CN109241078A - A kind of knowledge mapping hoc queries method based on hybrid database - Google Patents
A kind of knowledge mapping hoc queries method based on hybrid database Download PDFInfo
- Publication number
- CN109241078A CN109241078A CN201811005179.4A CN201811005179A CN109241078A CN 109241078 A CN109241078 A CN 109241078A CN 201811005179 A CN201811005179 A CN 201811005179A CN 109241078 A CN109241078 A CN 109241078A
- Authority
- CN
- China
- Prior art keywords
- entity
- relationship
- knowledge
- triplet sets
- neo4j
- 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
A kind of knowledge mapping hoc queries method based on hybrid database of the present invention, comprising: obtain the triplet sets that preset data is concentrated；Entity triplet sets and relationship triplet sets are distinguished from triplet sets；The storage that entity triplet sets are carried out on Neo4j, obtains the knowledge base with entity；For the construction of knowledge base index with entity, the knowledge base of tape index and entity is obtained；The storage that relationship triplet sets are carried out on Neo4j, obtains the knowledge base of tape index, entity and relationship；The storage that entity ambiguity information is carried out on MySQL, constructs entity ambiguity vocabulary；By the entity ambiguity vocabulary storage of building to the knowledge base of tape index, entity and relationship, complete knowledge base is obtained.The advantage of marriage relation type database and chart database of the present invention respectively, it is proposed the knowledge mapping method for organizing based on hybrid database, suitable for general extensive Opening field knowledge mapping, the search efficiency of knowledge mapping is improved while optimizing knowledge mapping storage organization.
Description
Technical field
The knowledge mapping hoc queries method based on hybrid database that present invention relates particularly to a kind of.
Background technique
Knowledge mapping has just lifted since Google2012 is proposed as a kind of efficient information tissue and retrieval mode
One knowledge mapping study upsurge is played.Relation extraction, knowledge reasoning, the representation of knowledge between entity extraction, attribute extraction, entity
Practise etc. is even more the hot spot for becoming research, but few documents refer to the bottom storage for how carrying out map, and
The interface for how combining design Storage to inquire, it is imperfect to the description of this respect content although being referred in other words,
It is too scattered.Storage and inquiry are all usually to occur as a whole, inquire the storage organization that has needed efficiently to prop up
It holds, and stores and then need constantly to optimize in conjunction with the characteristics of inquiry.
Traditional database, such as relevant database.It can be good at being carried out according to Schema layers of knowledge mapping of information
Cluster storage, the data age rate for accessing a certain classification is very high, but in other words, before being stored, need to know in advance
The Schema hierarchical information of data, and Schema is difficult to do big variation again once it is determined that get off, however for extensive
For the knowledge mapping of Opening field, the type of entity and relationship is usually more and complicated, is difficult to determine the Schema layer in map
Secondary information；Secondly, relevant database also seems power not when in face of multi-table join (usually connection depth is greater than 2) inquiry
From the heart, but carry out the very basic demand that such inquiry operation is knowledge mapping.
For NOSQL database, such as Major key database, column family storing data library, Oriented Documents database, figure
Database etc..Wherein the data structure of chart database and knowledge mapping are the most close to showing as by a large amount of entity node and reality
The huge graph structure model of incidence relation composition between body, it can show well between specific or abstract things
Connection；It can be good at the demand for the local access's characteristic for meeting figure simultaneously.But for being unsatisfactory for diagram data knot in map
The information of structure, for example, ambiguity information between entity this how to be stored, then become a problem also to be solved.
Summary of the invention
The technical problem to be solved in the present invention is that for above-mentioned current traditional Relational DataBase and chart database technology
Deficiency, a kind of knowledge mapping hoc queries method based on hybrid database is provided and is solved the above problems.
A kind of knowledge mapping hoc queries method based on hybrid database, comprising:
Step 1 obtains the triplet sets that preset data is concentrated；
Entity triplet sets and relationship triple collection are distinguished in step 2, the triplet sets obtained from step 1
It closes；
Step 3, the storage that entity triplet sets are carried out on Neo4j, obtain the knowledge base with entity；
Step 4, for the entity node building index stored in the knowledge base with entity, obtain knowing for tape index and entity
Know library；
Step 5, the storage that relationship triplet sets are carried out on Neo4j, obtain the knowledge of tape index, entity and relationship
Library；
Step 6, the storage that entity ambiguity is carried out on MySQL, construct entity ambiguity vocabulary；
The entity ambiguity vocabulary constructed in step 6 is stored tape index, entity and the relationship obtained to step 5 by step 7
Knowledge base obtains complete knowledge base；
Step 8, input entity to be checked are obtained using the method that the two-stage of MySQL+Neo4j is inquired in step 7 complete
It is inquired in whole knowledge base, obtains complete entity information.
Further, preset data collection described in step 2 refers to the general description to entity and relationship, is structuring number
According to the combination of any one or more in, unstructured data and semi-structured data.
Further, specific storage method is in step 3: different entity sections is distinguished from entity triplet sets
It puts and is stored.
Further, specific storage method is in step 5: entity section end to end is distinguished from relationship triplet sets
Then point inquires entity end to end in the knowledge base of tape index and entity that step 4 obtains, be the building of node end to end if hit
Relationship, otherwise relationship is cancelled.
Further, entity ambiguity described in step 6 refers to the feelings of existing polysemy and synonym between entity
Condition.
Further, the two-stage query structure of the MySQL+Neo4j specifically includes:
(1) entity to be checked is inputted, it is necessary first to carry out SQL query in MySQL database, judge whether inquiry orders
In: if SQL query is hit, determining entity to be checked, there are ambiguities, its corresponding all ambiguity entity is returned to user, and
Entity is disambiguated, the entity after disambiguation is input to progress CQL inquiry in Neo4j database；If SQL query is not hit by,
Determining entity to be checked, there is no ambiguities, and entity transmission to be checked is directly carried out to CQL inquiry into Neo4j database；
(2) CQL inquiry is carried out using the entity after entity to be checked or disambiguation as the input of Neo4j database, obtained
Complete entity information, as last output.
Further, the method for judging whether inquiry hits in SQL query is: entity and step 6 to be checked are obtained
To entity ambiguity vocabulary compare, match if it exists, query hit, it is on the contrary then inquiry be not hit by.
Present invention has an advantage that the advantage of marriage relation type database and chart database respectively, proposes to be based on mixed number
According to the knowledge mapping method for organizing in library, it is suitable for general extensive Opening field knowledge mapping, in optimization knowledge mapping storage
The search efficiency of knowledge mapping is improved while structure.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples, in attached drawing:
Fig. 1 is a kind of knowledge mapping hoc queries method flow diagram based on hybrid database of the invention；
Fig. 2 is the two-stage query structure figure of MySQL+Neo4j of the invention.
Specific embodiment
For a clearer understanding of the technical characteristics, objects and effects of the present invention, now control attached drawing is described in detail
A specific embodiment of the invention.
As shown in Figure 1, a kind of knowledge mapping hoc queries method based on hybrid database, comprising:
Step 1 obtains the triplet sets that preset data is concentrated, and preset data collection refers to that the generality to entity and relationship is retouched
It states, including structural data, unstructured data and semi-structured data；
Entity triplet sets and relationship triple collection are distinguished in step 2, the triplet sets obtained from step 1
It closes；
Step 3, the storage that entity triplet sets are carried out on Neo4j, distinguish difference from entity triplet sets
Entity node and stored, obtain the knowledge base with entity；
Step 4, for the entity node building index stored in the knowledge base with entity, obtain knowing for tape index and entity
Know library
Step 5, the storage that relationship triplet sets are carried out on Neo4j, are distinguished end to end from relationship triplet sets
Then entity node inquires entity end to end in the knowledge base of tape index and entity that step 4 obtains, save end to end if hit
Point building relationship, otherwise relationship is cancelled, and obtains the knowledge base of tape index, entity and relationship；
Step 6, the storage that entity ambiguity information is carried out on MySQL, construct entity ambiguity vocabulary, entity ambiguity refers to reality
Existing polysemy and the case where synonym between body.；
The entity ambiguity vocabulary constructed in step 6 is stored tape index, entity and the relationship obtained to step 5 by step 7
Knowledge base obtains complete knowledge base.
Step 8, input entity to be checked are obtained using the method that the two-stage of MySQL+Neo4j is inquired in step 7 complete
It is inquired in whole knowledge base, obtains complete entity information.
The method of the two-stage inquiry of MvSQL+Neo4j is specifically: query entity whether there is entity discrimination first in MvSOL
Adopted information is then disambiguated to enter back into Neo4j later and be inquired, otherwise directly inquired in Neo4j if it exists.As shown in Fig. 2,
Query process is as follows:
1, SQL query (as shown in label 1 in Fig. 2)
Because can not know whether the physical name of input has the case where ambiguity, thus the physical name inputted firstly the need of
SQL query is carried out in MySQL database, i.e., the physical name of input is matched into (discrimination with the first row of the ambiguity vocabulary in Fig. 2
The first of adopted vocabulary is classified as physical name, and second is classified as the entity there are ambiguity, such as key-value pair<S1,<E1, E2>>presentation-entity name
There are ambiguities by S1, and there are the entity E1 and E2 of ambiguity to be directed toward same character string S1), if hit, it can return and be directed toward same character
Multiple entities of string.Whether according to query hit, point following two situation is handled:
1) SQL query is hit:
There are ambiguities (as shown in Fig. 2, there are ambiguities by the physical name Sm of input, therefore after query hit for the physical name inputted
Return to the ambiguity entity Ek~Ek+n for being directed toward same character string Sm), corresponding all ambiguity entity Ek~Ek+n will be inputted and returned
It is disambiguated to user, and to entity and (as shown in label 2 in Fig. 2, is determined specifically to disambiguate mode by specific application scenarios),
Entity (Ek+i) after disambiguation is input to progress CQL inquiry in Neo4j database (as shown in label 3 in Fig. 2).
2) SQL query is not hit by:
Ambiguity is not present in the physical name inputted, directly progress CQL inquiry.
2, CQL inquires (i.e. to the inquiry of knowledge base in Fig. 2)
No matter whether SQL query hits, and finally obtained is all physical name.The complete information of entity in order to obtain needs
CQL inquiry is carried out using obtained physical name as the input of Neo4j database, so that complete entity information is obtained, as
Finally to the response of user's input.
Specific query case is as follows:
Query example 1: there are entity ambiguities for the physical name of input
1) entity: daphne odera is inputted
2) inquiry of ambiguity vocabulary SQL query: is carried out in MySQL
3) SQL query hit (there are ambiguities for the physical name " daphne odera " of representative input), returns to the discrimination for being directed toward " daphne odera "
Adopted entity:
Daphne odera (releases an album) for Zhou Jielun 2004
Daphne odera (Rutaceae Murraya plant)
Daphne odera (Zhou Jielun gives song recitals)
Daphne odera (poem name, collection of poems name)
Daphne odera (Thailand's TV series)
Daphne odera (Chinese medicine)
Daphne odera (novel " daphne odera ")
………………
4) entity disambiguates:
Assuming that based on context carrying out entity disambiguation at this time.
Context are as follows: " daphne odera of Zhou Jielun is the song that I is delithted with ".
Therefore the entity after based on context disambiguating are as follows: daphne odera (Zhou Jielun gives song recitals)
5) CQL is inquired:
Entity " daphne odera (Zhou Jielun gives song recitals) " after disambiguation is subjected to entity information inquiry in Neo4j, is obtained
Final output:
Daphne odera (Zhou Jielun gives song recitals)
BaiduTAG: musical works/single
Chinese name: daphne odera
Issuing date: 2004
Song original singer: Zhou Jielun
It composes a poem to a given tune of ci: square mountain of papers
Affiliated album: " daphne odera (releasing an album for Zhou Jielun 2004) "
Song duration: 4:56
Song language: mandarin
Music: Zhong Xinmin
It sets a song to music: Zhou Jielun
Music style: Chinese feature
………………
Query example 2: assuming that entity ambiguity is not present in the physical name of input
1) input entity: daphne odera (Zhou Jielun gives song recitals)
2) inquiry of ambiguity vocabulary SQL query: is carried out in MySQL
3) SQL query is not hit by and (represents the physical name inputted at this time and ambiguity is not present)
4) CQL is inquired:
Entity information inquiry is carried out in Neo4j, obtains final output:
Daphne odera (Zhou Jielun gives song recitals)
BaiduTAG: musical works/single
Chinese name: daphne odera
Issuing date: 2004
Song original singer: Zhou Jielun
It composes a poem to a given tune of ci: square mountain of papers
Affiliated album: " daphne odera (releasing an album for Zhou Jielun 2004) "
Song duration: 4:56
Song language: mandarin
Music: Zhong Xinmin
It sets a song to music: Zhou Jielun
Music style: Chinese feature
………………
The embodiment of the present invention is described with above attached drawing, but the invention is not limited to above-mentioned specific
Embodiment, the above mentioned embodiment is only schematical, rather than restrictive, those skilled in the art
Under the inspiration of the present invention, without breaking away from the scope protected by the purposes and claims of the present invention, it can also make very much
Form, all of these belong to the protection of the present invention.
Claims (7)
1. a kind of knowledge mapping hoc queries method based on hybrid database characterized by comprising
Step 1 obtains the triplet sets that preset data is concentrated；
Entity triplet sets and relationship triplet sets are distinguished in step 2, the triplet sets obtained from step 1；
Step 3, the storage that entity triplet sets are carried out on Neo4j, obtain the knowledge base with entity；
Step 4 is indexed for the entity node building stored in the knowledge base with entity, obtains the knowledge of tape index and entity
Library；
Step 5, the storage that relationship triplet sets are carried out on Neo4j, obtain the knowledge base of tape index, entity and relationship；
Step 6, the storage that entity ambiguity is carried out on MySQL, construct entity ambiguity vocabulary；
The knowledge of step 7, tape index, entity and relationship that the entity ambiguity vocabulary constructed in step 6 storage is obtained to step 5
Library obtains complete knowledge base；
Step 8, input entity to be checked are obtained using the method that the two-stage of MySQL+Neo4j is inquired in step 7 complete
It is inquired in knowledge base, obtains complete entity information.
2. a kind of knowledge mapping hoc queries method based on hybrid database according to claim 1, which is characterized in that
Preset data collection described in step 2 refers to the general description to entity and relationship, is structural data, unstructured data and half
The combination of any one or more in structural data.
3. a kind of knowledge mapping hoc queries method based on hybrid database according to claim 1, which is characterized in that
Specific storage method is in step 3: distinguishing different entity nodes from entity triplet sets and is stored.
4. a kind of knowledge mapping hoc queries method based on hybrid database according to claim 1, which is characterized in that
Specific storage method is in step 5: distinguishing entity node end to end from relationship triplet sets, then obtains in step 4
Tape index and entity knowledge base in inquire entity end to end, construct relationship if hit for node end to end, otherwise relationship is cancelled.
5. a kind of knowledge mapping hoc queries method based on hybrid database according to claim 1, which is characterized in that
Entity ambiguity described in step 6 refers between entity the case where existing polysemy and synonym.
6. a kind of knowledge mapping hoc queries method based on hybrid database according to claim 1, which is characterized in that
The two-stage query structure of the MySQL+Neo4j specifically includes:
(1) entity to be checked is inputted, it is necessary first to carry out SQL query in MySQL database, judge whether inquiry hits:
If SQL query is hit, determining entity to be checked, there are ambiguities, its corresponding all ambiguity entity are returned to user, and right
Entity is disambiguated, and the entity after disambiguation is input to progress CQL inquiry in Neo4j database；If SQL query is not hit by, sentence
Ambiguity is not present in fixed entity to be checked, and entity transmission to be checked is directly carried out to CQL inquiry into Neo4j database；
(2) CQL inquiry is carried out using the entity after entity to be checked or disambiguation as the input of Neo4j database, obtained complete
Entity information, as last output.
7. a kind of knowledge mapping hoc queries method based on hybrid database according to claim 6, which is characterized in that
The method whether hit of inquiry is judged in SQL query is: the entity ambiguity vocabulary that entity and step 6 to be checked are obtained into
Row comparison, matches, query hit if it exists, and on the contrary then inquiry is not hit by.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811005179.4A CN109241078B (en) | 2018-08-30 | 2018-08-30 | Knowledge graph organization query method based on mixed database |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811005179.4A CN109241078B (en) | 2018-08-30 | 2018-08-30 | Knowledge graph organization query method based on mixed database |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109241078A true CN109241078A (en) | 2019-01-18 |
| CN109241078B CN109241078B (en) | 2021-07-20 |
Family
ID=65067986
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811005179.4A Active CN109241078B (en) | 2018-08-30 | 2018-08-30 | Knowledge graph organization query method based on mixed database |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109241078B (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110019687A (en) * | 2019-04-11 | 2019-07-16 | 宁波深擎信息科技有限公司 | A kind of more intention assessment systems, method, equipment and the medium of knowledge based map |
| CN110489610A (en) * | 2019-08-14 | 2019-11-22 | 北京海致星图科技有限公司 | A kind of knowledge mapping real-time query solution |
| CN110597927A (en) * | 2019-10-14 | 2019-12-20 | 上海依图网络科技有限公司 | Storage query method and device based on heterogeneous database |
| CN110928960A (en) * | 2019-10-28 | 2020-03-27 | 华中科技大学 | Data storage system, method, equipment and storage medium |
| CN111160841A (en) * | 2019-11-29 | 2020-05-15 | 广东轩辕网络科技股份有限公司 | Organization architecture construction method and device based on knowledge graph |
| CN111859974A (en) * | 2019-04-22 | 2020-10-30 | 广东小天才科技有限公司 | Semantic disambiguation method and device combined with knowledge graph and intelligent learning equipment |
| CN113297089A (en) * | 2021-06-09 | 2021-08-24 | 南京大学 | Crowd-sourcing assistant implementation method based on knowledge graph |
| CN113342807A (en) * | 2021-05-20 | 2021-09-03 | 电子科技大学 | Knowledge graph based on mixed database and construction method thereof |
| CN113761213A (en) * | 2020-06-01 | 2021-12-07 | Tcl科技集团股份有限公司 | Data query system and method based on knowledge graph and terminal equipment |
| CN114238268A (en) * | 2021-11-29 | 2022-03-25 | 武汉达梦数据技术有限公司 | Data storage method and device |
| CN114398492A (en) * | 2021-12-24 | 2022-04-26 | 森纵艾数(北京)科技有限公司 | Knowledge graph construction method, terminal and medium in digital field |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140282219A1 (en) * | 2013-03-15 | 2014-09-18 | Robert Haddock | Intelligent internet system with adaptive user interface providing one-step access to knowledge |
| US20140372447A1 (en) * | 2013-06-12 | 2014-12-18 | Electronics And Telecommunications Research Institute | Knowledge index system and method of providing knowledge index |
| CN105224630A (en) * | 2015-09-24 | 2016-01-06 | 中国科学院自动化研究所 | Based on the integrated approach of Ontology on Semantic Web data |
| CN106815293A (en) * | 2016-12-08 | 2017-06-09 | 中国电子科技集团公司第三十二研究所 | System and method for constructing knowledge graph for information analysis |
| CN107330125A (en) * | 2017-07-20 | 2017-11-07 | 云南电网有限责任公司电力科学研究院 | The unstructured distribution data integrated approach of magnanimity of knowledge based graphical spectrum technology |
| CN107633075A (en) * | 2017-09-22 | 2018-01-26 | 吉林大学 | A kind of multi-source heterogeneous data fusion platform and fusion method |
| US20180137424A1 (en) * | 2016-11-17 | 2018-05-17 | General Electric Company | Methods and systems for identifying gaps in predictive model ontology |
-
2018
- 2018-08-30 CN CN201811005179.4A patent/CN109241078B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140282219A1 (en) * | 2013-03-15 | 2014-09-18 | Robert Haddock | Intelligent internet system with adaptive user interface providing one-step access to knowledge |
| US20140372447A1 (en) * | 2013-06-12 | 2014-12-18 | Electronics And Telecommunications Research Institute | Knowledge index system and method of providing knowledge index |
| CN105224630A (en) * | 2015-09-24 | 2016-01-06 | 中国科学院自动化研究所 | Based on the integrated approach of Ontology on Semantic Web data |
| US20180137424A1 (en) * | 2016-11-17 | 2018-05-17 | General Electric Company | Methods and systems for identifying gaps in predictive model ontology |
| CN106815293A (en) * | 2016-12-08 | 2017-06-09 | 中国电子科技集团公司第三十二研究所 | System and method for constructing knowledge graph for information analysis |
| CN107330125A (en) * | 2017-07-20 | 2017-11-07 | 云南电网有限责任公司电力科学研究院 | The unstructured distribution data integrated approach of magnanimity of knowledge based graphical spectrum technology |
| CN107633075A (en) * | 2017-09-22 | 2018-01-26 | 吉林大学 | A kind of multi-source heterogeneous data fusion platform and fusion method |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110019687B (en) * | 2019-04-11 | 2021-03-23 | 宁波深擎信息科技有限公司 | Multi-intention recognition system, method, equipment and medium based on knowledge graph |
| CN110019687A (en) * | 2019-04-11 | 2019-07-16 | 宁波深擎信息科技有限公司 | A kind of more intention assessment systems, method, equipment and the medium of knowledge based map |
| CN111859974A (en) * | 2019-04-22 | 2020-10-30 | 广东小天才科技有限公司 | Semantic disambiguation method and device combined with knowledge graph and intelligent learning equipment |
| CN110489610A (en) * | 2019-08-14 | 2019-11-22 | 北京海致星图科技有限公司 | A kind of knowledge mapping real-time query solution |
| CN110597927A (en) * | 2019-10-14 | 2019-12-20 | 上海依图网络科技有限公司 | Storage query method and device based on heterogeneous database |
| CN110928960B (en) * | 2019-10-28 | 2023-08-11 | 华中科技大学 | Data storage system, method, equipment and storage medium |
| CN110928960A (en) * | 2019-10-28 | 2020-03-27 | 华中科技大学 | Data storage system, method, equipment and storage medium |
| CN111160841A (en) * | 2019-11-29 | 2020-05-15 | 广东轩辕网络科技股份有限公司 | Organization architecture construction method and device based on knowledge graph |
| CN113761213B (en) * | 2020-06-01 | 2024-06-18 | Tcl科技集团股份有限公司 | Knowledge graph-based data query system, method and terminal equipment |
| CN113761213A (en) * | 2020-06-01 | 2021-12-07 | Tcl科技集团股份有限公司 | Data query system and method based on knowledge graph and terminal equipment |
| CN113342807A (en) * | 2021-05-20 | 2021-09-03 | 电子科技大学 | Knowledge graph based on mixed database and construction method thereof |
| CN113297089B (en) * | 2021-06-09 | 2023-06-20 | 南京大学 | Knowledge graph-based mass measurement assistant implementation method |
| CN113297089A (en) * | 2021-06-09 | 2021-08-24 | 南京大学 | Crowd-sourcing assistant implementation method based on knowledge graph |
| CN114238268B (en) * | 2021-11-29 | 2022-09-30 | 武汉达梦数据技术有限公司 | Data storage method and device |
| CN114238268A (en) * | 2021-11-29 | 2022-03-25 | 武汉达梦数据技术有限公司 | Data storage method and device |
| CN114398492A (en) * | 2021-12-24 | 2022-04-26 | 森纵艾数(北京)科技有限公司 | Knowledge graph construction method, terminal and medium in digital field |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109241078B (en) | 2021-07-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109241078A (en) | A kind of knowledge mapping hoc queries method based on hybrid database | |
| US11790006B2 (en) | Natural language question answering systems | |
| US20220382752A1 (en) | Mapping Natural Language To Queries Using A Query Grammar | |
| US10140333B2 (en) | Trusted query system and method | |
| CN103646032B (en) | A kind of based on body with the data base query method of limited natural language processing | |
| CN111291161A (en) | Legal case knowledge graph query method, device, equipment and storage medium | |
| KR101646754B1 (en) | Apparatus and Method of Mobile Semantic Search | |
| US20160055184A1 (en) | Data virtualization across heterogeneous formats | |
| CN111190900B (en) | JSON data visualization optimization method in cloud computing mode | |
| CN104657439A (en) | Generation system and method for structured query sentence used for precise retrieval of natural language | |
| CN112231321B (en) | Oracle secondary index and index real-time synchronization method | |
| CN104991905A (en) | Method for mathematical expression retrieval based on hierarchical indexing | |
| CN102810114A (en) | Personal computer resource management system based on body | |
| CN113190687B (en) | Knowledge graph determining method and device, computer equipment and storage medium | |
| CN103440232A (en) | Automatic sScientific paper standardization automatic detecting and editing method | |
| CN103440233A (en) | Automatic sScientific paper standardization automatic detecting and editing system | |
| CN106649879A (en) | Method for intelligent recommendation of professional book in library | |
| JP3653333B2 (en) | Database management method and system | |
| CN104537047A (en) | Garment basic sample plate retrieval system based on Lucene | |
| Mondal et al. | Natural language query to NoSQL generation using query-response model | |
| CN113849596A (en) | Intelligent search method based on natural language processing | |
| Chakrabarti et al. | Enhancing search with structure | |
| Ibrahim et al. | Exquisite: explaining quantities in text | |
| JP2009104276A (en) | Data management device | |
| CN110543468A (en) | Automatic construction method for big data knowledge base in public security field |
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 | ||
| EE01 | Entry into force of recordation of patent licensing contract | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20190118 Assignee: WUHAN TIMES GEOSMART TECHNOLOGY Co.,Ltd. Assignor: CHINA University OF GEOSCIENCES (WUHAN CITY) Contract record no.: X2022420000021 Denomination of invention: An organization and query method of knowledge map based on hybrid database Granted publication date: 20210720 License type: Common License Record date: 20220302 |