CN104021198B - The relational database information search method and device indexed based on Ontology - Google Patents
The relational database information search method and device indexed based on Ontology Download PDFInfo
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- CN104021198B CN104021198B CN201410267202.2A CN201410267202A CN104021198B CN 104021198 B CN104021198 B CN 104021198B CN 201410267202 A CN201410267202 A CN 201410267202A CN 104021198 B CN104021198 B CN 104021198B
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
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Abstract
The present invention relates to a kind of relational database information search method indexed based on Ontology and device, comprise the following steps：Information formation ontology database semantic indexing first by extracting database automatically, then participle is carried out to the query statement of user according to the user dictionary extracted from index, sql sentences are built automatically eventually through sql sentences generating algorithm, connected by database and to form Query Result and return to user, so as to realize the semantic retrieval to relational database.Prior art is contrasted, the present invention can use keyword query structural data in the way of class non-structural data enquiry；The key message that database structure and user are concerned about only is imported ontology knowledge base by the present invention simultaneously, and storage overhead and synchronized update expense are smaller, and realizes inquiry using the query function of data base management system itself, and the response time also greatly improves.
Description
Technical field
It is more particularly to a kind of to be indexed based on Ontology the present invention relates to a kind of database information retrieval method and device
Relational database information search method and device, belong to Computer Applied Technology field.
Background technology
Computer information retrieval starts from the beginning of the fifties, and in the developing history of more than 40 years, computer information retrieval is substantially gone through
Off-line retrieval, online information retrieval, networking online information retrieval three phases are passed through.Keyword and Google PageRank are based at this stage
The information retrieval technique of algorithm is quite ripe.But in the database retrieval field especially enterprises big data epoch
Database information retrieval, in addition to writing the inquiry of sql sentence retrievals manually, still lacks other more preferable automatiom information retrieval technologies
Research.
Semantic network technology is one of focus of current internet technical research.The word used in current most of pages
Information is not easy to machine and automatically processed, and is only suitable for people oneself and reads understanding.How the data and information that can automatically process are solved
The problem of aspect development is slower, information content increases severely on network, people arrange this pressure in the urgent need to computer shares knowledge
Today, a problem as information retrieval.Semantic net is just otherwise known as the potential scheme for solving this problem
web3.0。
The core technology realized as semantic net --- body (Ontology), defines the word of composition subject fields
The basic terms and its relation of remittance table, and combine these terms and relation to define the rule that vocabulary off-balancesheet is prolonged.
The target of body be capture association area knowledge there is provided being commonly understood by the domain knowledge, determine the field
The vocabulary inside approved jointly, and provide from the formalization pattern of different levels these vocabulary (term) correlations between vocabulary
Explicitly define.
With the rise of semantic network technology, turn into next target of search engine based on semantic intelligent search.These
Research is the retrieval to unstructured data mostly, for the number in the database especially enterprises big data epoch of structuring
According to storehouse information retrieval, still lack certain research.Although the wherein rare retrieval based on ontology knowledge base forms body text
Part, but all examples and information all need to import in ontology file, show for the large-scale data of enterprises
So not enough reality and autgmentability is nor very well.
In general, there is problems with existing research：
(1) in the few retrieval research to structured database of information retrieval field.
(2) although some researchs combine semantic network technology, all data are all poured into ontology knowledge base
Go, be eventually equal to the retrieval of ontology knowledge base, storage overhead increase is not only resulted in, while ontology knowledge base and database is same
Step, which updates, also turns into problem.
The content of the invention
The purpose of the present invention be for solve the problems, such as to similar unstructured data retrieve database efficient retrieval, will close
Key search words is combined with ontology in semantic net and retrieved there is provided a kind of based on the relational database information that Ontology is indexed
Method and apparatus, realize the key search function to the similar unstructured data of database.
The thought of the inventive method is the information formation ontology database semantic indexing by extracting database automatically, then
Participle is carried out to the query statement of user according to the user dictionary extracted from index, eventually through sql sentences generating algorithm certainly
It is dynamic to build sql sentences, connect to form Query Result and return to user by database, so as to realize the semanteme to relational database
Retrieval.
The concrete technical scheme of the present invention is as follows：
A kind of relational database information search method indexed based on Ontology, is comprised the following steps：
Step 1: to given database to be retrieved, according to defined ontology file structure, database is extracted automatically
Table row name and other train values generation ontology file for being concerned about of primary key column or user；
Step 2: the ontology file that scanning step one is generated, extracts word and self-defined part of speech builds User Defined word
Allusion quotation；
Step 3: the inquiry to user uses participle instrument to carry out participle and part-of-speech tagging according to custom dictionaries；
Step 4: the result of correspondence participle automatically generates sql sentences according to sql sentences generation template, sql sentences were generated
Journey is as follows：
According to the classification of User Defined part of speech, user input final word segmentation result be daob, dait, and indi this three
Plant the combination between part of speech and these three parts of speech.For single-mode (i.e. every kind of part of speech at most one), a shared 3+3+1=
Combination in 7, for every kind of combination, processing method is as follows：
Situation 1：A/daob：select*from A；
Situation 2：B/dait：All table names comprising B are enumerated for user's selection (if only one, directly acquiescence choosing
Select), change into situation 4；
Situation 3：C/indi：All row names comprising example C are enumerated for user's selection (if only one, directly acquiescence
Selection), change into situation 6；
Situation 4：A/daob+B/dait：select B from A；
Situation 5：A/daob+C/indi：Select*from A where D=' C ' (D is the row belonging to example C)；
Situation 6：B/dait+C/indi：Enumerate all table names comprising B for user's selection (if only one, it is directly silent
Recognize selection), change into situation 7；
Situation 7：A/daob+B/dait+C/indi：(D is belonging to example C to select B from A where D=' C '
Row).
For non-single-mode, i.e., at least one self-defined part of speech, comprising more than one word segmentation result, according to table
Row example whether associated packets, judge whether correlation method it is as follows：Between table row, for each table (number i.e. in body
According to object subclass) and each row (the data item subclass i.e. in body) according to the data object attribute inside body, see them
Between with the presence or absence of " having data item " data object attribute associate, have and be just classified as one group, different groups are not classified as then.Row with
Between example, for each row and example, judge the example whether be the row example i.e. example whether belong to expression the row
Data item subclass, be to be classified as one group, be otherwise classified as different groups.Each of which group is one of seven kinds of situations above again,
It is respectively processed again.
Step 5: carrying out data base querying for the sql sentences of generation, and return result to user.
Pass through the operation of above-mentioned steps, you can the query statement inputted by user builds sql sentences and forms inquiry knot automatically
Fruit is finally returned that to user.
It is a kind of that device, including ontology file generating means are retrieved based on the relational database information that Ontology is indexed, take out
Take family custom dictionaries device, synchronized update device, crucial character inputting apparatus, participle device, sql sentences generating means, number
According to library inquiry device and data presentation device；
The ontology file generating means be used for train value information that the database that is given to user and user be concerned about according to
Defined ontology file structure, extracts the table row name and primary key column of database or other train values life of user's care automatically
Into ontology file；
The extraction User Defined dictionary device is used to use tri- kinds of parts of speech of daob, dait and indi using participle instrument
Participle, and part-of-speech tagging are scanned to ontology file, and extracts the group item of word and part-of-speech tagging and generates user certainly
Define between dictionary, each group item with ", " separation；
The synchronized update device is used to occur to list example to the database table stored in ontology file in database
Increase, when deleting and changing, synchronized update ontology knowledge base and custom dictionaries；
The crucial character inputting apparatus is used for the query statement for receiving user；
The participle device be used for by the query statement of user using participle instrument according to custom dictionaries carry out participle with
And part-of-speech tagging；
The sql sentences generating means are used to generate word segmentation result structure of the template to user's query statement according to sql sentences
Build corresponding sql sentences；
The database inquiry device is used to inquire about database using sql sentences and returns to Query Result；
The Query Result that the data presentation device is used to return to database inquiry device is shown to user.
Beneficial effect
Prior art is contrasted, the present invention possesses following beneficial effect：
(1) keyword query structural data can be used in the way of class non-structural data enquiry, can be with non-knot
Structure inquiry shares unified query interface, and user is not required to it is to be understood that the inquiry to structural data can be achieved in sql specifications.
(2) key message that database structure and user are concerned about only is imported ontology knowledge base by the present invention, storage overhead and
Synchronized update expense is smaller, and realizes inquiry using the query function of data base management system itself, and the response time is also significantly
Improve.
Brief description of the drawings
Fig. 1 is the relation between ontology file structure and class and class.
Fig. 2 is electric power the whole network day statistical form array structure.
Fig. 3 is power transformer day statistical form array structure.
Fig. 4 is the body construction of specific storehouse information and data object information in embodiment.
Fig. 5 is data object in embodiment：The body construction of the specific data item information of electric power the whole network day statistics.
Fig. 6 is data object in embodiment：The body construction of the specific data item information of power transformer day statistics.
Fig. 7 is the structural representation of apparatus of the present invention.
Fig. 8 is the data flow diagram of the inventive method.
Fig. 9 is the structural representation of crucial character inputting apparatus of the invention.
Figure 10 is the structural representation of data presentation device of the present invention.
Embodiment
In order to better illustrate technical scheme, below by two example tables, the present invention is done furtherly
It is bright.
The present embodiment is using Domestic Database up to dream as the database of retrieval, and database name is PSIDP_SEARCH, data
The address in storehouse is localhost:12345/PSIDP_SEARCH, electric power the whole network therein day statistical form array structure as shown in Fig. 2
Power transformer day statistical form array structure is as shown in figure 3, the data flow of search method is as shown in figure 8, detailed process is as follows：
Step 1: to given database PSIDP_SEARCH to be retrieved, it is first according to defined ontology file structure
First the metadata of database is accessed using jdbc and extract the table row name and primary key column example of database, electric power the whole network day statistics
Plant stand in middle name column example, power transformer day statistics ranks example, then generates ontology file by jena；
Ontology file is owl files, is that, based on xml extensible markup languages, file can use different marks to mark
Remember class or example defined in us.Class and relation and example can be defined in body, wherein class is that the abstract of concept is retouched
State, relation is also known as attribute, including two kinds, and a kind of is the relation between data object attribute i.e. class and class, and another is data
Type attribute is class and data type such as integer, the relation of character string type etc..Example is the instantiation of class, and similar program is set
Class and the relation of example in meter.Ontology file in the present invention defines three top layer classes, is storehouse information respectively, data object and
Data item (storehouse in correspondence database, table, row respectively).Wherein storehouse information has two string of storehouse type and storehouse link address
The data type attribute of type, the address connected as database.For each specific database, storehouse information is initially set up
Subclass, then set up an example of the subclass, multiple databases just have the subclass of multiple storehouse information and the example of subclass.
Each table in storehouse as data object a subclass, each row in table as data item a subclass.Each storehouse
The subclass of information all can with by the database table generation data object subclass set up " have data object " data object belong to
Property association, the data object subclass each generated by table can with by the column-generation in the table data item subclass set up " have number
According to item " data object attribute association.Wherein all table row and table can be thus exported by a complete database
The association in storehouse is arranged, for the major key of conventional database column example or table, specific train value is extracted, is used as the example of row object
It is stored in body owl files.The extraction of file uses jdbc and body java api jena structures using generation using java
Build java programming automatic generations.Incidence relation such as Fig. 1 between ontology file structure and class and class.Should in actual database
In, modification operation is seldom updated to the structure of database, is that additions and deletions to data are looked into and changed mostly, so corresponding to body
, can trigger event modification pair when the example of the row object of write-in ontology file in database changes in the renewal of file
The ontology file example answered.
For example, structure content such as Fig. 4 of the database structure body shown according to the present example, data object electric power the whole network day
Count and associate such as Fig. 5 and Fig. 6 with the data item of power transformer day statistics.Wherein on class, attribute is with example in ontology file
In part description and ontology file structure it is as follows：
Step 2: the ontology file of scanning previous step generation, generates User Defined dictionary.
Class in the ontology file of previous step generation is segmented into following several types：Data object in ontology file,
Table i.e. in database, self-defined part of speech is used as using daob；Row in data item in ontology file, i.e. database table, make
Self-defined part of speech is used as with dait；The value of some key columns in example in ontology file, i.e. table, using indi as self-defined
Part of speech.Group item sequence according to word and its part-of-speech tagging in extraction ontology file defined above generates custom dictionaries, its
In separated with ", " between each group item.Carried out during extraction using the java api jena of body, respectively using the method in jena
Listclasses () and listIndividuals () list all class and example, and method is passed through again for class
GetSuperClass () judges its type according to its parent：If parent is data object, its part of speech is exactly daob,
If parent is data item, its part of speech is exactly dait.Each class and example and their part of speech can be generated accordingly, made
Deposited for user dictionary with txt forms.
Such as electric power the whole network day statistics/daob, power transformer day statistics/daob, ID/dait, maximum time of origin/
Dait, minimum value/dait and the whole network load/indi, Yuan Zhuan/indi etc..
Step 3: carrying out participle and part-of-speech tagging according to custom dictionaries to the inquiry of user.
ICTCLAS be the Chinese Academy of Sciences develop include Chinese word segmentation, part-of-speech tagging, name Entity recognition, new word identification, simultaneously
Support the outstanding Words partition system of user-oriented dictionary.The read statement of user is cut into by band by using ICTCLAS Words partition systems
There is the word sequence of part-of-speech tagging.The input of user is probably a query statement for carrying subjective intention, as long as wherein in sentence
Include the word in the User Defined dictionary of previous step generation, just marked its cutting and according to User Defined dictionary
Corresponding part of speech.Maximum as user inputs the whole network load is how many, and the result after cutting word is the whole network load/indi and maximum
Which the transformer title that value/dait. and for example counts the Zhong Yuan village user's input electric power transformer day has, and the result after cutting word is electricity
Power transformer day statistics/daob, Yuan Zhuan/indi and title/dait.
Step 4: to cutting word result according to sql sentences generation masterplate generation sql sentences.Such as the whole network load of previous step
Maximum is how many, i.e. indi+dait, belongs to situation 6, due to have in two tables maximum this, two tables can be returned
Name, user ultimately generates sentence select maximum from electric power the whole network day statistics where when clicking on electric power the whole network day statistics
Claim=' the whole network load ' (if sql sentences can equally be formed by clicking on power transformer day statistics, but because power transformer day is counted
In do not have entitled the whole network load example can cause Query Result for sky)；The power transformer day statistics Zhong Yuan village of previous step
Transformer title which has, i.e. daob+dait+indi belongs to situation 7, ultimately generates sentence select title from electric power
Transformer day statistics where plant stand name=' Yuan Zhuan '.The above is the inquiry of single-mode, if user's input electric power the whole network day system
The non-single-mode inquiry of plant stand name of meter peak and low valley and power transformer day statistics, as a result electric power the whole network day can be counted/
Daob, peak/dait, low ebb/dait is as one group, and power transformer day statistics/daob, and plant stand name/dait is as in addition
One group forms sql sentences respectively.
Step 5: the sql sentences generated using previous step carry out data base querying, user is returned result to.For the whole network
The maximum of load is how many, returning result 16330,15793,16251,17912,14343 etc.；For Yuan Zhuan transformer name
Title has which returning result Ji north Yuan Zhuan/220kV.1# transformers-height and Ji north Yuan Zhuan/220kV.2# transformers-height.For
Non- single-mode, returns to the result of different query statements.
The retrieval device of the present invention is as shown in fig. 7, comprises ontology file generating means, extract User Defined dictionary dress
Put, synchronized update device, crucial character inputting apparatus, participle device, sql sentences generating means, database inquiry device and data
Display device.
Wherein ontology file generating means and extraction User Defined dictionary device are used to obtain retrieval dress of the present invention
The Back ground Information put, synchronized update device is used to keep information between database and ontology file, User Defined dictionary
It is synchronous.Operating process is as follows：
The table row name and primary key column for extracting database PSIDP_SEARCH using ontology file generating means obtain body
File；
User Defined dictionary is obtained using User Defined dictionary device is extracted；
Renewal is synchronized to ontology file and User Defined dictionary using synchronized update device, renewal process is as follows：
When there is data increase operation, increase corresponding example in ontology file, while increasing to the data of addition extremely
In User Defined dictionary；
When there is data modification operation, the legacy data in ontology file and User Defined dictionary, Ran Houzeng are deleted
Plus amended data are into ontology file and User Defined dictionary；
When there is data deletion action, the corresponding data in ontology file and User Defined dictionary is deleted；
After user configures the Back ground Information of good retrieval device by said apparatus, it is possible to realized and closed using other devices
Key search words, detailed process is as follows：
User by crucial character inputting apparatus input inquiry sentence, as shown in figure 9, including keyword entry area (1) and
Query statement sends region (2), and user is in keyword entry area input inquiry sentence, and the maximum of such as the whole network load is many
It is few, " inquiry " (2) button is then clicked on, query statement is submitted；Then participle device by the query statement of user carry out participle with
And part-of-speech tagging；Sql sentences generating means build corresponding sql sentences to participle and part-of-speech tagging result；Data base querying
Device performs sql sentences and database is retrieved and retrieval result is returned；Retrieval result is shown to use by data presentation device
Family, as shown in Figure 10, including results display area domain (1), wherein (2) are normal sql sentences Query Result, including looked into data
Database table information and the data message (3) that shows in a tabular form, the situation 2 in (4) correspondence sql sentence generation masterplates,
Hyperlink (5) is contained in its result, the Query Result of situation 4 of display conversion is clicked on.(6) correspondence sql sentences generation masterplate
In situation 3, as a result equally have a hyperlink (7), click on the Query Result of situation 6 of display conversion.(8) it is paging column, every page extremely
Show 10 Query Results more.
Above-described to specifically describe, purpose, technical scheme and beneficial effect to invention have been carried out further specifically
It is bright, the specific embodiment that the foregoing is only the present invention is should be understood that, for explaining the present invention, is not used to limit this
The protection domain of invention, within the spirit and principles of the invention, any modification, equivalent substitution and improvements done etc. all should
Within protection scope of the present invention.
Claims (2)
1. a kind of relational database information search method indexed based on Ontology, it is characterised in that comprise the following steps：
Step 1：To given database to be retrieved, according to defined ontology file structure, the automatic table for extracting database
Other train values generation ontology file that row name and primary key column or user are concerned about；
Step 2：The ontology file that scanning step one is generated, extracts word and self-defined part of speech builds User Defined dictionary；
Step 3：Inquiry to user uses participle instrument to carry out participle and part-of-speech tagging according to custom dictionaries；
Step 4：The result of correspondence participle automatically generates sql sentences according to sql sentences generation template；
Step 5：Data base querying is performed to the sql sentences of generation, and returns to Query Result to user；
The sql sentences generation template is as follows：
Situation 1：A/daob：select*from A；
Situation 2：B/dait：Enumerate all table names comprising B to select for user, if only one, directly acquiescence selection, conversion
Into situation 4；
Situation 3：C/indi：All row names comprising example C are enumerated to select for user, if only one, directly acquiescence selection,
Change into situation 6；
Situation 4：A/daob+B/dait：select B from A；
Situation 5：A/daob+C/indi：Select*from A where D=' C ', wherein D are the row belonging to example C；
Situation 6：B/dait+C/indi：Enumerate all table names comprising B to select for user, if only one, directly acquiescence choosing
Select, change into situation 7；
Situation 7：A/daob+B/dait+C/indi：Select B from A where D=' C ', wherein D are belonging to example C
Row.
2. a kind of retrieve device based on the relational database information that Ontology is indexed, it is characterised in that including ontology file life
Into device, User Defined dictionary device, synchronized update device, crucial character inputting apparatus, participle device, the life of sql sentences are extracted
Into device, database inquiry device and data presentation device, the ontology file generating means are used for the data given to user
The train value information that storehouse and user are concerned about according to defined ontology file structure, the automatic table row name for extracting database and
Other train values generation ontology file that primary key column or user are concerned about；
The extraction User Defined dictionary device is used for using participle instrument using tri- kinds of parts of speech of daob, dait and indi to this
Body file is scanned participle, and part-of-speech tagging, and extracts the group item generation User Defined of word and part-of-speech tagging
Separated between dictionary, each group item with ", "；Wherein daob represents the data object in ontology file, and dait represents ontology file
In data item, indi represents the example in ontology file；
The synchronized update device is used for the increasing for occurring to list the database table stored in ontology file example in database
Plus, when deleting and changing, synchronized update ontology knowledge base and custom dictionaries；
The crucial character inputting apparatus is used for the query statement for receiving user；
The participle device is used to the query statement of user carrying out participle and word according to custom dictionaries using participle instrument
Property mark；
The sql sentences generating means are used to generate word segmentation result structure of the template to user's query statement according to the sql sentences
Build corresponding sql sentences；
The database inquiry device is used to inquire about database using sql sentences and returns to Query Result；
The Query Result that the data presentation device is used to return to database inquiry device is shown to user.
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