtained. In the relational knowledge base, data is stored by a table model, and in the knowledge map, data is stored by an (ontology, relationship, ontology) model.
The embodiment of the invention provides a knowledge query method and a knowledge query device, and refers to a schematic diagram of implementation steps of the knowledge query method shown in FIG. 2.
The embodiment of the invention also provides a knowledge inquiry device, which is described in the following embodiment. Because the principle of the device for solving the problems is similar to the knowledge query method, the implementation of the device can refer to the implementation of the knowledge query method, and repeated details are not repeated. Referring to fig. 3, a block diagram of a knowledge query apparatus is shown, the apparatus comprising:
an obtaining module 71, configured to obtain query information; determining query information according to the knowledge graph; generating a knowledge graph according to a relational knowledge base; a conversion module 72, configured to convert the query information into a structured query statement according to the relational repository by using a first-order query logic language; and the query module 73 is configured to query the relational knowledge base by using the structured query statement to obtain a knowledge query result.
In one embodiment, the conversion module is specifically configured to: converting the query information into an ontology query statement; converting the ontology query statement into a first-order query logic statement; describing the relational knowledge base by using a first-order query logic language to obtain a knowledge base conversion result; and determining a structured query statement corresponding to the query information according to the knowledge base conversion result and the first-order query logic statement.
In one embodiment, the query information is speech information, and the conversion module is specifically configured to: converting the voice information into character information; and converting the text information into an ontology query statement.
In one embodiment, referring to the block diagram of another knowledge query apparatus shown in fig. 4, the apparatus further comprises a mapping module 74 for converting the relational knowledge base into a knowledge graph.
The embodiment of the present invention further provides a computer device, referring to the schematic block diagram of the structure of the computer device shown in fig. 5, the computer device includes a memory 81, a processor 82, and a computer program stored in the memory and capable of running on the processor, and the processor implements the steps of any one of the above-mentioned knowledge query methods when executing the computer program.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the computer device described above may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program for executing any one of the above query implementation methods is stored in the computer-readable storage medium.
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.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. A knowledge query method, comprising:
acquiring query information; the query information is determined according to a knowledge graph; the knowledge graph is generated according to a relational knowledge base;
converting the query information into a structured query statement by using a first-order query logic language according to the relational knowledge base;
and querying the relational knowledge base by using the structured query statement to obtain a knowledge query result.
2. The method of claim 1, wherein transforming the query information into a structured query statement using a first order query logic language according to the relational repository comprises:
converting the query information into an ontology query statement;
converting the ontology query statement into a first-order query logic statement;
describing the relational knowledge base by using the first-order query logic language to obtain a knowledge base conversion result;
and determining a structured query statement corresponding to the query information according to the knowledge base conversion result and the first-order query logic statement.
3. The method of claim 2, wherein the query information is voice information;
converting the query information into an ontology query statement, comprising:
converting the voice information into character information;
and converting the text information into an ontology query statement.
4. The method of claim 1, wherein prior to obtaining query information, further comprising:
and converting the relational knowledge base into a knowledge graph.
5. A knowledge inquiry apparatus, comprising:
the acquisition module is used for acquiring query information; the query information is determined according to a knowledge graph; the knowledge graph is generated according to a relational knowledge base;
the conversion module is used for converting the query information into a structured query statement by utilizing a first-order query logic language according to the relational knowledge base;
and the query module is used for querying the relational knowledge base by using the structured query statement to obtain a knowledge query result.
6. The apparatus of claim 5, wherein the conversion module is specifically configured to:
converting the query information into an ontology query statement;
converting the ontology query statement into a first-order query logic statement;
describing the relational knowledge base by using the first-order query logic language to obtain a knowledge base conversion result;
and determining a structured query statement corresponding to the query information according to the knowledge base conversion result and the first-order query logic statement.
7. The apparatus of claim 6, wherein the query message is a voice message, and the conversion module is specifically configured to:
converting the voice information into character information;
and converting the text information into an ontology query statement.
8. The apparatus of claim 5, further comprising a mapping module to:
and converting the relational knowledge base into a knowledge graph.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 4.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109885665A (en) * 2019-01-09 2019-06-14 北京小乘网络科技有限公司 A kind of data query method, apparatus and system
CN110119404A (en) * 2019-04-12 2019-08-13 杭州量之智能科技有限公司 A kind of intelligence access system and method based on natural language understanding
CN110275959A (en) * 2019-05-22 2019-09-24 广东工业大学 A kind of Fast Learning method towards large-scale knowledge base

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109885665A (en) * 2019-01-09 2019-06-14 北京小乘网络科技有限公司 A kind of data query method, apparatus and system
CN110119404A (en) * 2019-04-12 2019-08-13 杭州量之智能科技有限公司 A kind of intelligence access system and method based on natural language understanding
CN110275959A (en) * 2019-05-22 2019-09-24 广东工业大学 A kind of Fast Learning method towards large-scale knowledge base

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