CN109189947A - A kind of mobile data knowledge mapping method for auto constructing based on relational database - Google Patents
A kind of mobile data knowledge mapping method for auto constructing based on relational database Download PDFInfo
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Abstract
A kind of mobile data knowledge mapping method for auto constructing based on relational database, the present invention relates to data intelligence processing technical fields；The concept level framework of generation knowledge mapping: the extraction of concept element in mobile data knowledge mapping is schemed by mobile data ER；Relationship elements generate in mobile data knowledge mapping；Property element generates in mobile data knowledge mapping；Instance elements in dynamic data knowledge map are claimed to extract；Knowledge mapping generates.The knowledge mapping of the data can not only be automatically formed from relational database, moreover it can be used to which the knowledge mapping of mobile data models, and can be also used for the knowledge mapping building of mobile data, and the conversion for the relation data in other fields to knowledge mapping.
Description
Technical field
The present invention relates to data intelligence processing technical fields, and in particular to a kind of mobile data based on relational database is known
Know map method for auto constructing.
Background technique
Explosive growth of the big data in terms of capacity, diversity and high speedup test modern enterprise data modeling,
Data storage, data processing and analysis ability.Especially in terms of data modeling, if none healthy and strong model, can make
Data deficiency model basis is obtained, the effect of big data cannot be given full play to.Association between still more current data is even more important, such as
Fruit lacks connection, will form information island, so that being confined in a certain small region when being handled big data and being analyzed
And more valuable information cannot be excavated.
For commmunication company's enterprise-level big data, the data resource of each provincial company, specialized company is contained, is specifically had
Customer data, account data etc..The data volume of these data is big and relationship is complex.Under normal conditions, this kind of data
Modeling uses relational data model, that is, uses relational database, in modeling process, firstly, the integrality in order to guarantee data,
Unnecessary data redundancy is reduced, the extension of database is conducive to, strictly to be designed in accordance with the paradigm theory of data；Secondly,
In order to make to associate between data, using common semantic data modeling method, i.e. entity-relation method, entity-is constructed
Relational graph (ER figure), but in place of its Shortcomings: first is that due to relating to a large amount of data, modeling process is extremely complex；Second is that
Although ER figure uses semantic data modeling method, this modeling pattern, the corresponding use of model after especially establishing
Tables of data amount it is big, and be associated between a large amount of table and table, so that the access efficiency of data is lower, it is important to rule cannot be formed
Then, existing knowledge cannot be formed new knowledge according to regular, then made inferences, to obtain implicit valuable
Information.
The method for carrying out effective expression to knowledge at present is knowledge mapping.It is with figure that knowledge mapping, which is by real world,
Mode shows the relationship between concept and concept, not only helps people to recognize objective world, but also define realization for computer
Data model, the complicated representation of knowledge is reticulated structure by it, complex query can be carried out to data, can by rule from
Implicit information is excavated in associated data, discloses the active development rule in knowledge and field, is knowledge and technology
Research provides practical, valuable reference.By taking mobile data as an example, customer data is made into knowledge mapping, by rule-based reasoning,
Accurately personalized service can be done for client.
It is found after analyzing mobile data, such data structured program is preferable, and most of data are with relationship
The form of database stores, although by ER figure can some of data associate, first is that more because of tables of data, and
It is more to be that watch chain is taken over, so that in inquiry and data processing, efficiency is lower, more importantly rule and inference machine cannot be borrowed
The knowledge excavation that data are carried out with depth is made, i.e., data cannot be made to flow, the effect of data cannot be given full play to.
Therefore, the data in relational database are built into the knowledge mapping of such data automatically, data are associated,
Convenient for borrow knowledge mapping in inquiry and inference mechanism data are analyzed and are excavated.Currently, using relational database come
Indicate and stored knowledge map method it is relatively more, will such as be used for knowledge mapping expression RDF and OWL language by level, it is vertical
The storage of straight and hybrid mode is into relational database.But data are constructed to the side of knowledge mapping automatically from relational database
Method does not occur.It is relational model of database storage due to there is a large amount of data in big data, so relation data is automatic
Building knowledge mapping can make the data volume of knowledge mapping increase, and be more advantageous to it and provide valuable information.
Summary of the invention
In view of the defects and deficiencies of the prior art, the present invention intends to provide a kind of structure is simple, design rationally, make
It, can not only be from relational database certainly with a kind of convenient mobile data knowledge mapping method for auto constructing based on relational database
The dynamic knowledge mapping for forming the data, moreover it can be used to which the knowledge mapping of mobile data models, and can be also used for mobile data
Knowledge mapping building, and the conversion for the relation data in other fields to knowledge mapping.
To achieve the above object, the technical solution adopted by the present invention is that: its steps is as follows:
1, the concept level framework of generation knowledge mapping is schemed by mobile data ER:
The content in knowledge mapping that the concept level framework of knowledge mapping refers to is related to abstract concept, relationship and attribute, is to whole
The mapping of ER framework in a relational database to knowledge mapping framework, comprising:
1.1, in mobile data knowledge mapping concept element extraction:
Concept is abstracted to data, entity of the concept element in knowledge mapping in relation data ER figure, relationship, category
Property, i.e., entity, relationship and attribute are seen to the concept in knowledge mapping as, in order to illustrate more clearly of knowledge mapping building side
Formula, the concept mapped entities in knowledge mapping are known as entitative concept, and attribute is mapped to the concept in knowledge mapping and is known as belonging to
Property concept, concept of the relationship map into knowledge mapping are known as relation concept；
1.2, relationship elements generate in mobile data knowledge mapping:
In step 1.1, the relationship in ER figure is counted as the relation concept in knowledge mapping, the concept have oneself attribute and
Relationship, such as Fig. 1, R are relationship in ER figure, and attribute Ra is mapped in knowledge mapping, then for R be relation concept and its
Attribute Ra(is attributive concept), the attribute definition between R and Ra is carried out by method in step 1.3.
Since the relationship in ER figure sees concept as in knowledge mapping, therefore, it is necessary to generate new relationship for establishing
Association between concept, specific production stage are as follows: as shown in Figure 1, since the relationship in ER figure is usually an operation, this
It is then an Event Concepts, therefore concept corresponding to the both ends of the operation is to include with Event Concepts after concept is regarded in operation as
Relationship, that is, R include E1 and R include E2；Again by the concept E1 attribute Ra and entity with R relationship in former ER figure respectively
The attribute of E2 establishes new relationship, and relationship name is set as " E1Ra ", " E1Ea2 "；
1.3, property element generates in mobile data knowledge mapping:
Attribute in ER figure is mapped in knowledge mapping, becomes attributive concept.As shown in Figure 1, E1 has attribute Ea1, according to step
Method in 1.1 illustrates that Ea1 is a concept, and Ea1 is the attribute of client, that is to say, that should between E1 and Ea1
There is a relation on attributes, and this relation on attributes is to lie in ER figure, but need to show presence in knowledge mapping, so
An attribute must be generated, the specific steps are that: add " a having " word before Property Name, as " has Ea1 ", this name
Mode is conducive to the automatic building of knowledge mapping, and what attribute was indicated in knowledge mapping is pass between entitative concept and attributive concept
Connection；
2, instance elements in dynamic data knowledge map is claimed to extract:
Example is the concrete concept in knowledge mapping, and such as one is the client of " U1 ", and Ea1 is " 001 ".Wherein U1 and " 001 " are equal
It is the example of concept " E1 " and concept " Ea1 ".And these examples are in relational database, therefore instance elements extraction be by
Data in relational database correspond to the process of said concepts.Data of the example in relational database, because ER schemes
Middle entity, relationship and attribute have specific data corresponding in the database.It is mapped to after forming concept in knowledge mapping, this
A little data are then the examples of concept, and specific extraction step is as follows: the specific client in every a line being named, that is, assuming that first
Behavior " U1 ", Ea1 are " 001 ", and the second behavior " U2 ", Ea1 is " 002 ", and by customer name " U1 ", " U2 " this is general respectively as E1
The example of thought, and increase the relationship of E1 and U1 and U2, relationship is set as " having example "；Similarly by " 001 " and " 002 " conduct " Ea1 "
Two examples of this concept, and establish " having example " relationship；
3, knowledge mapping generates:
The concept level framework of knowledge mapping is constructed respectively in completion step 1 and step 2, and concept, relationship and attribute are carried out
After mapping, in knowledge based map example is to the relationship of concept and the inheritance of attribute, the data of marriage relation database
Form knowledge mapping；Specific generation step is as follows: because the attribute of concept has inheritance in knowledge mapping, for example,
The attribute of concept is then inherited, e.g., " E1 has Ea1 Ea1 " indicates between concept " E1 " and concept " Ea1 " there is " having Ea1 " to belong to
Property；And example can inherit the attribute of concept, so the example " U1 ", " U2 " etc. under concept " E1 " also have " having Ea1 " attribute,
And correspond respectively to " 001 " and " 002 " example of concept " Ea1 " below；According to this rule, by the concept extracted,
Example gets up to form knowledge mapping by attribute and relationship respectively.
After adopting the above method, the invention has the following beneficial effects: a kind of movement based on relational database of the present invention
Data knowledge map method for auto constructing can not only automatically form the knowledge mapping of the data from relational database, moreover it can be used to
The knowledge mapping of mobile data models, and can be also used for the knowledge mapping building of mobile data, and is used for the pass in other fields
Coefficient is according to the conversion for arriving knowledge mapping.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, 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
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 is ER figure and knowledge mapping mapping graph of the invention.
Fig. 2 is the knowledge mapping that embodiment is constructed based on mobile enterprise data.
Specific embodiment
The present invention will be further described below with reference to the drawings.
Referring to as shown in Figure 1, present embodiment the technical solution adopted is that: its steps is as follows:
1, the concept level framework of generation knowledge mapping is schemed by mobile data ER:
The content in knowledge mapping that the concept level framework of knowledge mapping refers to is related to abstract concept, relationship and attribute, is to whole
The mapping of ER framework in a relational database to knowledge mapping framework, comprising:
1.1, in mobile data knowledge mapping concept element extraction:
Concept is abstracted to data, entity of the concept element in knowledge mapping in relation data ER figure, relationship, category
Property, i.e., entity, relationship and attribute are seen to the concept in knowledge mapping as, in order to illustrate more clearly of knowledge mapping building side
Formula, the concept mapped entities in knowledge mapping are known as entitative concept, and attribute is mapped to the concept in knowledge mapping and is known as belonging to
Property concept, concept of the relationship map into knowledge mapping are known as relation concept；
1.2, relationship elements generate in mobile data knowledge mapping:
In step 1.1, the relationship in ER figure is counted as the relation concept in knowledge mapping, the concept have oneself attribute and
Relationship, such as Fig. 1, R are relationship in ER figure, and attribute Ra is mapped in knowledge mapping, then for R be relation concept and its
Attribute Ra(is attributive concept), the attribute definition between R and Ra is pressed method in 3) and is carried out.
Since the relationship in ER figure sees concept as in knowledge mapping, therefore, it is necessary to generate new relationship for establishing
Association between concept, specific production stage are as follows: as shown in Figure 1, since the relationship in ER figure is usually an operation, this
It is then an Event Concepts, therefore concept corresponding to the both ends of the operation is to include with Event Concepts after concept is regarded in operation as
Relationship, that is, R include E1 and R include E2；Again by the concept E1 attribute Ra and entity with R relationship in former ER figure respectively
The attribute of E2 establishes new relationship, and relationship name is set as " E1Ra ", " E1Ea2 "；
1.3, property element generates in mobile data knowledge mapping:
Attribute in ER figure is mapped in knowledge mapping, becomes attributive concept.As shown in Figure 1, E1 has attribute Ea1, according to step
Method in 1.1 illustrates that Ea1 is a concept, and Ea1 is the attribute of client, that is to say, that should between E1 and Ea1
There is a relation on attributes, and this relation on attributes is to lie in ER figure, but need to show presence in knowledge mapping, so
An attribute must be generated, the specific steps are that: add " a having " word before Property Name, as " has Ea1 ", this name
Mode is conducive to the automatic building of knowledge mapping, and what attribute was indicated in knowledge mapping is pass between entitative concept and attributive concept
Connection；
2, instance elements in dynamic data knowledge map is claimed to extract:
Example is the concrete concept in knowledge mapping, and such as one is the client of " U1 ", and Ea1 is " 001 ".Wherein U1 and " 001 " are equal
It is the example of concept " E1 " and concept " Ea1 ".And these examples are in relational database, therefore instance elements extraction be by
Data in relational database correspond to the process of said concepts.Data of the example in relational database, because ER schemes
Middle entity, relationship and attribute have specific data corresponding in the database.It is mapped to after forming concept in knowledge mapping, this
A little data are then the examples of concept, and specific extraction step is as follows: the specific client in every a line being named, that is, assuming that first
Behavior " U1 ", Ea1 are " 001 ", and the second behavior " U2 ", Ea1 is " 002 ", and by customer name " U1 ", " U2 " this is general respectively as E1
The example of thought, and increase the relationship of E1 and U1 and U2, relationship is set as " having example "；Similarly by " 001 " and " 002 " conduct " Ea1 "
Two examples of this concept, and establish " having example " relationship；
3, knowledge mapping generates:
The concept level framework of knowledge mapping is constructed respectively in completion step 1 and step 2, and concept, relationship and attribute are carried out
After mapping, in knowledge based map example is to the relationship of concept and the inheritance of attribute, the data of marriage relation database
Form knowledge mapping；Specific generation step is as follows: because the attribute of concept has inheritance in knowledge mapping, for example,
The attribute of concept is then inherited, e.g., " E1 has Ea1 Ea1 " indicates between concept " E1 " and concept " Ea1 " there is " having Ea1 " to belong to
Property；And example can inherit the attribute of concept, so the example " U1 ", " U2 " etc. under concept " E1 " also have " having Ea1 " attribute,
And correspond respectively to " 001 " and " 002 " example of concept " Ea1 " below；According to this rule, by the concept extracted,
Example gets up to form knowledge mapping by attribute and relationship respectively.
Present embodiment the utility model has the advantages that
1, extraction and generation that can automatically by the data in relational database Jing Guo concept, example, relationship and attribute, then root
The association of concept and example is set up based on relationship and attribute according to the property of knowledge mapping, is relational data model and knowledge mapping
Between establish tie；
2, it is more than the knowledge mapping that can automatically form the data from relational database, can be also used for the knowledge of mobile data
Map modeling, i.e., through the invention in knowledge mapping construction method, establish the knowledge mapping model of data；
3, there is universality, that is, the knowledge mapping building that not only can be used for mobile data can be also used for other fields
Conversion of the relation data to knowledge mapping.
Embodiment:
Referring to Fig. 2, in the present embodiment by taking the relation data in client, product and the client's ordering products in mobile data as an example
Carry out declarative knowledge map construction process:
1, the concept level framework of generation knowledge mapping is schemed by mobile data ER:
1.1, in mobile data knowledge mapping concept element extraction:
Entity, relationship and attribute in ER figure on the right side of Fig. 2 is mapped to the concept in knowledge mapping, for the ease of distinguishing, respectively
It is called entitative concept, relation concept and attributive concept；Such as, " client " and " product " belongs to entitative concept；" order " relation belonging to
Concept；" customer ID ", " customer status ", " product number ", " order time " etc. belong to attributive concept；
1.2, relationship elements generate in mobile data knowledge mapping:
There are some relationships to lie in figure in ER figure, but must show storage in knowledge mapping, so needing to give birth to
At some relationships；Since the relationship map in ER figure to knowledge mapping is relation concept, i.e., see the relationship as an event,
In Fig. 2, " order " is a relation concept, and essence is an Event Concepts in knowledge mapping, so " order " and " client ",
" product " is inclusion relation；
Product is had subscribed for client, then " client " and " order time ", " product number ", " product uses region " etc. there is
The relationships such as " client's order time ", " client's ordering products number ", " client's ordering products act on region "；
1.3, property element generates in mobile data knowledge mapping:
Similar with the generation of relationship elements, attribute is display presence in ER figure, but since attribute is mapped in knowledge mapping
After become attributive concept, be no longer attribute, it is therefore desirable to set up and be associated with attributive concept by generating attribute；Shown in Fig. 2,
" client " and " customer ID " establishes the attribute of " having customer ID ", and " product " and " product number ", which establishes, " has product volume
Number " attribute, " order " and " order time " also establish the attribute of " having the order time "；
2, instance elements in dynamic data knowledge map is claimed to extract:
Data of the example in relational database, because entity, relationship and attribute have specifically in the database in ER figure
Data it is corresponding；It is mapped to after forming concept in knowledge mapping, these data are then the examples of concept.Such as " Zhang San ", " Lee
Four " be the example of client；" 2008001 ", " 2008002 " are the examples etc. of customer ID；
3, the generation of knowledge mapping:
Step 1 and step 2 construct the concept level framework of knowledge mapping respectively, and are mapped concept, relationship and attribute,
The content of the step be in knowledge based map example to the relationship of concept and the inheritance of attribute, the number of marriage relation database
According to formation knowledge mapping.In Fig. 2, example " Li Si " inherits " client's order time " relationship of concept " client ", " orders with concept
The example " in March, 2008 " of purchase time " establishes association.Similarly establish " Li Si client's ordering products number p001 " and
The data correlation of " Li Si client's ordering products use region Taiyuan ", i.e. " product number that Li Si orders is p001 ", " Li Si
The product of order is Taiyuan using domain ".
The above is only used to illustrate the technical scheme of the present invention and not to limit it, and those of ordinary skill in the art are to this hair
The other modifications or equivalent replacement that bright technical solution is made, as long as it does not depart from the spirit and scope of the technical scheme of the present invention,
It is intended to be within the scope of the claims of the invention.
Claims (3)
1. a kind of mobile data knowledge mapping method for auto constructing based on relational database, it is characterised in that: its steps is such as
Under:
(1), the concept level framework of generation knowledge mapping is schemed by mobile data ER:
(1.1), in mobile data knowledge mapping concept element extraction:
Concept is abstracted to data, entity of the concept element in knowledge mapping in relation data ER figure, relationship, category
Property, the concept mapped entities in knowledge mapping is known as entitative concept, and attribute is mapped to the concept in knowledge mapping and is known as belonging to
Property concept, concept of the relationship map into knowledge mapping are known as relation concept；
(1.2), relationship elements generate in mobile data knowledge mapping:
In step 1.1, the relationship in ER figure is counted as the relation concept in knowledge mapping, the concept have oneself attribute and
Relationship, R are relationship in ER figure, and attribute Ra is mapped in knowledge mapping, then are relation concept and its attribute Ra for R,
Attribute definition between R and Ra is carried out by method in step 1.3；Since the relationship in ER figure sees concept as in knowledge mapping,
Therefore, it is necessary to generate new relationship for establishing the association between concept, specific production stage is as follows:
It is then an Event Concepts after which is regarded as concept, therefore should since the relationship in ER figure is usually an operation
Concept corresponding to the both ends of operation and Event Concepts are the relationships for including, that is, R includes that E1 and R includes E2；Again by concept E1
Establish new relationship with the attribute of the attribute Ra and entity E2 of R relationship in former ER figure respectively, relationship name be set as " E1Ra ",
"E1Ea2"；
(1.3), property element generates in mobile data knowledge mapping:
Attribute in ER figure is mapped in knowledge mapping, becomes attributive concept, and E1 has attribute Ea1, according to the side in step (1.1)
Method illustrates that Ea1 is a concept, and Ea1 is the attribute of client, there is a relation on attributes between E1 and Ea1, and this
Relation on attributes is to lie in ER figure, but need to show presence in knowledge mapping, so an attribute must be generated, is had
Body step are as follows:
Add " a having " word before Property Name, as " has Ea1 "；
2. instance elements in dynamic data knowledge map is claimed to extract:
Data of the example in relational database, because entity, relationship and attribute have specifically in the database in ER figure
Data it is corresponding.It is mapped to after forming concept in knowledge mapping, these data are then the examples of concept, and specific extraction step is such as
Under:
By the specific client name in every a line, that is, assuming that the first behavior " U1 ", Ea1 is " 001 ", the second behavior " U2 ",
Ea1 is " 002 ", and by customer name " U1 ", " U2 " and increases the relationship of E1 and U1 and U2 respectively as the example of this concept of E1,
Relationship is set as " having example "；Similarly two examples by " 001 " and " 002 " as " Ea1 " this concept, and establish " having example "
Relationship；
3. knowledge mapping generates:
The concept level framework of knowledge mapping is constructed respectively in completion step (1) and step (2), and to concept, relationship and attribute
After being mapped, in knowledge based map example to the relationship of concept and the inheritance of attribute, marriage relation database
Data form knowledge mapping；Specific generation step is as follows:
Example can inherit the attribute of concept, so example " U1 ", " U2 " under concept " E1 " etc. also has " having Ea1 " attribute,
And correspond respectively to " 001 " and " 002 " example of concept " Ea1 " below；According to this rule, by the concept extracted,
Example gets up to form knowledge mapping by attribute and relationship respectively.
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Application publication date: 20190111 |