CN111930778B - Knowledge query method and device - Google Patents
Knowledge query method and device Download PDFInfo
- Publication number
- CN111930778B CN111930778B CN202010807304.4A CN202010807304A CN111930778B CN 111930778 B CN111930778 B CN 111930778B CN 202010807304 A CN202010807304 A CN 202010807304A CN 111930778 B CN111930778 B CN 111930778B
- Authority
- CN
- China
- Prior art keywords
- query
- information
- knowledge
- statement
- knowledge base
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000006243 chemical reaction Methods 0.000 claims description 24
- 238000013507 mapping Methods 0.000 claims description 21
- 238000004590 computer program Methods 0.000 claims description 16
- 238000010586 diagram Methods 0.000 description 14
- 230000006870 function Effects 0.000 description 5
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
Classifications
-
- 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
Landscapes
- 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 inquiry information; the query information is determined according to the knowledge graph; generating a knowledge graph according to the relational knowledge base; according to the relational knowledge base, utilizing a first-order query logic language to convert query information into a structured query statement; 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 agents, improving the query efficiency and improving the query experience of users.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a knowledge query method and apparatus.
Background
In the field of intelligent customer service, a relational database based on full text retrieval is generally used by agents, and when the database is queried, query sentences are written manually according to the query requirements of users to query. The existing query method depends on manpower, has certain requirements on the professional level of the manpower, has low query efficiency and affects the query experience of users 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 agents, improve the query efficiency and improve the query experience of users.
In a first aspect, an embodiment of the present invention provides a knowledge query method, where the method includes: acquiring inquiry information; the query information is determined according to the knowledge graph; the knowledge graph is generated according to a relational knowledge base; according to the relational knowledge base, the query information is converted into a structured query statement by using a first-order query logic language; querying the relational knowledge base by using the structured query statement to obtain a knowledge query result; before obtaining the query information, the method further comprises: generating a mapping file through D2 RQ; modifying the entity relation model in the mapping file; and completing the conversion from the relational knowledge base to the knowledge graph according to the mapping file.
In a second aspect, an embodiment of the present invention further provides a knowledge query device, where the device includes: the acquisition module is used for acquiring the query information; the query information is determined according to the 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; the query module is used for querying the relational knowledge base by utilizing the structured query statement to obtain a knowledge query result; the device further comprises a mapping module, which is specifically used for: generating a mapping file through D2 RQ; modifying the entity relation model in the mapping file; and completing the conversion from the relational knowledge base to the knowledge graph according to the mapping file.
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 in the memory and capable of running on the processor, where the processor implements the knowledge query method described above when executing the computer program.
In a fourth aspect, embodiments of the present invention further provide a computer readable storage medium storing a computer program that when executed by a processor implements the knowledge query method described above.
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 sentence by using a first-order query logic language according to the relational database, and finally querying the relational database by using the structured query sentence 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 agents, improving the query efficiency and improving the query experience of users.
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 above objects, features and advantages of the present invention more 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 that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a knowledge query method provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of steps performed in a knowledge query method according to an embodiment of the present invention;
FIG. 3 is a block diagram of a knowledge query device 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
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, 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 embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the field of intelligent customer service, a relational database based on full text retrieval is generally used by agents, and the knowledge base in the form is insufficient in query intelligence, so that the most accurate answer cannot be presumed according to the intention of a user, and the service experience of the user is affected to a certain extent.
Based on the knowledge query method and the knowledge query device provided by the embodiment of the invention, the relational database can be mapped into the knowledge graph, the knowledge graph is converted and rewritten based on the query of the ontology layer, the details of the type of the database at the bottom layer are hidden for the seat, and the multi-source query function of knowledge is realized.
For the sake of understanding the present embodiment, first, a detailed description is given of a knowledge query method disclosed in the present embodiment.
The embodiment of the invention provides a knowledge query method, referring to a flow chart of the knowledge query method shown in fig. 1, comprising the following steps:
step S102, query information is acquired.
In the embodiment of the invention, the query information is information that the user needs to query the seat. The query information is determined by the user according to a knowledge graph, and the knowledge graph is generated according to a relational knowledge base.
Step S104, according to the relational knowledge base, the query information is converted into a structured query statement by using a first-order query logic language.
In an embodiment of the present invention, the first-order query logic language may be a Datalog (data storage) language. And (3) combining the relational knowledge base, and converting the query information by using a first-order query logic language to obtain SQL (Structured Query Language ) sentences.
And S106, inquiring the relational knowledge base by utilizing the structured inquiry statement to obtain a knowledge inquiry 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 aiming at a knowledge graph layer, the dependence on manual labor and the professional requirement on manual labor are reduced, and the query efficiency and the 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 sentence by using a first-order query logic language according to the relational database, and finally querying the relational database by using the structured query sentence 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 agents, improving the query efficiency and improving the query experience of users.
Considering that in order to improve the accuracy of query information conversion, according to the relational knowledge base, the query information is converted into a structured query statement by using a first-order query logic language, and the method can be implemented as follows:
converting the query information into an ontology query statement; converting the ontology query statement into a first-order query logic statement; describing a 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 an embodiment of the invention, the original questions entered by the user in the input library are converted by the background system into sentences represented by the ontology query language (Protocol and RDF Query Language, SPARQL) according to the query logic. For example, the query information "query all gold credit cards and annual fee information" may be converted into an ontology query statement "SELECT? creditcardrate WHERE {? Crodivcard rdf: type exp:jinka }).
The ontology query statement is rewritten into a structured query statement through the following steps: 1. rewriting into a first-order query logic language (e.g., datalog); 2. mapping the database relationship into a Datalog substructure; step 3: and finally generating a query result based on the knowledge graph by querying the Datalog rewritten from the SPARQL and the database.
The following describes, in one embodiment, the steps of rewriting an ontology query statement into a structured query statement.
For the relational knowledge base, it is assumed that there are two tables, including a financial services table (Finan) of table 1, and a service type table (YType) of table 2:
| ID | Name | Explain | Type |
| 1 | living time of life | Access at any time | 1 |
| 2 | Two-purpose toilet | Personal deposit without appointment for deposit, one-time deposit and one-time taking | 1 |
| 3 | Periodically | Contract lifetime | 1 |
| 4 | Informing public units of deposit | Early notification of withdrawal without committing to the lifetime | 2 |
TABLE 1
| ID | YName | Explain |
| 1 | For private business | Business transacted for individuals |
| 2 | For public business | Business transacted to enterprise |
TABLE 2
Taking the above two tables as examples:
for the ontology query statement "SELECTNameExplain WHERE {? Type rdf Type exp YNAM } "Type exp"
Step 1, rewrite into a first-order query logic statement (e.g., 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) indicates that the business category can be queried from the financial business table of Table 1. The same YType (x) indicates that e.g. live traffic can be queried from the table 2 traffic type table as either for public traffic or for private traffic. Finan (x, _) indicates that a live service specific attribute, such as "access at any time", can be queried. BelongTo (x, y) is a relational query for querying the contents of both tables 1 and 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 knowledge base conversion result.
Finan(x)←Finan(x,_,_,_)
YType(x)←YType(x,_)
BelongTo(x,y)←Finan(x,_,_,y)
Step 3, integrating and inquiring the Datalog rules rewritten from the SPARQL and the database:
SELECTNameExplain WHERE {? Type rdf: type exp: frame }, into:
BelongTo (x, y), YType (x), converts to:
Finan(x,_,_,y),YType(x,_)。
in the above formula, finan (x, _, _, y) and YTYPE (x, _) are 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 the knowledge graph layer, the data format of the bottom layer is transparent, namely, the bottom layer data can be represented by a relational database or other data sources, and the data can be conveniently converted according to datalog.
In order to improve the application range and the use experience of a user, the query information is voice information; the conversion of the query information into an ontology query statement may be performed as follows:
converting the voice information into text information; and converting the text information into an ontology query sentence.
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 using the ontology query statement.
In order to improve the information processing efficiency, the following steps may be further executed before the query information is acquired:
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 map, the mapping can generate a mapping (high-precision map) file by D2RQ (Database to RDF), the entity relation model in the mapping file is modified, and finally the conversion from the relational knowledge base to the knowledge map is completed according to the mapping file.
In the embodiment of the invention, the knowledge graph query is based on an ontology layer, the query information used is ontology query voice, the ontology query is converted into SQL query, and the query of the bottom data is completed, so that the knowledge query result is obtained. In the relational knowledge base, data is stored in a table model, and in the knowledge map, data is stored in a (ontology, relationship, ontology) model.
The embodiment of the invention provides a knowledge query method and a knowledge query device, and referring to a knowledge query method implementation step schematic diagram shown in fig. 2, the method can map a relational knowledge base into a knowledge graph of a specific ontology model according to requirements for storage, and simultaneously, when in query, the method can automatically map an ontology query language into a first-order logic language and structure query, and finally obtain an ontology query result.
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 that of the knowledge query method, the implementation of the device can refer to the implementation of the knowledge query method, and the repetition is omitted. Referring to fig. 3, a block diagram of a knowledge query device is shown, the device comprising:
an acquisition module 71 for acquiring query information; the query information is determined according to the knowledge graph; generating a knowledge graph according to the relational knowledge base; a conversion module 72, configured to convert the query information into a structured query statement using a first-order query logic language according to the relational knowledge base; a query module 73, configured to query the relational knowledge base by using the structured query term 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 a 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 voice information, and the conversion module is specifically configured to: converting the voice information into text information; and converting the text information into an ontology query sentence.
In one embodiment, referring to another block diagram of knowledge query apparatus shown in FIG. 4, the apparatus further includes a mapping module 74 for converting a 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, where the computer device includes a memory 81, a processor 82, and a computer program stored on the memory and capable of running on the processor, and when the processor executes the computer program, the processor implements the steps of any one of the knowledge query methods described above.
It will 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, which is not repeated herein.
The embodiment of the invention also provides a computer readable storage medium, and the computer readable storage medium stores a computer program for executing any one of the above query implementation methods.
It will be appreciated by those skilled in the art that 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A knowledge query method, comprising:
acquiring inquiry information; the query information is determined according to the knowledge graph; the knowledge graph is generated according to a relational knowledge base;
according to the relational knowledge base, the query information is converted into a structured query statement by using a first-order query logic language;
querying the relational knowledge base by using the structured query statement to obtain a knowledge query result;
before obtaining the query information, the method further comprises:
generating a mapping file through D2 RQ;
modifying the entity relation model in the mapping file;
and completing the conversion from the relational knowledge base to the knowledge graph according to the mapping file.
2. The method of claim 1, wherein converting the query information into a structured query statement using a first-order query logic language based on the relational knowledge base, 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 the 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, including:
converting the voice information into text information;
and converting the text information into an ontology query sentence.
4. A knowledge query device, comprising:
the acquisition module is used for acquiring the query information; the query information is determined according to the 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;
the query module is used for querying the relational knowledge base by utilizing the structured query statement to obtain a knowledge query result;
the device further comprises a mapping module, which is specifically used for:
generating a mapping file through D2 RQ;
modifying the entity relation model in the mapping file;
and completing the conversion from the relational knowledge base to the knowledge graph according to the mapping file.
5. The apparatus of claim 4, 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 the structured query statement corresponding to the query information according to the knowledge base conversion result and the first-order query logic statement.
6. The apparatus of claim 5, wherein the query information is voice information, and the conversion module is specifically configured to:
converting the voice information into text information;
and converting the text information into an ontology query sentence.
7. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 3 when executing the computer program.
8. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the method of any of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010807304.4A CN111930778B (en) | 2020-08-12 | 2020-08-12 | Knowledge query method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010807304.4A CN111930778B (en) | 2020-08-12 | 2020-08-12 | Knowledge query method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111930778A CN111930778A (en) | 2020-11-13 |
| CN111930778B true CN111930778B (en) | 2024-02-23 |
Family
ID=73311565
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010807304.4A Active CN111930778B (en) | 2020-08-12 | 2020-08-12 | Knowledge query method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111930778B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113486139A (en) * | 2021-07-27 | 2021-10-08 | 中国银行股份有限公司 | User report query device and method |
Citations (3)
| 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 |
-
2020
- 2020-08-12 CN CN202010807304.4A patent/CN111930778B/en active Active
Patent Citations (3)
| 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 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111930778A (en) | 2020-11-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110543517B (en) | Method, device and medium for realizing complex query of mass data based on elastic search | |
| US8812411B2 (en) | Domains for knowledge-based data quality solution | |
| US8645911B2 (en) | Systems, methods and machine readable mediums for defining and executing new commands in a spreadsheet software application | |
| US7702622B2 (en) | Advanced techniques for SQL generation of performancepoint business rules | |
| US20060235837A1 (en) | Rewriting table functions as SQL strings | |
| Yang et al. | Lenses: An on-demand approach to etl | |
| KR20190076047A (en) | System and method for determining relationships between data elements | |
| US9292094B2 (en) | Gesture inferred vocabulary bindings | |
| Ibragimov et al. | Optimizing aggregate SPARQL queries using materialized RDF views | |
| Kautish et al. | Design of new architecture for model management systems using knowledge sharing concept | |
| WO2020259325A1 (en) | Feature processing method applicable to machine learning, and device | |
| CN117009422B (en) | Method for realizing data import by convenience business personnel | |
| CN111930778B (en) | Knowledge query method and device | |
| CN110287193A (en) | A kind of document generating method, device, equipment and computer readable storage medium | |
| CN114385820A (en) | Enterprise-level audit knowledge map construction method | |
| Marotta et al. | Data warehouse design: A schema-transformation approach | |
| Soumiya et al. | Converting UML class diagrams into temporal object relational database | |
| CN110032574B (en) | SQL statement processing method and device | |
| CN112634004A (en) | Blood margin map analysis method and system for credit investigation data | |
| CN116578723A (en) | Information query method and device, processor and electronic equipment | |
| CN115510214A (en) | A stock index knowledge graph construction processing system, method, device, processor and storage medium for intelligent question answering in financial field | |
| Orsi | Context based querying of dynamic and heterogeneous information sources. | |
| Halpin | Join constraints | |
| Pointner et al. | Enhancing Interoperability of HL7 Resources Using Namespaces in Graph Databases | |
| CN112163049B (en) | Method and device for mapping business object into data entity |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |