CN108829858A - Data query method, apparatus and computer readable storage medium - Google Patents
Data query method, apparatus and computer readable storage medium Download PDFInfo
- Publication number
- CN108829858A CN108829858A CN201810647001.3A CN201810647001A CN108829858A CN 108829858 A CN108829858 A CN 108829858A CN 201810647001 A CN201810647001 A CN 201810647001A CN 108829858 A CN108829858 A CN 108829858A
- Authority
- CN
- China
- Prior art keywords
- data
- entity
- query
- knowledge mapping
- user
- 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.)
- Granted
Links
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
Present disclose provides a kind of data query method, apparatus and computer readable storage mediums, are related to technical field of data processing.Data query method therein includes：Receive the query information of user's input；Natural language processing is carried out to the query information of user's input, parsing obtains searching keyword；The corresponding query entity of searching keyword is determined in knowledge mapping；Data-link associated with query entity in knowledge mapping is returned to user.The disclosure returns to the related data chain in knowledge mapping in user input query information rear line, the upstream and downstream incidence relation of related data information can be sufficiently shown to user, to extend the range of customer analysis data, facilitates user and carry out more comprehensive data analysis.
Description
Technical field
This disclosure relates to technical field of data processing, in particular to a kind of data query method, apparatus and computer-readable
Storage medium.
Background technique
In today of internet rapid development, information is largely present in non-structured text data, a large amount of semi-structured
Table and webpage and the structural data of production system in.Financial industry is compared to other industries to various media events
Susceptibility it is much higher, therefore be highly dependent on the accuracy of media event, comprehensive and timeliness.Much, more full data
If can be timely aggregated into financial industry user hand with information, and can then will from the relationship for wherein analyzing countless ties
The working efficiency of user is greatly promoted, auxiliary user, which makes, most accurately to be judged, maximum return value is obtained.
Financial industry user generally requires experience following steps before making final investment or decision.First
It is manual data acquisition and processing, data (such as search engine, major news portal net is acquired from each different news sources
Stand, government notice, forum etc.), thus to obtain a large amount of news information, it is wherein right to be filtered out according to the personal experience of financial user
The invalid data of user.Followed by analysis and pooled classification data, user needs to extract each message event after acquisition of information
Key point and pooled classification, formed document, report or storage into the data system of structuring (such as Excel or
The databases such as mysql).Since data volume is very huge, in order to search for convenient and improve search efficiency, so the knot of data storage
Fruit can lack the information that some data provide originally.It is finally decision, user will do it data analysis after classification data, therefrom sentence
The range of influence and influence of the generation of disconnected media event to investment subject, then makes according to data analysis result final
Decision.
Summary of the invention
The technical problem that the disclosure solves is how to realize more comprehensive data analysis.
According to the one aspect of the embodiment of the present disclosure, a kind of data query method is provided, including：Receive user's input
Query information；Natural language processing is carried out to the query information of user's input, parsing obtains searching keyword；In knowledge mapping
Determine the corresponding query entity of searching keyword；Data-link associated with query entity in knowledge mapping is returned to user.
In some embodiments, determine that the corresponding query entity of searching keyword includes in knowledge mapping：In knowledge graph
The corresponding starting query entity of searching keyword is determined in spectrum；Number associated with query entity in knowledge mapping is returned to user
Include according to chain：It returns in knowledge mapping to user to originate data-link of the query entity as start node.
In some embodiments, determine that the corresponding query entity of searching keyword includes in knowledge mapping：In knowledge graph
The corresponding starting query entity of searching keyword is determined in spectrum and terminates query entity；To user return knowledge mapping in look into
Asking the associated data-link of entity includes：To user return knowledge mapping in using originate query entity as start node, with terminate
Query entity is the data-link of end node.
In some embodiments, the descending sequence of the relationship number for including according to data-link returns to knowledge graph to user
Data-link associated with query entity in spectrum.
In some embodiments, which further includes：Acquisition database data；Database data is carried out certainly
Right Language Processing extracts the relationship between entity and entity；Knowledge mapping is generated according to the relationship between entity and entity.
In some embodiments, natural language processing is carried out to database data, to extract between entity and entity
Relationship includes：Data critical word is extracted from database data using Natural Language Processing Models；By the reverse document frequency of word frequency-
Higher than first threshold data critical word as entity；Extracted from database data using Natural Language Processing Models entity it
Between relationship.
In some embodiments, acquisition database data include：Configuration data source address list starts the page number, sign-off sheet
Code and acquisition time；According to acquisition time, automatically extracts data source address list, starts determined by the page number, end code
News data；Parsing obtains the title and textual data in news data, stores to database.
According to the other side of the embodiment of the present disclosure, a kind of data query device is provided, including：Information receives mould
Block is configured as receiving the query information of user's input；Keyword resolution module is configured as the query information inputted to user
Natural language processing is carried out, parsing obtains searching keyword；Entity determining module is configured as determining inquiry in knowledge mapping
The corresponding query entity of keyword；Data return module is configured as returning to user related to query entity in knowledge mapping
The data-link of connection.
In some embodiments, entity determining module is configured as：Determine that searching keyword is corresponding in knowledge mapping
Originate query entity；Data return module is configured as：It returns in knowledge mapping to user to originate query entity as starting section
The data-link of point.
In some embodiments, entity determining module is configured as：Determine that searching keyword is corresponding in knowledge mapping
It originates query entity and terminates query entity；Data return module is configured as：It returns in knowledge mapping to user with starting
Query entity is start node, the data-link to terminate query entity as end node.
In some embodiments, data return module is configured as：The relationship number for including according to data-link is descending
Sequentially, data-link associated with query entity in knowledge mapping is returned to user.
In some embodiments, data query device further includes knowledge mapping generation module, is configured as：Acquisition database
Data；Natural language processing is carried out to database data, extracts the relationship between entity and entity；According to entity and entity
Between relationship generate knowledge mapping.
In some embodiments, knowledge mapping generation module is configured as：Using Natural Language Processing Models from database
Data critical word is extracted in data；The reverse document frequency of word frequency-is higher than the data critical word of first threshold as entity；It utilizes
Natural Language Processing Models from database data extract entity between relationship.
In some embodiments, knowledge mapping generation module is configured as：Configuration data source address list, start the page number,
End code and acquisition time；According to acquisition time, automatically extracts data source address list, starts the page number, end code institute
Determining news data；Parsing obtains the title and textual data in news data, stores to database.
According to the another aspect of the embodiment of the present disclosure, another data query device is provided, including：Memory；With
And it is coupled to the processor of memory, processor is configured as executing data above-mentioned based on instruction stored in memory
Querying method.
According to another aspect of the embodiment of the present disclosure, a kind of computer readable storage medium is provided, wherein computer
Readable storage medium storing program for executing is stored with computer instruction, and instruction realizes data query method above-mentioned when being executed by processor.
The disclosure returns to the related data chain in knowledge mapping in user input query information rear line, can be to user
Sufficiently show that the upstream and downstream incidence relation of related data information facilitates user to extend the range of customer analysis data
Carry out more comprehensive data analysis.
By the detailed description referring to the drawings to the exemplary embodiment of the disclosure, the other feature of the disclosure and its
Advantage will become apparent.
Detailed description of the invention
In order to illustrate more clearly of the embodiment of the present disclosure or technical solution in the prior art, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Disclosed some embodiments without any creative labor, may be used also for those of ordinary skill in the art
To obtain other drawings based on these drawings.
Fig. 1 shows the flow diagram for generating one embodiment of knowledge mapping process.
Fig. 2 shows the schematic diagrames of knowledge mapping.
Fig. 3 shows the flow diagram of one embodiment of disclosure data query method.
Fig. 4 shows the complete job flow chart of data query joint data acquisition.
Fig. 5 shows the flow diagram of the data query device of an embodiment of the present disclosure.
Fig. 6 shows the structural schematic diagram of the data query device of the disclosure another embodiment.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present disclosure, the technical solution in the embodiment of the present disclosure is carried out clear, complete
Site preparation description, it is clear that described embodiment is only disclosure a part of the embodiment, instead of all the embodiments.Below
Description only actually at least one exemplary embodiment be it is illustrative, never as to the disclosure and its application or making
Any restrictions.Based on the embodiment in the disclosure, those of ordinary skill in the art are not making creative work premise
Under all other embodiment obtained, belong to the disclosure protection range.
Inventor is the study found that in the related technology in order to allow final user to focus more on the analysis of event, usual data
Collection personnel and user are not the same person or the same team, in this way caused by result be exactly both sides to acquired information
Key point have different opinions, the missing of significant data or certain details certainly will be will lead to, to influence final number
According to analysis, the risk of customer investment is improved.Data analysis summary result is cured in document or database be in order to
User is facilitated to check, however document usually has fixed template, database has fixed field, such data processing
Cheng Wufa, come the cause and effect of dynamic analysis event, causes the imperfection of data according to the attribute of current information.In addition, with
The increment storage of data, historical data can accumulate gradually, cause data query process slow.
The data query method of the disclosure is discussed in detail step by step below.
(1) knowledge mapping is generated
Fig. 1 introduction is combined to generate one embodiment of knowledge mapping process first.
Fig. 1 shows the flow diagram for generating one embodiment of knowledge mapping process.As shown in Figure 1, the present embodiment
The middle process for generating knowledge mapping includes step S102~step S106.
In step s 102, acquisition database data.
It is completely dependent on information required for manually going in multiple news sources to acquire in the related technology, therefore needs daily a large amount of
Manpower complete complicated onerous toil, cause temporal waste, as soon as necessarily had certain whenever newly-increased data source
Manpower go to collect new data source information.
In order to reduce the degree of difficulty of acquisition data, the present embodiment is accomplished that the automatic collection of news data, to save
Manpower and time cost.Firstly, configuration data source address list, the beginning page number, end code and acquisition time.Then, root
According to acquisition time, automatically extracts data source address list, starts news data determined by the page number, end code.Finally, parsing
The title and textual data in news data are obtained, is stored to database, next natural language processing is used for.If terminal
The knowledge mapping for paying close attention to financial events generates, and can acquire news data relevant to financial, finance and economics.
In step S104, natural language processing is carried out to database data, extracts the relationship between entity and entity.
Specifically, can use Natural Language Processing Models extracts data critical word from database data.Such as news number
According to：" Monday (June 11), Zhang San has arrived at Singapore, i.e., historic meeting will be carried out with Korean Li Si.It is reported that Zhang Sanyu
The independent meeting of Li Si will be in the morning 9 12 day local time：15 hold.After talks, Zhang San will hold press conference, and in evening
8 points or so are left Singapore and return to the U.S..' deep V ' market, 1,003 high pointes of breakthrough are in a few days presented in stock gold.But because of ' independent meeting
View ' the rare progress in the eve U.S. is negotiated in advance, in addition the U.S.'s 10 term Yield of public debt uplink, causes hedging mood to start back
It falls, price of gold is hovered near 1300 dollars of critical points ".In the present embodiment, Natural Language Processing Models can be by the text in news data
The cutting of this progress semantically, by text dividing at words such as Zhang San, Singapore, Korea；Then by each root according to mark phase
The part of speech answered, such as：Zhang San's (name), Singapore's (place name)；Finally, the mark entity needed for obtaining, including name, place name, number
Word etc..
Optionally, Natural Language Processing Models can be using the reverse document frequency algorithm of word frequency-, by the reverse document of word frequency-
Frequency is higher than the data critical word of first threshold as entity.When the reverse document frequency algorithm of concrete application word frequency-, word frequency-is inverse
It is to the calculation formula of document frequency：
X=Y*lg { M/ (N+1) }
Wherein, X indicates that the reverse document frequency of the word frequency-of certain word, Y indicate the word frequency of certain word, i.e., the word is in this article
There is total degree, M indicates to store the total number of documents in the corpus of all news datas, and N indicates to include the word in corpus
Number of files.The thought of the reverse document frequency algorithm of word frequency-is, if the frequency that some word or phrase occur in an article
Height, and seldom occur in other articles, then it is assumed that this word or phrase have good discrimination.Take the reverse text of word frequency-
The shelves biggish data critical word of frequency values helps to generate the knowledge mapping for representing news key point as entity.
It can be further from the relationship extracted in database data between entity using Natural Language Processing Models.For example,
Database data records text information " influence of 618 pairs of stock markets ".So, text dividing is first by Natural Language Processing Models
" 618 ", " on ", " stock market ", " influence ", then mark the part of speech of each word." 618 (noun) ", " stock market (noun) ", " to (be situated between
Word) ".Noun in text is entity, the relationship " between ... influence " can determine entity in text, i.e. starting entity is
" 618 ", terminating entity is " stock market ".It will be understood by those skilled in the art that if only one entity of text information, has been defaulted as
Beginning entity.
When occurring has the entity of identical content, entity will not be replaced, and only increase by one on the corresponding node of entity
Relationship is directed toward next node.Final numerous data will will form network complicated in a mistake, and here it is knowledge mappings
It is formed.
In step s 106, knowledge mapping is generated according to the relationship between entity and entity.
Fig. 2 shows the schematic diagrames of knowledge mapping.Can be used in stored knowledge map Neo4j, OrientDB,
TITAN is, it is preferable to use Neo4j.Neo4j is a high performance graphic data base, and structural data is stored on network by it
Rather than in table.It is one it is Embedded, based on disk, have the Java persistence engine of complete transactional attribute, but
It is that it stores structural data on the diagram rather than in table.Neo4j can also be counted as a high performance figure engine, should
Engine has all characteristics of mature database.The structured message for including in the news data of acquisition can be portrayed in this way
In Neo4j database.
In above-described embodiment, automatic data collection source data simultaneously automatically processes event by natural language processing, parses
To relationship storage between entity and entity into knowledge mapping, thus for user query in a manner of knowledge mapping, it can be to user
It shows and the related entity of event or relationship, help user's quick obtaining dependent event information.
(2) data query
One embodiment of disclosure data query method is introduced below with reference to Fig. 3.
Fig. 3 shows the flow diagram of one embodiment of disclosure data query method.As shown in figure 3, this implementation
Data query method in example includes step S302~step S308.
In step s 302, the query information of user's input is received.
In step s 304, natural language processing is carried out to the query information of user's input, parsing obtains searching keyword.
In step S306, the corresponding query entity of searching keyword is determined in knowledge mapping.
In step S308, data-link associated with query entity in knowledge mapping is returned to user.
Query information according to user's input is different, and query process can substantially be divided into two kinds of situations.
The first situation is, using the query information of natural language processing parsing user's input, after obtaining searching keyword
The corresponding starting query entity of searching keyword is determined in knowledge mapping and terminates query entity, then returns to knowledge to user
To originate query entity as start node, the data-link to terminate query entity as end node in map.
For example, the query information of user's input is " Zhang San meets with Li Si ".Parsing " Zhang San meet with Li Si " obtain " Zhang San ",
" Li Si ".Starting query entity " Zhang San " is determined in knowledge mapping, terminates query entity " Li Si ".Finally inquire two numbers
According to chain, the first data chain is " Zhang San "->" U.S. "->" independent meeting "->" Korea "->" Li Si ", the second data
Chain is " Zhang San "->" sanction "->" Korea "->" Li Si ".
Second case is, using the query information of natural language processing parsing user's input, after obtaining searching keyword
The corresponding starting query entity of searching keyword is determined in knowledge mapping, then is returned in knowledge mapping to user to originate inquiry
Entity is the data-link of start node.
When determining data-link, related knot can be begun stepping through to originate query entity according to depth-first rule
Shu Shiti, then to terminate entity to start to continue to traverse related end node, until traversal terminates or reaches certain
Terminate after condition.
Optionally, in step S308, the descending sequence of the relationship number for including according to data-link returns to user and knows
Know data-link associated with query entity in map.I.e. two hit entities between existing relational tree more multipriority more
Height, relationship number mostly will preferentially be shown.Finally according to the ranking value of traversal number, the number of nodes more than incidence relation is preferentially returned
According to.
For example, the first data chain " Zhang San "->" U.S. "->" independent meeting "->" Korea "->" Li Si " prior to
Article 2 data-link " Zhang San "->" sanction "->" Korea "->" Li Si "
Optionally, if entity in query process in any map of miss, can by middleware (such as
Kafka text information) is transferred to the information collection stage, executes the data acquisition instructions comprising specified text.Fig. 4 shows number
It is investigated that asking the complete job flow chart of joint data acquisition.
In above-described embodiment, the related data chain in knowledge mapping, energy are returned in user input query information rear line
It is enough sufficiently to show that the upstream and downstream incidence relation of related data information has to extend the range of customer analysis data to user
Help user and carries out more comprehensive data analysis.
In addition, relationship is most important element in chart database, it can be by the interrelated structure of entity by relationship
Build relevant complex model.Each node in chart database model directly includes a relation list, is deposited in relation list
Put the relation record of this node Yu other nodes.These relation records are organized according to type and direction, and can be saved
Adeditive attribute.When no matter when running the connection JOIN operation of similarity relation database, chart database will all be come using this list
The directly node of access connection, without search, the matching primitives operation recorded, to improve the efficiency of information inquiry and steady
It is qualitative.
The data query device of an embodiment of the present disclosure is described below with reference to Fig. 5.
Fig. 5 shows the flow diagram of the data query device of an embodiment of the present disclosure.As shown in Fig. 5, this implementation
Example in data query device 50 include：
Information receiving module 502 is configured as receiving the query information of user's input.
Keyword resolution module 504 is configured as carrying out natural language processing to the query information that user inputs, parse
To searching keyword.
Entity determining module 506 is configured as determining the corresponding query entity of searching keyword in knowledge mapping.
Data return module 508 is configured as returning to data-link associated with query entity in knowledge mapping to user.
In some embodiments, entity determining module 506 is configured as：Determine that searching keyword is corresponding in knowledge mapping
Starting query entity；Data return module 508 is configured as：It is returned in knowledge mapping to user and is to originate query entity
The data-link of beginning node.
In some embodiments, entity determining module 506 is configured as：Determine that searching keyword is corresponding in knowledge mapping
Starting query entity and terminate query entity；Data return module 508 is configured as：It is returned in knowledge mapping to user
To originate query entity as start node, the data-link to terminate query entity as end node.
In some embodiments, data return module 508 is configured as：The relationship number for including according to data-link is descending
Sequence, to user return knowledge mapping in data-link associated with query entity.
In above-described embodiment, the related data chain in knowledge mapping, energy are returned in user input query information rear line
It is enough sufficiently to show that the upstream and downstream incidence relation of related data information has to extend the range of customer analysis data to user
Help user and carries out more comprehensive data analysis.
In some embodiments, data query device 50 further includes knowledge mapping generation module 500, is configured as：Acquisition
Database data；Natural language processing is carried out to database data, extracts the relationship between entity and entity；According to entity with
And the relationship between entity generates knowledge mapping.
In some embodiments, knowledge mapping generation module 500 is configured as：Using Natural Language Processing Models from data
Data critical word is extracted in the data of library；The reverse document frequency of word frequency-is higher than the data critical word of first threshold as entity；Benefit
With Natural Language Processing Models from database data extract entity between relationship.
In some embodiments, knowledge mapping generation module 500 is configured as：Configuration data source address list starts page
Code, end code and acquisition time；According to acquisition time, automatically extracts data source address list, starts the page number, end code
Identified news data；Parsing obtains the title and textual data in news data, stores to database.
In above-described embodiment, automatic data collection source data simultaneously automatically processes event by natural language processing, parses
To relationship storage between entity and entity into knowledge mapping, thus for user query in a manner of knowledge mapping, it can be to user
It shows and the related entity of event or relationship, help user's quick obtaining dependent event information.
Fig. 6 shows the structural schematic diagram of the data query device of the disclosure another embodiment.As shown in fig. 6, the reality
The data query device 60 for applying example includes：Memory 610 and the processor 620 for being coupled to the memory 610, processor 620
It is configured as executing the data query method in any one aforementioned embodiment based on the instruction being stored in memory 610.
Wherein, memory 610 is such as may include system storage, fixed non-volatile memory medium.System storage
Device is for example stored with operating system, application program, Boot loader (Boot Loader) and other programs etc..
Data query device 60 can also include input/output interface 630, network interface 640, memory interface 650 etc..This
It can for example be connected by bus 660 between a little interfaces 630,640,650 and memory 610 and processor 620.Wherein, defeated
Enter output interface 630 and provides connecting interface for input-output equipment such as display, mouse, keyboard, touch screens.Network interface 640
Connecting interface is provided for various networked devices.The external storages such as memory interface 650 is SD card, USB flash disk provide connecting interface.
The disclosure further includes a kind of computer readable storage medium, is stored thereon with computer instruction, and the instruction is processed
Device realizes the data query method in any one aforementioned embodiment when executing.
It should be understood by those skilled in the art that, embodiment of the disclosure can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the disclosure
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the disclosure, which can be used in one or more,
The calculating implemented in non-transient storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) can be used
The form of machine program product.
The disclosure is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present disclosure
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
The foregoing is merely the preferred embodiments of the disclosure, not to limit the disclosure, all spirit in the disclosure and
Within principle, any modification, equivalent replacement, improvement and so on be should be included within the protection scope of the disclosure.
Claims (16)
1. a kind of data query method, including：
Receive the query information of user's input；
Natural language processing is carried out to the query information of user's input, parsing obtains searching keyword；
The corresponding query entity of searching keyword is determined in knowledge mapping；
Data-link associated with query entity in knowledge mapping is returned to user.
2. data query method as described in claim 1, wherein
It is described to determine that the corresponding query entity of searching keyword includes in knowledge mapping：Determine that inquiry is crucial in knowledge mapping
The corresponding starting query entity of word；
It is described to user return knowledge mapping in data-link associated with query entity include：It is returned in knowledge mapping to user
To originate data-link of the query entity as start node.
3. data query method as described in claim 1, wherein
It is described to determine that the corresponding query entity of searching keyword includes in knowledge mapping：Determine that inquiry is crucial in knowledge mapping
The corresponding starting query entity of word and end query entity；
It is described to user return knowledge mapping in data-link associated with query entity include：It is returned in knowledge mapping to user
To originate query entity as start node, the data-link to terminate query entity as end node.
4. data query method as claimed any one in claims 1 to 3, wherein the relationship number for including according to data-link by
Small sequence is arrived greatly, returns to data-link associated with query entity in knowledge mapping to user.
5. data query method as described in claim 1, the data query method further include：
Acquisition database data；
Natural language processing is carried out to database data, extracts the relationship between entity and the entity；
Knowledge mapping is generated according to the relationship between entity and the entity.
6. data query method as claimed in claim 5, wherein it is described that natural language processing is carried out to database data, with
The relationship extracted between entity and the entity includes：
Data critical word is extracted from database data using Natural Language Processing Models；
The reverse document frequency of word frequency-is higher than the data critical word of first threshold as entity；
Using Natural Language Processing Models from the relationship extracted in database data between the entity.
7. data query method as claimed in claim 5, wherein the acquisition database data include：
Configuration data source address list starts the page number, end code and acquisition time；
According to acquisition time, automatically extracts data source address list, starts news data determined by the page number, end code；
Parsing obtains the title and textual data in the news data, stores to database.
8. a kind of data query device, including：
Information receiving module is configured as receiving the query information of user's input；
Keyword resolution module is configured as carrying out natural language processing to the query information that user inputs, and parsing is inquired
Keyword；
Entity determining module is configured as determining the corresponding query entity of searching keyword in knowledge mapping；
Data return module is configured as returning to data-link associated with query entity in knowledge mapping to user.
9. data query device as claimed in claim 8, wherein
The entity determining module is configured as：The corresponding starting query entity of searching keyword is determined in knowledge mapping；
The data return module is configured as：It returns in knowledge mapping to user to originate number of the query entity as start node
According to chain.
10. data query device as claimed in claim 8, wherein
The entity determining module is configured as：Determined in knowledge mapping the corresponding starting query entity of searching keyword and
Terminate query entity；
The data return module is configured as：To user return knowledge mapping in using originate query entity as start node, with
Terminate the data-link that query entity is end node.
11. the data query device as described in any one of claim 8 to 10, wherein the data return module is configured
For：The descending sequence of the relationship number for including according to data-link returns associated with query entity in knowledge mapping to user
Data-link.
12. data query device as claimed in claim 8, the data query device further includes knowledge mapping generation module,
It is configured as：
Acquisition database data；
Natural language processing is carried out to database data, extracts the relationship between entity and the entity；
Knowledge mapping is generated according to the relationship between entity and the entity.
13. data query device as claimed in claim 12, wherein the knowledge mapping generation module is configured as：
Data critical word is extracted from database data using Natural Language Processing Models；
The reverse document frequency of word frequency-is higher than the data critical word of first threshold as entity；
Using Natural Language Processing Models from the relationship extracted in database data between the entity.
14. data query device as claimed in claim 12, wherein the knowledge mapping generation module is configured as：
Configuration data source address list starts the page number, end code and acquisition time；
According to acquisition time, automatically extracts data source address list, starts news data determined by the page number, end code；
Parsing obtains the title and textual data in the news data, stores to database.
15. a kind of data query device, including：
Memory；And
It is coupled to the processor of the memory, the processor is configured to the instruction based on storage in the memory,
Execute the data query method as described in any one of claims 1 to 7.
16. a kind of computer readable storage medium, wherein the computer-readable recording medium storage has computer instruction, institute
State the data query method realized as described in any one of claims 1 to 7 when instruction is executed by processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810647001.3A CN108829858B (en) | 2018-06-22 | 2018-06-22 | Data query method and device and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810647001.3A CN108829858B (en) | 2018-06-22 | 2018-06-22 | Data query method and device and computer readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108829858A true CN108829858A (en) | 2018-11-16 |
| CN108829858B CN108829858B (en) | 2021-09-17 |
Family
ID=64143010
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810647001.3A Active CN108829858B (en) | 2018-06-22 | 2018-06-22 | Data query method and device and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108829858B (en) |
Cited By (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109658208A (en) * | 2019-01-15 | 2019-04-19 | 京东方科技集团股份有限公司 | Recommended method, device, medium and the electronic equipment of drug |
| CN109670049A (en) * | 2018-11-19 | 2019-04-23 | 平安科技（深圳）有限公司 | Map path query method, apparatus, computer equipment and storage medium |
| CN109710738A (en) * | 2018-12-24 | 2019-05-03 | 广州天鹏计算机科技有限公司 | Drug inquiry method, apparatus, system, computer equipment and storage medium |
| CN109766446A (en) * | 2018-12-13 | 2019-05-17 | 平安科技（深圳）有限公司 | A kind of data survey method, data survey device and computer readable storage medium |
| CN109918436A (en) * | 2019-03-08 | 2019-06-21 | 上海一健事信息科技有限公司 | A kind of Medical Knowledge management and inquiry system |
| CN110119463A (en) * | 2019-04-04 | 2019-08-13 | 厦门快商通信息咨询有限公司 | Information processing method, device, equipment and storage medium |
| CN110187678A (en) * | 2019-04-19 | 2019-08-30 | 广东省智能制造研究所 | A kind of storage of manufacturing industry process equipment information and digitlization application system |
| CN110209930A (en) * | 2019-04-30 | 2019-09-06 | 平安科技（深圳）有限公司 | The investee's analysis method and device of knowledge based map |
| CN110502645A (en) * | 2019-08-28 | 2019-11-26 | 中国联合网络通信集团有限公司 | Information query method and device |
| CN110674112A (en) * | 2019-09-23 | 2020-01-10 | 北京百分点信息科技有限公司 | Data query method and device and electronic equipment |
| CN110688490A (en) * | 2019-09-19 | 2020-01-14 | 上海数禾信息科技有限公司 | Method and device for configuring knowledge graph |
| CN110765295A (en) * | 2019-09-06 | 2020-02-07 | 中国平安财产保险股份有限公司 | Graph database-based query method and device, computer equipment and storage medium |
| CN110928984A (en) * | 2019-09-30 | 2020-03-27 | 珠海格力电器股份有限公司 | Knowledge graph construction method and device, terminal and storage medium |
| CN111061750A (en) * | 2019-12-17 | 2020-04-24 | Oppo广东移动通信有限公司 | Query processing method and device and computer readable storage medium |
| CN111475624A (en) * | 2020-04-13 | 2020-07-31 | 杭州海康威视***技术有限公司 | Monitoring data retrieval method, device and equipment |
| CN111538863A (en) * | 2020-03-19 | 2020-08-14 | 天津完美引力科技有限公司 | Data display method, device and system, storage medium and electronic device |
| CN111581188A (en) * | 2020-01-20 | 2020-08-25 | 全息空间(深圳)智能科技有限公司 | Live broadcast platform personal data tracking method, system and storage medium |
| CN111666746A (en) * | 2020-06-05 | 2020-09-15 | 中国银行股份有限公司 | Method and device for generating conference summary, electronic equipment and storage medium |
| CN111782824A (en) * | 2020-08-14 | 2020-10-16 | 中国工商银行股份有限公司 | Information query method, device, system and medium |
| CN111859969A (en) * | 2020-07-20 | 2020-10-30 | 航天科工智慧产业发展有限公司 | Data analysis method and device, electronic equipment and storage medium |
| CN111984797A (en) * | 2020-09-07 | 2020-11-24 | 中国银行股份有限公司 | Customer identity recognition device and method |
| CN112035506A (en) * | 2019-10-28 | 2020-12-04 | 竹间智能科技（上海）有限公司 | Semantic recognition method and equipment |
| CN112597105A (en) * | 2020-12-26 | 2021-04-02 | 中国农业银行股份有限公司 | Processing method of file associated object, server side equipment and storage medium |
| CN112711716A (en) * | 2021-01-25 | 2021-04-27 | 广东工业大学 | Knowledge graph-based marine industry news pushing method and system |
| CN112818092A (en) * | 2020-04-20 | 2021-05-18 | 腾讯科技（深圳）有限公司 | Knowledge graph query statement generation method, device, equipment and storage medium |
| CN112948547A (en) * | 2021-01-26 | 2021-06-11 | 中国石油大学(北京) | Logging knowledge graph construction query method, device, equipment and storage medium |
| CN112988950A (en) * | 2021-03-12 | 2021-06-18 | 成都数联铭品科技有限公司 | Front-end rendering method and system of knowledge graph, electronic device and storage medium |
| US12019990B2 (en) | 2019-12-17 | 2024-06-25 | Beijing Baidu Netcom Science Technology Co., Ltd. | Representation learning method and device based on natural language and knowledge graph |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130166590A1 (en) * | 2011-12-27 | 2013-06-27 | Electronics And Telecommunications Research Institute | Content search recommendation apparatus and method based on semantic network |
| US20130311242A1 (en) * | 2012-05-21 | 2013-11-21 | International Business Machines Corporation | Business Process Analytics |
| CN103870720A (en) * | 2014-03-19 | 2014-06-18 | 中国人民解放军国防科学技术大学 | Prediction method and device for protein signal transduction subnet |
| US8880521B2 (en) * | 2004-09-15 | 2014-11-04 | 3Degrees Llc | Collections of linked databases |
| CN104462504A (en) * | 2014-12-19 | 2015-03-25 | 北京奇虎科技有限公司 | Method and device for providing reasoning process data in search |
| CN104903886A (en) * | 2012-07-23 | 2015-09-09 | 脸谱公司 | Structured search queries based on social-graph information |
| CN106250393A (en) * | 2016-07-13 | 2016-12-21 | 广州安望信息科技有限公司 | The short text understanding method of a kind of knowledge based collection of illustrative plates and device |
| CN106713083A (en) * | 2016-11-24 | 2017-05-24 | 海信集团有限公司 | Intelligent home appliance control method and device based on knowledge map, and system |
| CN106919655A (en) * | 2017-01-24 | 2017-07-04 | 网易（杭州）网络有限公司 | A kind of answer provides method and apparatus |
| CN106919671A (en) * | 2017-02-20 | 2017-07-04 | 广东省中医院 | A kind of traditional Chinese medical science text medical record is excavated and aid decision intelligence system |
| CN107341215A (en) * | 2017-06-07 | 2017-11-10 | 北京航空航天大学 | A kind of vertical knowledge mapping classification ensemble querying method of multi-source based on Distributed Computing Platform |
| CN107368509A (en) * | 2012-12-31 | 2017-11-21 | 脸谱公司 | Communication means, communication system and computer-readable non-transitory storage medium |
| CN107515951A (en) * | 2017-09-20 | 2017-12-26 | 广东中标数据科技股份有限公司 | A kind of searching method based on graphic data base, system and device |
| CN107545000A (en) * | 2016-06-28 | 2018-01-05 | 百度在线网络技术（北京）有限公司 | The information-pushing method and device of knowledge based collection of illustrative plates |
| CN107783973A (en) * | 2016-08-24 | 2018-03-09 | 慧科讯业有限公司 | The methods, devices and systems being monitored based on domain knowledge spectrum data storehouse to the Internet media event |
| CN107944025A (en) * | 2017-12-12 | 2018-04-20 | 北京百度网讯科技有限公司 | Information-pushing method and device |
-
2018
- 2018-06-22 CN CN201810647001.3A patent/CN108829858B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8880521B2 (en) * | 2004-09-15 | 2014-11-04 | 3Degrees Llc | Collections of linked databases |
| US20130166590A1 (en) * | 2011-12-27 | 2013-06-27 | Electronics And Telecommunications Research Institute | Content search recommendation apparatus and method based on semantic network |
| US20130311242A1 (en) * | 2012-05-21 | 2013-11-21 | International Business Machines Corporation | Business Process Analytics |
| CN104903886A (en) * | 2012-07-23 | 2015-09-09 | 脸谱公司 | Structured search queries based on social-graph information |
| CN107368509A (en) * | 2012-12-31 | 2017-11-21 | 脸谱公司 | Communication means, communication system and computer-readable non-transitory storage medium |
| CN103870720A (en) * | 2014-03-19 | 2014-06-18 | 中国人民解放军国防科学技术大学 | Prediction method and device for protein signal transduction subnet |
| CN104462504A (en) * | 2014-12-19 | 2015-03-25 | 北京奇虎科技有限公司 | Method and device for providing reasoning process data in search |
| CN107545000A (en) * | 2016-06-28 | 2018-01-05 | 百度在线网络技术（北京）有限公司 | The information-pushing method and device of knowledge based collection of illustrative plates |
| CN106250393A (en) * | 2016-07-13 | 2016-12-21 | 广州安望信息科技有限公司 | The short text understanding method of a kind of knowledge based collection of illustrative plates and device |
| CN107783973A (en) * | 2016-08-24 | 2018-03-09 | 慧科讯业有限公司 | The methods, devices and systems being monitored based on domain knowledge spectrum data storehouse to the Internet media event |
| CN106713083A (en) * | 2016-11-24 | 2017-05-24 | 海信集团有限公司 | Intelligent home appliance control method and device based on knowledge map, and system |
| CN106919655A (en) * | 2017-01-24 | 2017-07-04 | 网易（杭州）网络有限公司 | A kind of answer provides method and apparatus |
| CN106919671A (en) * | 2017-02-20 | 2017-07-04 | 广东省中医院 | A kind of traditional Chinese medical science text medical record is excavated and aid decision intelligence system |
| CN107341215A (en) * | 2017-06-07 | 2017-11-10 | 北京航空航天大学 | A kind of vertical knowledge mapping classification ensemble querying method of multi-source based on Distributed Computing Platform |
| CN107515951A (en) * | 2017-09-20 | 2017-12-26 | 广东中标数据科技股份有限公司 | A kind of searching method based on graphic data base, system and device |
| CN107944025A (en) * | 2017-12-12 | 2018-04-20 | 北京百度网讯科技有限公司 | Information-pushing method and device |
Non-Patent Citations (1)
| Title |
|---|
| 胡芳槐: "基于多种数据源的中文知识图谱构建方法研究", 《中国博士学位论文全文数据库 信息科技辑》 * |
Cited By (37)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109670049A (en) * | 2018-11-19 | 2019-04-23 | 平安科技（深圳）有限公司 | Map path query method, apparatus, computer equipment and storage medium |
| CN109766446A (en) * | 2018-12-13 | 2019-05-17 | 平安科技（深圳）有限公司 | A kind of data survey method, data survey device and computer readable storage medium |
| CN109710738A (en) * | 2018-12-24 | 2019-05-03 | 广州天鹏计算机科技有限公司 | Drug inquiry method, apparatus, system, computer equipment and storage medium |
| CN109658208A (en) * | 2019-01-15 | 2019-04-19 | 京东方科技集团股份有限公司 | Recommended method, device, medium and the electronic equipment of drug |
| CN109918436A (en) * | 2019-03-08 | 2019-06-21 | 上海一健事信息科技有限公司 | A kind of Medical Knowledge management and inquiry system |
| CN110119463A (en) * | 2019-04-04 | 2019-08-13 | 厦门快商通信息咨询有限公司 | Information processing method, device, equipment and storage medium |
| CN110187678A (en) * | 2019-04-19 | 2019-08-30 | 广东省智能制造研究所 | A kind of storage of manufacturing industry process equipment information and digitlization application system |
| CN110209930A (en) * | 2019-04-30 | 2019-09-06 | 平安科技（深圳）有限公司 | The investee's analysis method and device of knowledge based map |
| CN110502645B (en) * | 2019-08-28 | 2022-07-08 | 中国联合网络通信集团有限公司 | Information query method and device |
| CN110502645A (en) * | 2019-08-28 | 2019-11-26 | 中国联合网络通信集团有限公司 | Information query method and device |
| CN110765295A (en) * | 2019-09-06 | 2020-02-07 | 中国平安财产保险股份有限公司 | Graph database-based query method and device, computer equipment and storage medium |
| CN110688490A (en) * | 2019-09-19 | 2020-01-14 | 上海数禾信息科技有限公司 | Method and device for configuring knowledge graph |
| CN110674112A (en) * | 2019-09-23 | 2020-01-10 | 北京百分点信息科技有限公司 | Data query method and device and electronic equipment |
| CN110928984A (en) * | 2019-09-30 | 2020-03-27 | 珠海格力电器股份有限公司 | Knowledge graph construction method and device, terminal and storage medium |
| CN112035506A (en) * | 2019-10-28 | 2020-12-04 | 竹间智能科技（上海）有限公司 | Semantic recognition method and equipment |
| US12019990B2 (en) | 2019-12-17 | 2024-06-25 | Beijing Baidu Netcom Science Technology Co., Ltd. | Representation learning method and device based on natural language and knowledge graph |
| CN111061750A (en) * | 2019-12-17 | 2020-04-24 | Oppo广东移动通信有限公司 | Query processing method and device and computer readable storage medium |
| CN111581188A (en) * | 2020-01-20 | 2020-08-25 | 全息空间(深圳)智能科技有限公司 | Live broadcast platform personal data tracking method, system and storage medium |
| CN111538863A (en) * | 2020-03-19 | 2020-08-14 | 天津完美引力科技有限公司 | Data display method, device and system, storage medium and electronic device |
| CN111538863B (en) * | 2020-03-19 | 2023-08-18 | 北京完美知识科技有限公司 | Data display method, device, system, storage medium and electronic device |
| CN111475624A (en) * | 2020-04-13 | 2020-07-31 | 杭州海康威视***技术有限公司 | Monitoring data retrieval method, device and equipment |
| CN111475624B (en) * | 2020-04-13 | 2023-08-04 | 杭州海康威视***技术有限公司 | Monitoring data retrieval method, device and equipment |
| CN112818092B (en) * | 2020-04-20 | 2023-08-11 | 腾讯科技（深圳）有限公司 | Knowledge graph query statement generation method, device, equipment and storage medium |
| CN112818092A (en) * | 2020-04-20 | 2021-05-18 | 腾讯科技（深圳）有限公司 | Knowledge graph query statement generation method, device, equipment and storage medium |
| CN111666746A (en) * | 2020-06-05 | 2020-09-15 | 中国银行股份有限公司 | Method and device for generating conference summary, electronic equipment and storage medium |
| CN111666746B (en) * | 2020-06-05 | 2023-09-29 | 中国银行股份有限公司 | Conference summary generation method and device, electronic equipment and storage medium |
| CN111859969A (en) * | 2020-07-20 | 2020-10-30 | 航天科工智慧产业发展有限公司 | Data analysis method and device, electronic equipment and storage medium |
| CN111859969B (en) * | 2020-07-20 | 2024-05-03 | 航天科工智慧产业发展有限公司 | Data analysis method and device, electronic equipment and storage medium |
| CN111782824B (en) * | 2020-08-14 | 2024-04-19 | 中国工商银行股份有限公司 | Information query method, device, system and medium |
| CN111782824A (en) * | 2020-08-14 | 2020-10-16 | 中国工商银行股份有限公司 | Information query method, device, system and medium |
| CN111984797A (en) * | 2020-09-07 | 2020-11-24 | 中国银行股份有限公司 | Customer identity recognition device and method |
| CN112597105A (en) * | 2020-12-26 | 2021-04-02 | 中国农业银行股份有限公司 | Processing method of file associated object, server side equipment and storage medium |
| CN112711716A (en) * | 2021-01-25 | 2021-04-27 | 广东工业大学 | Knowledge graph-based marine industry news pushing method and system |
| CN112948547A (en) * | 2021-01-26 | 2021-06-11 | 中国石油大学(北京) | Logging knowledge graph construction query method, device, equipment and storage medium |
| CN112948547B (en) * | 2021-01-26 | 2024-04-09 | 中国石油大学(北京) | Logging knowledge graph construction query method, device, equipment and storage medium |
| CN112988950A (en) * | 2021-03-12 | 2021-06-18 | 成都数联铭品科技有限公司 | Front-end rendering method and system of knowledge graph, electronic device and storage medium |
| CN112988950B (en) * | 2021-03-12 | 2023-10-13 | 成都数联铭品科技有限公司 | Front-end rendering method and system of knowledge graph, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108829858B (en) | 2021-09-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108829858A (en) | Data query method, apparatus and computer readable storage medium | |
| WO2019174132A1 (en) | Data processing method, server and computer storage medium | |
| CN104199965B (en) | Semantic information retrieval method | |
| JP6247292B2 (en) | Query expansion | |
| CN108038091A (en) | A kind of similar calculating of judgement document's case based on figure and search method and system | |
| CN103544255A (en) | Text semantic relativity based network public opinion information analysis method | |
| CN107102993B (en) | User appeal analysis method and device | |
| CN109726393B (en) | Policy analysis system and method based on natural language processing technology | |
| CN111274267A (en) | Database query method and device and computer readable storage medium | |
| WO2020211393A1 (en) | Written judgment information retrieval method and device, computer apparatus, and storage medium | |
| JP2008515061A (en) | A method for searching data elements on the web using conceptual and contextual metadata search engines | |
| CN111127068B (en) | Automatic pricing method and device for engineering quantity list | |
| CN111026965B (en) | Hot topic tracing method and device based on knowledge graph | |
| CN103559234A (en) | System and method for automated semantic annotation of RESTful Web services | |
| CN113094512B (en) | Fault analysis system and method in industrial production and manufacturing | |
| CN111708774B (en) | Industry analytic system based on big data | |
| CN114579104A (en) | Data analysis scene generation method, device, equipment and storage medium | |
| CN104484379B (en) | Determine the method and apparatus of music property relationship and inquiry processing method and device | |
| CN111553556A (en) | Business data analysis method and device, computer equipment and storage medium | |
| CN110928903A (en) | Data extraction method and device, equipment and storage medium | |
| CN110909126A (en) | Information query method and device | |
| CN110246496A (en) | Audio recognition method, system, computer equipment and storage medium | |
| CN115687572A (en) | Data information retrieval method, device, equipment and storage medium | |
| CN112084448A (en) | Similar information processing method and device | |
| CN111984625B (en) | Database load characteristic processing method and device, medium and electronic equipment |
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 | ||
| CB02 | Change of applicant information |
Address after: Room 221, 2nd floor, Block C, 18 Kechuang 11th Street, Beijing Daxing District, Beijing Applicant after: JINGDONG DIGITAL TECHNOLOGY HOLDINGS Co.,Ltd. Address before: Room 221, 2nd floor, Block C, 18 Kechuang 11th Street, Beijing Daxing District, Beijing Applicant before: BEIJING JINGDONG FINANCIAL TECHNOLOGY HOLDING Co.,Ltd. |
|
| CB02 | Change of applicant information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP01 | Change in the name or title of a patent holder |
Address after: Room 221, 2 / F, block C, 18 Kechuang 11th Street, Daxing District, Beijing, 100176 Patentee after: Jingdong Technology Holding Co.,Ltd. Address before: Room 221, 2 / F, block C, 18 Kechuang 11th Street, Daxing District, Beijing, 100176 Patentee before: Jingdong Digital Technology Holding Co.,Ltd. Address after: Room 221, 2 / F, block C, 18 Kechuang 11th Street, Daxing District, Beijing, 100176 Patentee after: Jingdong Digital Technology Holding Co.,Ltd. Address before: Room 221, 2 / F, block C, 18 Kechuang 11th Street, Daxing District, Beijing, 100176 Patentee before: JINGDONG DIGITAL TECHNOLOGY HOLDINGS Co.,Ltd. |
|
| CP01 | Change in the name or title of a patent holder |