CN108959358B - A kind of end-user listening data access method and system based on ontology model - Google Patents
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Abstract
The present invention relates to a kind of end-user listening data access method and system based on ontology model.This method comprises: 1) the ontology inference rule according to inquiry meta-model and based on the inquiry meta-model, the ontology model to be inquired user pre-process, " inwardly shared " relationship and " shared outward " relationship therein are inferred；2) Ontology Query SPARQL sentence is converted user input into；3) SQL statement is generated according to the Ontology Query SPARQL sentence, by carrying out the access and inquiry that SQL query realizes data to the ontology model.The present invention makes full use of semantic primitive and the inferential capability of ontology model to optimize the query construction process of terminal user, user is helped to be detached from the actual storage Mode detail of database, complete support is provided to the classified statistic demand of terminal user simultaneously, the deficiency for having filled up existing related work has stronger system availability and ability to express.
Description
Technical field
The present invention relates to a kind of data access method and data access systems, belong to data integration field, and in particular to one
End-user listening data access method and data access system of the kind based on ontology model.
Background technique
In big data era, enterprises have accumulated largely data relevant to business and tissue.As company manager,
If these data effectively can be grasped and be analyzed, business decision and intellectual analysis can be preferably done, this is for enterprise competitiveness
It is particularly significant with the promotion of profitability.However inside most enterprises, the access and acquisition of data are still until nowadays
Influence the important bottleneck that enterprise creates value.This bottleneck is mainly reflected in computer professional and company manager or business
Wide gap between analyst: usual computer professional has manipulation data base management system and writes data query language
Ability, but do not know about the data analysis requirements of enterprise practical；And management of enterprise operation person or business analyst are familiar with enterprise's industry
Business, grasps the knowledge of management of enterprise operation, to be also aware that the demand of data analysis, but lacks necessary profession IT technical ability,
Management system can not be directly accessed the database to obtain required data.The present invention claims the latter's this kind to grasp data access demand,
But the user for not having computer major technical ability is terminal user.
For the data access problem of terminal user, there are two types of traditional solutions, and a kind of scheme is in data base set
Built-in some common data retrieval sentences or data acquisition, analysis template, terminal user can be by simply interactive in system
" key " accesses data.This Project expressing ability is limited, with the development of business, the new data access demand of terminal user will not
It is disconnected to emerge in large numbers, it can not exhaustion in advance.Another scheme is that computer professional is allowed to intervene, and is linked up with terminal user, is understood
Its data access demand, then write corresponding query statement and go to obtain data.The shortcomings that this scheme includes 2 aspects: firstly,
There are communication disorders between terminal user and computer professional, the data access demand of user is difficult to accurately express；Secondly,
The participation for introducing intermediate role leads to the excessive cycle of entire workflow, inefficiency when changes in demand is frequent.
Field concept or query statement are expressed as visually by visual query system (VQS:Visual Query System)
Change element, user is allowed to manipulate visualized elements by interactive interface to complete the construction of query statement.Data based on ontology
Access technique (OBDA:Ontology Based Data Access) is using ontology (Ontology) as terminal user and data
Medium between the system of library.However the related work of existing VQS system and OBDA technology based on ontology can't be well
Solve the problems, such as the data access of terminal user, main cause has two o'clock:
1, database actual storage mode is directly exposed in the query construction stage, does not make full use of the reasoning of ontology model
Ability, leads to terminal user's demand beyond expression of words and construction inquiry, and system availability is insufficient.
2, support all is not provided to the classified statistic demand of terminal user in query construction stage and inquiry conversion stage,
Cause the applicable scene domain of scheme smaller, system expression scarce capacity.
Summary of the invention
The present invention is proposed for the data access demand of terminal user and the defect of existing the relevant technologies " based on inquiry
The ontology inference rule of meta-model " and " Ontology Query SPARQL sentence construction algorithm ", and using the two as technical foundation design with
Realize a kind of end-user listening data access method and system based on ontology model.
Above-mentioned technical problem of the invention is mainly to be addressed by following technical proposals:
A kind of end-user listening data access method based on ontology model, comprising the following steps:
1) the ontology inference rule according to inquiry meta-model and based on the inquiry meta-model, to the ontology to be inquired of user
Model is pre-processed, and " inwardly shared " relationship and " shared outward " relationship therein are inferred；
2) Ontology Query SPARQL sentence is converted user input into；
3) SQL statement is generated according to the Ontology Query SPARQL sentence, by carrying out SQL query to the ontology model
Realize the access and inquiry of data.
Further, the inquiry meta-model is related to User behavior, unrelated with specific area, using OWL Ontology Language
It is expressed, including " querying attributes ", " query object " and " inquiry index ", wherein " querying attributes " are closed for describing user
The database table entry of the heart, database table belonging to " querying attributes " are then abstracted as " query object ", " querying attributes " and " inquiry pair
As " between in addition to physics " subordinate " association, also have " inwardly shared " relationship in logic by ontology inference regular expression；
For polymerizable one kind " querying attributes ", " inquiry index " concept is used to express its statistical value, " inquiry index " with
There is " shared outward " relationship in logic by ontology inference regular expression between " query object ".
Further, the ontology inference rule operates on the inquiry meta-model, for expressing " shared outward "
" inwardly shared " described and the derived relation that is not directly present in original bulk model.
It is further, described to convert user input into Ontology Query SPARQL sentence, comprising the following steps:
A) all " query object " and " external key association " relationships between them involved in inquiry are determined；
B) " external key association " relationship described in utilizing, generates chart-pattern part most crucial in Ontology Query sentence；
C) in " the inquiry index " in traverse user input, " output attribute " and " packet attributes " Lai Shengcheng SPARQL sentence
Rest part, SPARQL each section is finally assembled into whole character string and is returned.
Further, step a) uses ontology route searching subalgorithm, inputs user_input and ontology model with user
Object ont_mgr is input, exports the list of " external key association " relationship to inquire between related all " query objects "；
The ontology route searching subalgorithm includes: " query object " group extracted all in-degrees from " query object " digraph and be 0
At origin object set, origin object set is next traversed, each " query object " is started using it as starting point with range
First search order traversal " query object " digraph checks any " query object " being accessed in ergodic process
Whether it with any " querying attributes " in user's input has " subordinate " relationship, by " external key association " pass of the secondary traversal if having
The results list is added in system.
Further, step b) constructs subalgorithm using chart-pattern, inputs user_input, ontology model object with user
Ont_mgr is input with " external key is associated with " relation list path_list, exports the chart-pattern part WHERE for Ontology Query, and
Variable i d used in chart-pattern is write back user to input in the output attribute and packet attributes of user_input, for constructing
Other parts in SPARQL sentence.
Further, using user's Visual Intelligent Interface Model, in the interface comprising inquiry metric tree, query object figure,
Querying attributes table, inquiry 4 child windows of the list of elements, wherein inquiry metric tree window is responsible for showing " inquiry index "；Query object
Figure window is responsible for showing " query object " with diagram form；Querying attributes table window is responsible for showing " the inquiry pair clicked with user
As " relevant " querying attributes "；Inquiry list of elements window is responsible for recording the selected element of user, for " looking into selected by user
Inquiry index " can click the polymerization methods of selective goal.
Further, in user's Visual Intelligent Interface Model interface, user passes through selection inquiry index and query object
The polymerization that attribute is realized in cooperation is carried out, selects querying attributes to realize output, screening and the grouping of attribute, and it is defeated to be finally completed user
Enter.
Further, user's input is integrated into five-tuple (mname, ind, gprop, oprop, fcond), wherein
Mname is the model name inquired, and ind is index set selected by user, and gprop is packet attributes set selected by user,
Oprop is output attribute set selected by user, and fcond is screening conditions set selected by user.
A kind of end-user listening data access system based on ontology model comprising:
Ontology model preprocessing module, is responsible for according to inquiry meta-model and the ontology inference based on the inquiry meta-model is advised
Then, the ontology model to be inquired user pre-processes, and infers " inwardly shared " relationship therein and " shared outward " closes
System；
Ontology Query SPARQL sentence constructing module is responsible for converting user input into Ontology Query SPARQL sentence；
Enquiry module is responsible for generating SQL statement according to the Ontology Query SPARQL sentence, by the ontology model
Carry out access and inquiry that SQL query realizes data.
Terminal user of the present invention towards not Basis of Computer Engineering, serves as terminal user and data base set using ontology model
The intermediary between medium and heterogeneous database mode between system has got through complete terminal under the support of ontology model and has used
User data browsing process.The present invention makes full use of semantic primitive and the inferential capability of ontology model to optimize the inquiry of terminal user
Process is constructed, helps user to be detached from the actual storage Mode detail of database, while mentioning to the classified statistic demand of terminal user
Complete support has been supplied, the deficiency of existing related work has been filled up, has had stronger system availability and ability to express.
Detailed description of the invention
Fig. 1 is inquiry meta-model schematic diagram in the present invention.
Fig. 2 is system interaction interface schematic diagram in the present invention.
Fig. 3 is the SPARQL query statement schematic diagram that system generates in the present invention.
Fig. 4 is the schematic diagram for the SQL query statement that system generates in the present invention.
Fig. 5 is the schematic diagram that system visualizes query result in the present invention.
Fig. 6 is database table foreign key relationship schematic diagram.
Specific embodiment
Below by specific embodiments and the drawings, the present invention is described in further details.
1. Ontology Query meta-model
In order to be detached from specific area and specific example general inference rule is arranged, and model is provided to the polymerization of attribute
On support, this method devises inquiry meta-model only related to User behavior, unrelated with specific area first, and Fig. 1 is this
Inquire the schematic diagram of meta-model.The meta-model is still expressed with OWL (Web Ontology Language) Ontology Language.Its
In " querying attributes " concept for describing user's database table entry of concern, such as " brand name ", belonging to " querying attributes "
Database table is then abstracted as " query object " concept, such as " brand ", in addition to physics between " querying attributes " and " query object "
" subordinate " association also has " inwardly shared " relationship in logic by ontology inference regular expression；For polymerizable one kind
" querying attributes " use in model " inquiry index " concept to express its statistical value, if " turnover " is (" actual money "
Statistical value), also there is " being total to outward in logic by ontology inference regular expression between " inquiry index " and " query object "
Enjoy " relationship.
As shown in fig. 6, the major key of Table A and table B are respectively attribute a, d, attribute c is the external key of table B in Table A, this is sentenced directly
For " external key association " relationship connect, indirect " external key association " relationship can be derived similarly.In this relationship, there is inverse external key
The logic subordinate relation in direction and suitable external key direction is referred to as " inwardly shared " and " shared outward " of attribute in the present invention
Relationship.
" external key association " relationship refers to: there are public keywords in two database tables, and the keyword is at one
It is major key in table, this public keyword is known as the external key of another table." inwardly shared " relationship refers to: attribute e
Inverse external key direction logic is subordinated to this relationship of Table A.That is the attribute e in table B can be logically to concept corresponding to Table A
It is described.
" shared outward " relationship refers to: the attribute b after polymerization is subordinated to this pass of table B along external key direction logic
System.That is Table A and table B is because foreign key relationship forms many-one and is associated with, the former can (i.e. external key c) be grouped, if Table A by the latter
The data type of attribute b is numeric type, then Table A is grouped by external key c and applies aggregate function (total, average value, most to attribute b
Big value, minimum value etc.) after, attribute b can logically be described concept corresponding to table B.
2. the ontology inference rule based on inquiry meta-model
Ontology inference rule in the present invention is exactly the inference rule of both relationships of " inwardly shared " and " shared outward ".
The rule is constructed using inquiring the abstract concept in meta-model, abstraction relation and abstract attribute as basic element, operation
On inquiry meta-model, it is mainly used for expressing described in " shared outward " and " inwardly shared " and being not directly present in original
Derived relation in ontology model.These ontology inference rules can help VQS system suitably to shield database to terminal user
Actual storage mode, the model view that reduction terminal user understands naturally, and optimize the query construction process of terminal user.
By taking the utilization of " shared outward " relationship as an example.The following institute of content of the ontology inference rule of its corresponding inquiry oriented
Show, " shared outward " association can be established between " inquiry index " and " query object " by the inference rule:
prefix:<http://www.semanticweb.org/pku/ontologies/catering>
[rule_out_share:
(? i rdfs:subClassOf: inquiry index) (? p rdfs:subClassOf: querying attributes) (? o rdfs:
SubClassOf: query object)
(? ou rdfs:subClassOf: query object) (? i: index source? p) (? p: subordinate? o) (? o: external key association?
ou)
=>
(? i: shared outward? ou)
]
Specifically, the ontology inference rule of above-mentioned " shared outward " relationship is described as follows:
1)? i is a kind of inquiry index,? p is a kind of querying attributes,? o is a kind of query object,? ou is also a kind of inquiry
Object.
2) there are following relationships: index? does i derive from attribute? p, attribute? does is p subordinated to object? o, object? o is closed by external key
It is linked to object? ou.
3) result is inferred: index? i and object? there is " shared outward " relationship between ou.
3. Ontology Query SPARQL sentence construction algorithm
Ontology Query construction algorithm is responsible for converting user input into SPARQL sentence, most crucial group in SPARQL sentence
It is the part chart-pattern WHERE at ingredient, in order to construct chart-pattern, it is necessary first to determine " query objects " all involved in inquiry
And " external key incidence relation " between them, ontology route searching subalgorithm will be used to handle this task.
Ontology route searching subalgorithm inputs user_input and ontology model object ont_mgr (ontology model with user
The content that an ontology model is saved in object is the storage form of ontology model) it is input, it exports as involved in inquiry
The list of " external key association " relationship between all " query objects ", that is, connect the path of these " query objects ".The think of of algorithm
Road is that " query object " composition origin object set that all in-degrees are 0 is extracted from " query object " digraph, following time
Origin object set is gone through, each " query object " is started using it as starting point with the " inquiry pair of breadth first search order traversal
As " digraph, for any " query object " being accessed in ergodic process, check its whether with it is any in user's input
" querying attributes " have " subordinate " relationship, and the results list is added in " external key association " relationship of the secondary traversal if having.
Most crucial constituent is the part chart-pattern WHERE in SPARQL sentence, and this section describes involved by inquiry
Concept, the relationship between concept, the attribute of concept and the screening to attribute, need to be handled at first, chart-pattern construction
Subalgorithm will be used to complete the construction task of chart-pattern part.
" chart-pattern " refers to: this inquires the SPARQL language of the mode for the graph structure that corresponding ontology path is constituted
Speech description.Chart-pattern is described using a plurality of SPARQL triple, referred to as " chart-pattern triple ", triple by two concepts and
Relationship composition between them.Such as triple is (A is a kind of, shop) in " A is a kind of shop ".
Chart-pattern constructs subalgorithm and inputs user_input, ontology model object ont_mgr and " external key is associated with " with user
Relation list path_list is input, exports the chart-pattern part WHERE for Ontology Query, and will become used in chart-pattern
Amount id writes back user and inputs in the output attribute and packet attributes of user_input, for constructing other portions in SPARQL sentence
Point.Wherein, " output attribute " refers to " querying attributes " for needing to use when calculating " inquiry index ", i.e., that " inquires index " comes
Source, " packet attributes ", which refer to, is grouped " querying attributes " of institute's reference when being grouped statistics.The thinking of algorithm is that traversal is " outer first
Key association " relation list path_list is generated accordingly each of to traverse " external key association " relationship and " query object " class
Chart-pattern triple, and record all " query object " lists traversed；Then " query object " list is traversed again, is checked
It is subordinated to " querying attributes " of each " query object " in user's input, generates corresponding chart-pattern ternary for these " querying attributes "
Group, and judge whether to need to add screening conditions；Finally return to the chart-pattern partial character string in Ontology Query.Each " inquiry
Object " and " querying attributes " will all generate unique variable id.
Complete Ontology Query construction process is responsible for by Ontology Query construction algorithm.Ontology Query construction algorithm is defeated with user
Enter user_input and ontology model object ont_mgr for input, exports as SPARQL query statement character string.The thinking of algorithm
It is that ontology route searching subalgorithm searchOntPath is called to obtain " external key association " relation list needed for construction is inquired first
Path_list, then calling figure schema construction subalgorithm genSparqlWhere generates figure most crucial in Ontology Query sentence
Mode part connects " inquiry index ", " output attribute " and " packet attributes " Lai Shengcheng SPARQL language in lower traverse user input
SPARQL each section is finally assembled into whole character string and returned by the rest part in sentence.
4. user's virtual interactive interface designs
In the present invention, the interactive interface of terminal user and system is as shown in Fig. 2, mainly comprising inquiry metric tree in interface
4 (left 1 column), query object figure (left 2 columns), querying attributes table (right 2 columns), the inquiry list of elements (right 1 column) child windows.Wherein look into
Metric tree window is ask to be responsible for showing " inquiry index "；Query object figure window is responsible for showing " query object " with diagram form；Inquiry
Attribute list window is responsible for showing " querying attributes " relevant to " query object " that user is clicked；List of elements window is inquired to be responsible for
The selected element of user is recorded, the polymerization methods of selective goal can be clicked for " inquiry index " selected by user.
In the interface, user can carry out the polymerization that attribute is realized in cooperation by selection inquiry index and query object,
It selects querying attributes to realize output, screening and the grouping of attribute, and is finally completed user's input.System utilizes the sheet of inquiry oriented
The derived relation that body inference rule is derived helps user to shield actual data model storage and Optimizing Queries construction process, makes
Oligo-element can be operated to express the more complicated query structure including classified statistic inquiry, system by obtaining terminal user
Availability and ability to express are stronger.Final user input user_input be integrated into five-tuple (mname, ind, gprop,
Oprop, fcond) it is stored in system, wherein mname is institute's interrogation model title, and ind is index set selected by user, gprop
For packet attributes set selected by user, oprop is output attribute set selected by user, and fcond is screening conditions collection selected by user
It closes, subsequent system operation Ontology Query construction algorithm completes the construction of Ontology Query sentence SPARQL, then passes through mark on the basis of this
Quasi- SPARQL turns SQL method and generates SQL query statement, and completes SQL query operation.
Embodiment 1:
It is assumed that the query demand of terminal user is " combined turnovers of certain food and drink brand each shop various quarters in 2016 ", use
The use operation of VQS system of the invention is as follows:
Terminal user (inquiry user) is firstly the need of the ontology model for selecting inquired field, and such as user selects in this example
" management of food and drink foreground message " model, after having selected corresponding domain model, user can start to construct query pattern.
Terminal user clicks to enter the query construction interface in " management of food and drink foreground message " field, as shown in Fig. 2, user
Inquiry index " turnover " is clicked as desired, is clicked in query object " shop ", and by querying attributes, " order begins a theatrical performance the time for setting
(season) " grouping.It is the query construction stage after constructed in groups, to attribute, " order begins a theatrical performance the time to user in (year as desired
Part) " setting screening conditions " are equal to: 2016 ", to querying attributes " brand name " setting screening conditions by " be: so-and-so fete ".Extremely
This completes query construction, then clicks " confirmation " and submits inquiry.
After user completes operation, system will run complete data access process and obtain query result, and enter data
It shows interface, includes: query result, SPARQL inquiry, four part of SQL query and data visualization, specifically such as attached drawing 3-5 institute
Show, wherein Fig. 3 is the SPARQL query statement generated, and Fig. 4 is the SQL query statement generated, and Fig. 5 is can to query result progress
Depending on changing the schematic diagram shown.
Embodiment 2:
The present embodiment is a kind of end-user listening data access system based on ontology model using the method for the present invention, packet
It includes:
Ontology model preprocessing module, is responsible for according to inquiry meta-model and the ontology inference based on the inquiry meta-model is advised
Then, the ontology model to be inquired user pre-processes, and infers " inwardly shared " relationship therein and " shared outward " closes
System；
Ontology Query SPARQL sentence constructing module is responsible for converting user input into Ontology Query SPARQL sentence；
Enquiry module is responsible for generating SQL statement according to the Ontology Query SPARQL sentence, by the ontology model
Carry out access and inquiry that SQL query realizes data.
Above embodiments are the general process of the method progress data access in the present invention, which is only to this hair
Bright spirit gives an example.Those skilled in the art can do various repair to described specific embodiment
Change or supplement or be substituted in a similar manner, however, it does not deviate from the spirit of the invention or surmounts the appended claims institute
The range of definition.
Claims (8)
1. a kind of end-user listening data access method based on ontology model, which comprises the following steps:
1) the ontology inference rule according to inquiry meta-model and based on the inquiry meta-model, to the ontology model to be inquired of user
It is pre-processed, infers " inwardly shared " relationship and " shared outward " relationship therein；
The inquiry meta-model is related to User behavior, unrelated with specific area, is expressed using OWL Ontology Language, including
" querying attributes ", " query object " and " inquiry index ", wherein " querying attributes " are for describing user's database table of concern
, database table belonging to " querying attributes " is then abstracted as " query object ", between " querying attributes " and " query object " in addition to
" subordinate " of physics is associated with, and also has " inwardly shared " relationship in logic by ontology inference regular expression；For polymerizable
One kind " querying attributes ", use " inquiry index " concept to express its statistical value, " inquiry index " and " query object "
Between have " outward shared " relationship in logic by ontology inference regular expression；
The ontology inference rule operates on the inquiry meta-model, for expressing " shared outward " and " inwardly shared " institute
Derived relation that is describing and being not directly present in original bulk model；
2) Ontology Query SPARQL sentence is converted user input into；
3) SQL statement is generated according to the Ontology Query SPARQL sentence, by carrying out SQL query realization to the ontology model
The access and inquiry of data.
2. the method according to claim 1, wherein described convert user input into Ontology Query SPARQL language
Sentence, comprising the following steps:
A) all " query object " and " external key association " relationships between them involved in inquiry are determined；
B) " external key association " relationship described in utilizing, generates chart-pattern part most crucial in Ontology Query sentence；
C) its in " the inquiry index " in traverse user input, " output attribute " and " packet attributes " Lai Shengcheng SPARQL sentence
SPARQL each section is finally assembled into whole character string and returned by remaining part point.
3. according to the method described in claim 2, it is characterized in that, step a) uses ontology route searching subalgorithm, with user
Inputting user_input and ontology model object ont_mgr is input, export for all " query objects " involved in inquiring it
Between " external key association " relationship list；The ontology route searching subalgorithm includes: to extract from " query object " digraph
" query object " that all in-degrees are 0 forms origin object set, next traverses origin object set, to each " inquiry pair
As " all started using it as starting point with breadth first search order traversal " query object " digraph, for being visited in ergodic process
Any " query object " asked, checks whether it with any " querying attributes " in user's input has " subordinate " relationship, if having
The results list then is added in " external key association " relationship of the secondary traversal.
4. according to the method described in claim 2, it is characterized in that, step b) using chart-pattern construct subalgorithm, it is defeated with user
Entering user_input, ontology model object ont_mgr with " external key is associated with " relation list path_list is input, is exported as this
The chart-pattern part WHERE of body inquiry, and variable i d used in chart-pattern is write back into the output that user inputs user_input
In attribute and packet attributes, for constructing the other parts in SPARQL sentence.
5. the method according to claim 1, wherein using user's Visual Intelligent Interface Model, the interface Zhong Bao
The metric tree containing inquiry, query object figure, querying attributes table, inquiry 4 child windows of the list of elements, wherein inquiry metric tree window is responsible for
Show " inquiry index "；Query object figure window is responsible for showing " query object " with diagram form；Querying attributes table window is responsible for exhibition
Show " querying attributes " relevant to " query object " that user is clicked；It is selected that inquiry list of elements window is responsible for record user
Element can click the polymerization methods of selective goal for " inquiry index " selected by user.
6. according to the method described in claim 5, it is characterized in that, in user's Visual Intelligent Interface Model interface, Yong Hutong
It crosses selection inquiry index and query object carries out the polymerization that attribute is realized in cooperation, querying attributes is selected to realize the output of attribute, sieve
Choosing and grouping, and it is finally completed user's input.
7. according to the method described in claim 6, it is characterized in that, the user input be integrated into five-tuple (mname, ind,
Gprop, oprop, fcond), wherein mname is the model name inquired, and ind is index set selected by user, and gprop is
Packet attributes set selected by user, oprop are output attribute set selected by user, and fcond is screening conditions set selected by user.
8. a kind of end-user listening data based on ontology model accesses system characterized by comprising
Ontology model preprocessing module, is responsible for according to inquiry meta-model and the ontology inference based on the inquiry meta-model is regular,
The ontology model to be inquired user pre-processes, and infers " inwardly shared " relationship and " shared outward " relationship therein；
Ontology Query SPARQL sentence constructing module is responsible for converting user input into Ontology Query SPARQL sentence；
Enquiry module is responsible for generating SQL statement according to the Ontology Query SPARQL sentence, by carrying out to the ontology model
The access and inquiry of SQL query realization data；
The inquiry meta-model is related to User behavior, unrelated with specific area, is expressed using OWL Ontology Language, including
" querying attributes ", " query object " and " inquiry index ", wherein " querying attributes " are for describing user's database table of concern
, database table belonging to " querying attributes " is then abstracted as " query object ", between " querying attributes " and " query object " in addition to
" subordinate " of physics is associated with, and also has " inwardly shared " relationship in logic by ontology inference regular expression；For polymerizable
One kind " querying attributes ", use " inquiry index " concept to express its statistical value, " inquiry index " and " query object "
Between have " outward shared " relationship in logic by ontology inference regular expression；
The ontology inference rule operates on the inquiry meta-model, for expressing " shared outward " and " inwardly shared " institute
Derived relation that is describing and being not directly present in original bulk model.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101694629A (en) * | 2009-10-23 | 2010-04-14 | 北京邮电大学 | Context sensing application platform based on main body and work method thereof |
| CN103793465A (en) * | 2013-12-20 | 2014-05-14 | 武汉理工大学 | Cloud computing based real-time mass user behavior analyzing method and system |
| US8799177B1 (en) * | 2010-07-29 | 2014-08-05 | Intuit Inc. | Method and apparatus for building small business graph from electronic business data |
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| US8799177B1 (en) * | 2010-07-29 | 2014-08-05 | Intuit Inc. | Method and apparatus for building small business graph from electronic business data |
| CN103793465A (en) * | 2013-12-20 | 2014-05-14 | 武汉理工大学 | Cloud computing based real-time mass user behavior analyzing method and system |
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