CN110297872A - A kind of building, querying method and the system of sciemtifec and technical sphere knowledge mapping - Google Patents
A kind of building, querying method and the system of sciemtifec and technical sphere knowledge mapping Download PDFInfo
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- 230000008676 import Effects 0.000 claims description 9
- 238000006243 chemical reaction Methods 0.000 claims description 7
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Abstract
The invention discloses building, querying method and the systems of a kind of sciemtifec and technical sphere knowledge mapping, belong to Scientific Management Information technical field.The building of sciemtifec and technical sphere knowledge mapping of the invention, querying method user comb the data in relational database, comb out the object to be defined, relationship, attribute information, define object, relationship, attribute information by data modeling, and define relational database tables of data to object, relationship, attribute map information；It is object, relationship, attribute instance storage into chart database by relational database persistence architecture, is matched by attribute value and carry out map query analysis.The building of the sciemtifec and technical sphere knowledge mapping of the invention, querying method can preferably meet user and find demand to the value exploration and information of big data gold mine, have good application value.
Description
Technical field
The present invention relates to Scientific Management Information technical fields, specifically provide a kind of building of sciemtifec and technical sphere knowledge mapping, look into
Ask method and system.
Background technique
In recent years, knowledge mapping is becoming a kind of key technology for realizing multi-source heterogeneous hypermedia data fusion.Knowledge
Map is substantially a kind of semantic network, and the node in figure represents entity or concept, various between Bian Daibiao entity/concept
Semantic relation.The Ontology Modeling of knowledge based graphical spectrum technology is provided for the big data of multi-source, isomery, type multiplicity
A kind of unified data model of high abstract concept level.Based on such a data model, pass through one group of map Core Generator
The big data of various sources, isomery, magnanimity is converged, merged and is associated together and stored.Knowledge based map
Big data analysis realizes the essential semantic association of big data, freely diversification more than traditional relevant database, can
Preferably meet user and demand is found to the value exploration and information of big data gold mine.
But in the prior art have knowledge mapping construction method at least have the following deficiencies: 1) data model be difficult to according to
Specific industry flexible expansion；2) open world knowledge map focuses on range, emphasizes to merge more entities, and accuracy is not high,
And influenced to be difficult to cover the pass between the entity, attribute, entity in the vertical field of specific industry by ontology library by concept and range
System etc..
Summary of the invention
Technical assignment of the invention is that in view of the above problems, user can preferably be met to big number by providing one kind
According to the building of the sciemtifec and technical sphere knowledge mapping of value exploration and information the discovery demand of gold mine, querying method.
The further technical assignment of the present invention is to provide a kind of building of sciemtifec and technical sphere knowledge mapping, inquiry system.
To achieve the above object, the present invention provides the following technical scheme that
A kind of building of sciemtifec and technical sphere knowledge mapping, querying method, user is to the data in relational database in this method
It is combed, combs out the object to be defined, relationship, attribute information, object, relationship, attribute information are defined by data modeling,
And define relational database tables of data to object, relationship, attribute map information；It is pair by relational database persistence architecture
As, relationship, attribute instance storage into chart database, pass through attribute value matching carry out map query analysis.
Preferably, this method specifically includes the following steps:
S1, combing data: the data in relational database are combed；
S2, data modeling: user is defined object, attribute, relational model according to hierarchical relationship；
S3, tables of data is defined in relational database to object, the mapping of relationship, attribute；
S4, spectrum data import: by the persistence architecture in relational database in pairs as, relationship, attribute instance data
It stores in chart database；
S5, progress map query analysis is matched by attribute value.
Preferably, combing out scientific research personnel, scientific research project, science research output, R&D institution's object in step S1, comb
Project output, mechanism output, mechanism project, facility personnel's relationship out, while combing out every kind of object, the attribute letter that relationship includes
Breath.
Preferably, in step S2, defining object includes scientific research personnel, scientific research project, science research output, R&D institution, often
It include multiple attributes in kind object；Definition relationship includes direct relation definition and indirect relation definition, and direct relation definition includes
Project output, mechanism output, mechanism project, facility personnel include multiple attributes in every kind of relationship；Indirect relation definition, including
Same project, same to mechanism, same output include multiple attribute design conditions in every kind of indirect relation.
Wherein, defining object includes multiple attributes, such as scientific research personnel's object may include expert's name, expert's ranking belongs to
Property information.Each object corresponds to a vertexLabel in JanusGraph, and each attribute corresponds to one in JanusGraph
A PropertyKey.
Definition relationship include multiple attributes, as facility personnel's relationship may include organization, personnel's title, whether
The attribute informations such as duty, hiring date, leaving date.Each relationship corresponds to an edgeLabel in JanusGraph.
Indirect relation, which defines, to be calculated according to direct relation by the attribute conditions inquiry of setting.
Preferably, defining tables of data in relational database in step S3 and defining relationship number to object, the mapping of relationship
Whether mapping and attribute according to the field of tables of data in library to attribute will index.
Preferably, in step S4, data mapping relations according to defined in step S3 for being extracted from relational database
Carry out data conversion, by Apache NiFi by the persistence architecture in relational database in pairs as, relationship, attribute instance
Data are stored into chart database.
The data extracted from relational database mapping relations according to defined in step S3 carry out data conversion, wherein
JanusGraph uses HBase as storage rear end, uses ElasticSearch as index rear end.Specific step is as follows:
1) data source is selected, the tables of data for having built mapping under data source in step s 2 is selected；
2) data guiding flow is created by pulling mode in Apache NiFi, wherein what is defined in step s3 reflects
It penetrates and provides Data Conversion Service in a manner of Restful for NiFi；
3) Booting sequence extracts data into chart database.
Preferably, carrying out being based on Gremlin figure query language when map query analysis in step S5, pass through scientific research personnel
Name or the matched mode of multiple attribute values inquire scientific research personnel, and then inquire the relation information of specific scientific research personnel.It closes indirectly
System's inquiry, needs to be calculated by indirect relation attribute conditions.Wherein, scientific research project, scientific research institution's map query analysis with
Scientific research personnel's map query analysis is identical.
A kind of building of sciemtifec and technical sphere knowledge mapping, inquiry system, the system include combing data module, data modeling mould
Mapping definition module, spectrum data import modul and the map query analysis module of block, object, relationship and attribute；
Combing data module is for combing the data in relational database；
Data modeling module is defined object, attribute, relational model according to hierarchical relationship for user；
The mapping definition module of object, relationship and attribute is for defining in relational database tables of data to object, relationship, category
The mapping of property；
Spectrum data import modul be used for by the persistence architecture in relational database in pairs as, relationship, attribute instance
Data are stored into chart database；
Map query analysis module, which is used to match by attribute value, carries out map query analysis.
Preferably, the combing data module combs out scientific research personnel, scientific research project, science research output, R&D institution pair
As, comb out project output, mechanism output, mechanism project, facility personnel's relationship, while comb out every kind of object, relationship includes
Attribute information.
Preferably, being based on Gremlin figure query language when map query analysis module carries out map query analysis, lead to
It crosses scientific research personnel's name or the matched mode of multiple attribute values inquires scientific research personnel, and then inquire the relationship letter of specific scientific research personnel
Breath.
Compared with prior art, the building of sciemtifec and technical sphere knowledge mapping of the invention, querying method are with following prominent
The utility model has the advantages that
(1) building of the sciemtifec and technical sphere knowledge mapping, querying method support user's custom object, relationship, attribute,
Can application scenarios change in the case where flexible expansion；
(2) it supports to establish tables of data to object, the mapping of relationship and field to the mapping of attribute, passes through Apache
Nifi extracts data in relational database and is converted to object, attribute, the storage of relationship example data into chart database, supports data
Incremental update；
(3) the current world knowledge map construction method of effective solution is applied to concept existing for specific industry, accurate
The drawbacks such as property；
(4) it provides man-machine interactive relationship to explore, effectively combines the computing capability of computer and the cognitive ability of user,
Allow user is very clear to obtain scientific research personnel, scientific research project, scientific research institution, the direct relation of science research output and close indirectly
System, it is easy to operate, it helps user from incidence relation between mining data in mass data, there is good application value.
Detailed description of the invention
Fig. 1 is the flow chart of the building of sciemtifec and technical sphere knowledge mapping of the present invention, querying method.
Specific embodiment
Below in conjunction with drawings and examples, to the building of sciemtifec and technical sphere knowledge mapping of the invention, querying method and it is
System is described in further detail.
Embodiment
As shown in Figure 1, the building of sciemtifec and technical sphere knowledge mapping of the invention, querying method, user is in relational database
Data combed, comb out the object to be defined, relationship, attribute information, object, relationship, category defined by data modeling
Property information, and define relational database tables of data to object, relationship, attribute map information；By relational database data pick-up
Object, relationship, attribute instance storage are converted into chart database, is matched by attribute value and carries out map query analysis.Specifically
The following steps are included:
S1, combing data: the data in relational database are combed.
Scientific research personnel, scientific research project, science research output, R&D institution's object are combed out, combs out project output, mechanism produces
Out, mechanism project, facility personnel's relationship, while combing out every kind of object, the attribute information that relationship includes.
S2, data modeling: user is defined object, attribute, relational model according to hierarchical relationship.
Defining object includes scientific research personnel, scientific research project, science research output, R&D institution, includes multiple categories in every kind of object
Property；Definition relationship include direct relation definition and indirect relation definition, direct relation definition include project output, mechanism output,
Mechanism project, facility personnel include multiple attributes in every kind of relationship；Indirect relation definition, including same project, same to mechanism, same to production
It out, include multiple attribute design conditions in every kind of indirect relation.
Wherein, defining object includes multiple attributes, such as scientific research personnel's object may include expert's name, expert's ranking belongs to
Property information.Each object corresponds to a vertexLabel in JanusGraph, and each attribute corresponds to one in JanusGraph
A PropertyKey.
Definition relationship include multiple attributes, as facility personnel's relationship may include organization, personnel's title, whether
The attribute informations such as duty, hiring date, leaving date.Each relationship corresponds to an edgeLabel in JanusGraph.
Indirect relation, which defines, to be calculated according to direct relation by the attribute conditions inquiry of setting.
S3, tables of data is defined in relational database to object, the mapping of relationship, attribute.
It defines tables of data in relational database and defines the field of tables of data in relational database to object, the mapping of relationship
Whether mapping and attribute to attribute will index.
S4, spectrum data import: by the persistence architecture in relational database in pairs as, relationship, attribute instance data
It stores in chart database.
The data extracted from relational database mapping relations according to defined in step S3 carry out data conversion, pass through
Apache NiFi is by the persistence architecture in relational database in pairs as, relationship, attribute instance data storage is to chart database
In.
The data extracted from relational database mapping relations according to defined in step S3 carry out data conversion, wherein
JanusGraph uses HBase as storage rear end, uses ElasticSearch as index rear end.Specific step is as follows:
1) data source is selected, the tables of data for having built mapping under data source in step s 2 is selected；
2) data guiding flow is created by pulling mode in Apache NiFi, wherein what is defined in step s3 reflects
It penetrates and provides Data Conversion Service in a manner of Restful for NiFi；
3) Booting sequence extracts data into chart database.
S5, progress map query analysis is matched by attribute value.
It carries out being based on Gremlin figure query language when map query analysis, passes through scientific research personnel's name or multiple attribute values
Matched mode inquires scientific research personnel, and then inquires the relation information of specific scientific research personnel.Indirect relation inquiry, need by
Attribute of a relation condition is connect to be calculated.Wherein, scientific research project, scientific research institution's map query analysis and the inquiry point of scientific research personnel's map
Phase separation is same.
The building of sciemtifec and technical sphere knowledge mapping of the invention, inquiry system, including combing data module, data modeling mould
Mapping definition module, spectrum data import modul and the map query analysis module of block, object, relationship and attribute.
Combing data module is for combing the data in relational database.
Combing data module combs out scientific research personnel, scientific research project, science research output, R&D institution's object, combs out project
Output, mechanism output, mechanism project, facility personnel's relationship, while combing out every kind of object, the attribute information that relationship includes.
Data modeling module is defined object, attribute, relational model according to hierarchical relationship for user.
Defining object includes scientific research personnel, scientific research project, science research output, R&D institution, includes multiple categories in every kind of object
Property；Definition relationship include direct relation definition and indirect relation definition, direct relation definition include project output, mechanism output,
Mechanism project, facility personnel include multiple attributes in every kind of relationship；Indirect relation definition, including same project, same to mechanism, same to production
It out, include multiple attribute design conditions in every kind of indirect relation.
The mapping definition module of object, relationship and attribute is for defining in relational database tables of data to object, relationship, category
The mapping of property.
Spectrum data import modul be used for by the persistence architecture in relational database in pairs as, relationship, attribute instance
Data are stored into chart database.
Map query analysis module, which is used to match by attribute value, carries out map query analysis.Carry out map query analysis
When, it is based on Gremlin figure query language, scientific research personnel is inquired by scientific research personnel's name or the matched mode of multiple attribute values,
And then inquire the relation information of specific scientific research personnel.
Embodiment described above, the only present invention more preferably specific embodiment, those skilled in the art is at this
The usual variations and alternatives carried out within the scope of inventive technique scheme should be all included within the scope of the present invention.
Claims (10)
1. a kind of building of sciemtifec and technical sphere knowledge mapping, querying method, it is characterised in that: user is to relational database in this method
In data combed, comb out the object to be defined, relationship, attribute information, by data modeling define object, relationship,
Attribute information, and define relational database tables of data to object, relationship, attribute map information；Relational database data are taken out
It takes and is converted to object, relationship, attribute instance storage into chart database, matched by attribute value and carry out map query analysis.
2. the building of sciemtifec and technical sphere knowledge mapping according to claim 1, querying method, it is characterised in that: this method tool
Body the following steps are included:
S1, combing data: the data in relational database are combed；
S2, data modeling: user is defined object, attribute, relational model according to hierarchical relationship；
S3, tables of data is defined in relational database to object, the mapping of relationship, attribute；
S4, spectrum data import: by the persistence architecture in relational database in pairs as, relationship, attribute instance data store
Into chart database；
S5, progress map query analysis is matched by attribute value.
3. the building of sciemtifec and technical sphere knowledge mapping according to claim 2, querying method, it is characterised in that: in step S1,
Scientific research personnel, scientific research project, science research output, R&D institution's object are combed out, project output, mechanism output, mechanism item are combed out
Mesh, facility personnel's relationship, while combing out every kind of object, the attribute information that relationship includes.
4. the building of sciemtifec and technical sphere knowledge mapping according to claim 3, querying method, it is characterised in that: in step S2,
Defining object includes scientific research personnel, scientific research project, science research output, R&D institution, includes multiple attributes in every kind of object；Definition is closed
System include direct relation definition and indirect relation definition, direct relation definition include project output, mechanism output, mechanism project,
Facility personnel includes multiple attributes in every kind of relationship；Indirect relation definition, including same project, same to mechanism, same to output, every inter-species
It connects in relationship comprising multiple attribute design conditions.
5. the building of sciemtifec and technical sphere knowledge mapping according to claim 4, querying method, it is characterised in that: in step S3,
Defining tables of data in relational database, to object, the mapping of relationship, the field for defining tables of data in relational database arrives attribute
Whether mapping and attribute will index.
6. the building of sciemtifec and technical sphere knowledge mapping according to claim 5, querying method, it is characterised in that: in step S4,
The data extracted from relational database mapping relations according to defined in step S3 carry out data conversion, pass through Apache
NiFi by the persistence architecture in relational database in pairs as, relationship, attribute instance data storage into chart database.
7. the building of sciemtifec and technical sphere knowledge mapping according to claim 6, querying method, it is characterised in that: in step S5
It carries out being based on Gremlin figure query language when map query analysis, passes through scientific research personnel's name or the matched side of multiple attribute values
Formula inquires scientific research personnel, and then inquires the relation information of specific scientific research personnel.
8. a kind of building of sciemtifec and technical sphere knowledge mapping, inquiry system, it is characterised in that: the system include combing data module,
Data modeling module, object, relationship and attribute mapping definition module, spectrum data import modul and map query analysis mould
Block；
Combing data module is for combing the data in relational database；
Data modeling module is defined object, attribute, relational model according to hierarchical relationship for user；
The mapping definition module of object, relationship and attribute is used to defining tables of data in relational database to object, relationship, attribute
Mapping；
Spectrum data import modul be used for by the persistence architecture in relational database in pairs as, relationship, attribute instance data
It stores in chart database；
Map query analysis module, which is used to match by attribute value, carries out map query analysis.
9. the building of sciemtifec and technical sphere knowledge mapping according to claim 8, inquiry system, it is characterised in that: the combing
Data module combs out scientific research personnel, scientific research project, science research output, R&D institution's object, combs out project output, mechanism produces
Out, mechanism project, facility personnel's relationship, while combing out every kind of object, the attribute information that relationship includes.
10. the building of sciemtifec and technical sphere knowledge mapping according to claim 9, inquiry system, it is characterised in that: map inquiry
When analysis module carries out map query analysis, it is based on Gremlin figure query language, passes through scientific research personnel's name or multiple attribute values
Matched mode inquires scientific research personnel, and then inquires the relation information of specific scientific research personnel.
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