CN104133673B - The instances of ontology matching system and method customized based on user - Google Patents
The instances of ontology matching system and method customized based on user Download PDFInfo
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Abstract
The present invention provides a kind of instances of ontology matching system customized based on user and method, and the instances of ontology matching system that should be customized based on user includes assembly module and configuration module；The assembly module, including multiple atom component；The configuration module, customized information for receiving user's input, and matching task customized file is generated according to the customized information, and the atom component according to needed for the matching task customized file chooses user from the assembly module, to perform instances of ontology matching task, the matching task customized file includes：Body O to be matched1And O2URI positions, match parameter and matching flow.The present invention can allow users to the feature according to matching body, and the example match flow of the body to design oneself needs is customized and assembled to atom component, suitable matching task is generated, and acquisition accurately matches result.
Description
Technical field
It is the present invention relates to semantic web technologies field, more particularly to a kind of matched based on the instances of ontology that user customizes
System and method.
Background technology
Semantic Web is proposed by the founder Tim Berners-Lee of WWW, is the weight for exploring next generation internet development
Want technology.Current Semantic Web Technology has been obtained for extensive development, and emerges substantial amounts of practical application, such as
LinkedData, Semantic Wiki etc..Along with the development of semantic net, increasing data are described by the way of body.
But the isomery between different bodies causes the new obstacle of data exchange and Semantic Interoperation.The isomery of body is divided into two
The isomery of aspect, mode layer isomery and instance layer.Due to the disclosure of now a large amount of extensive ontology knowledge bases, each knowledge base
Example number is larger, it is impossible to can manually be alignd as mode layer, so the example of automatic aligning ontology knowledge base
The technology of layer becomes a big focus of current semantic network technology.
Existing many researchs on instances of ontology matching process both at home and abroad at present, and have many instances of ontology matchings
System is developed and is applied, and relatively famous system includes ASMOV, SLINT+, Sigma, Paris, Codi etc.,
These systems can obtain preferable result on the data set having, but can not be in all instances of ontology matching tasks
All obtain gratifying matching result.For example they can not all handle the example of two bodies predicate number change compared with
Matching task when big, and these systems are all based on similarity-rough set to determine that matching pair, and the system having exist
Existing information is not made full use of when matching, some systems can only be applicable specific data set.And it is most
System is all to have fixed matching flow, and uses unified matching strategy, the scheme of unified calculating similarity.These
Bad influence can be all brought in the matching of actual knowledge base.
The content of the invention
(1) technical problem to be solved
The technical problem to be solved in the present invention is the entity information for how making full use of body, and matching is flowed according to demand
Cheng Jinhang is assembled, and acquisition accurately matches result.
(2) technical scheme
In order to solve the above technical problems, technical scheme provides a kind of instances of ontology customized based on user
Match system, including assembly module and configuration module；
The assembly module, including multiple atom component；
The configuration module, the customized information for receiving user's input, and appointed according to customized information generation matching
Business customized file, and the atom component according to needed for the matching task customized file chooses user from the assembly module
To perform instances of ontology matching task, the matching task customized file includes：Body O to be matched1And O2URI positions, matching
Parameter and matching flow.
Further, the atom component in the assembly module includes：
Preprocessor, for completing initialization matching task before actual matching operation is performed, including, parsing body,
Term in standardization body, the foundation for going in noise and specific adaptation used data structure；
Disabler, the generation for realizing the candidate couple between instances of ontology is operated using inverted index；
Similarity Measure instrument, the similarity of the value for calculating alignment predicate；
Similarity polymerizer, polymerize for the similarity for multiple attributes；
Matching strategy device, the strategy for the matching strategy based on information and based on similarity-rough set carries out example alignment；
Validator, the instances of ontology matching pair for rejecting mistake；
Outcome evaluation device, for being estimated according to the evaluation index specified to the instances of ontology matching result after optimization.
Further, the atom component in the assembly module also includes translater, the body for handling different language
Language is unitized during example match task.
Further, the Similarity Measure instrument includes：Cosine similarity calculating instrument based on tfidf, based on volume
The Similarity Measure instrument of distance is collected, based on wordNet Similarity Measure instruments.
Further, the similarity polymerizer includes for the method that the similarity of multiple attributes is polymerize：It is average
Polymerization, sigomid polymerizations, Weighted Index average polymerization method.
In order to solve the above technical problems, present invention also offers a kind of method that said system carries out instances of ontology matching,
Including：
S1：User inputs customized information, and generates matching task customization text according to the customized information by configuration module
Part, and the atom component according to needed for the matching task customized file chooses user from the assembly module；
S2：Initialize term in matching task, including parsing body, standardization body, go noise and specific matching
The foundation of used data structure in device；
S3：Candidate couple between generation instances of ontology is operated using inverted index, candidate is obtained to set and unique letter
Cease example collection；
S4：Predicate Similarity Measure is carried out to set to candidate, predicate similarity is then subjected to similarity polymerization, then
Using candidate couple and its fraction as node, enter Priority Queues from high to low by fraction；
S5：To the unique information example collection newly produced, the alignment operation of example is carried out using unique subject matching strategy,
Then by the example of the alignment newly produced to producing new example match pair using a surplus object matching strategy, according to what is newly produced
Example match is to updating unique information example collection, and then the example match using the new generation is to updating phase in Priority Queues
The fraction of candidate couple is closed, and produces new candidate couple and calculates its fraction, the step is repeated until new unique without producing
Information instances set；
S6：For candidate to set, the candidate couple of highest scoring is obtained using score matching strategy, if the candidate obtained
To fraction less than default threshold value then jump to S7, otherwise using the candidate of the acquisition to as example match pair, then updating
The fraction of corresponding candidate couple, and new candidate couple is produced, and unique information example collection is updated, jump to S5；
S7：To the example match of alignment to verifying.
Further, the mode being polymerize in the step S4 to similarity is：
Wherein S is the set of the value of the similarity of all predicates, wi' be i-th of predicate similarity weight, wi" be
The weight of i-th of predicate.
(3) beneficial effect
In the instances of ontology matching system that the present invention is provided, user can be according to the feature of instances of ontology, to pre-defined
Module assembled, to carry out the matching of instances of ontology.It matches the pattern that flow is an iteration, and this pattern can have
The propagation of effect control matching error.The blocking method of the present invention can substantially reduce the number of matching candidate pair so that system can
Efficiently to handle the example match of extensive body, and similarity polymerization therein can be given with the high similarity of logarithm value
Rational weight is given, is conducive to eliminating the influence of the noise of Ontology Matching.By three kinds of strategy combination modes, it is possible to prevente effectively from
The cold start-up problem of Similarity Measure, different matching tasks can be tackled according to user-defined matching scheme, and obtain
Accurate matching result.
Brief description of the drawings
Fig. 1 is a kind of schematic diagram for instances of ontology matching system customized based on user that embodiment of the present invention is provided.
Embodiment
With reference to the accompanying drawings and examples, the embodiment to the present invention is described in further detail.Implement below
Example is used to illustrate the present invention, but is not limited to the scope of the present invention.
Fig. 1 is a kind of schematic diagram for instances of ontology matching system customized based on user that embodiment of the present invention is provided,
The system includes assembly module 1 and configuration module 2；
The assembly module 1, including multiple atom component 11；
The configuration module 2, the customized information for receiving user's input, and appointed according to customized information generation matching
Business customized file, and the atom component according to needed for the matching task customized file chooses user from the assembly module
To perform instances of ontology matching task, the matching task customized file includes：Body O to be matched1And O2URI positions, matching
Parameter and matching flow.
Wherein, the atom component 11 in the assembly module 1 includes：
Preprocessor, for completing initialization matching task before actual matching operation is performed, including, parsing body,
Term in standardization body, the foundation for going in noise and specific adaptation used data structure；Wherein, preprocessor
It is main to complete the necessary initial work before actual matching operation is performed, such as the parsing of instances of ontology.In addition, in body
The standardization of term, the foundation for going specific data structure used in noise and specific matching process, are typically also locating in advance
The reason stage completes, and the standardization of especially term directly affects the effect of matching strategy below, and such as birthday is in knowing for having
Know and be expressed as " moon--year " inside storehouse, and be expressed as " Year/Month/Day " inside the instances of ontology having, this can cause similarity
Compare and very big error occur, it is possible to which specification can be carried out with the term of specification to those by preprocessor.
Disabler, the generation for realizing the candidate couple between instances of ontology is operated using inverted index；The disabler is used for this
The generation of candidate matches pair between body example, the present invention realizes a kind of brand-new disabler, and different and conventional will be all
Information carry out inverted index, it is possible to use tfidf sequence extracts part " keyword " to do inverted index as feature,
And to coordinate predicate together when inverted index is done；Candidate matches are chosen to the example progress blocking operation of body
It is right, number of comparisons can be greatly reduced.For example, can extract tfidf comes the word of first five, its reason is as we have found that originally
The example of body has a feature：The information that some examples possess only seldom a part of example may just have, it is possible to this
Plant information extraction to come out, only possess the example of this information to being only possible to match.
Similarity Measure instrument, the similarity of the value for calculating alignment predicate；Wherein, the Similarity Measure kit
Include：Cosine similarity calculating instrument based on tfidf, the Similarity Measure instrument based on editing distance is similar based on wordNet
Spend calculating instrument.Wherein, different modes can be customized to the Similarity Measure of the predicate of the example of body.The similarity of attribute
Calculating instrument can be selected from here.Because the Similarity Measure mode of each pair attribute may difference, the side so customized
Formula contributes to the alignment knowledge base of efficiently and accurately.For example when this attribute of birthday is calculated, we may think that two
The same birthday similarity is 1, and other situations are all 0, and when the similarity of comment is calculated, it may be possible to a 0-
1 real number.
Similarity polymerizer, polymerize for the similarity for multiple attributes；Wherein, the similarity polymerizer pair
The method being polymerize in the similarity of multiple attributes includes：Average polymerization method, sigomid polymerizations, Weighted Index is put down
Homopolymerization method.Because each pair entity possesses multipair attribute, after each pair attribute is calculated, it would be desirable to which they are aggregated into
The value of one similarity, the method for traditional polymerization similarity includes average polymerization method and sigomid polymerizations, but both
Polymerization sometimes and do not applying to, and is especially changed greatly in the alignment properties number of each pair example of two bodies
When, this when, each pair example might have many noise datas, it is possible to use Weighted Index average polymerization method, it
Meeting not be that the accurate information of noise data gives very high weight to those so that can weaken the influence of noise and obtain accurate
Matching result.
Matching strategy device, the strategy for the matching strategy based on information and based on similarity-rough set carries out example alignment；
The system in the matching strategy based on information and the strategy based on similarity-rough set, the present invention can be selected to realize three
With strategy：First is that matching pair is directly found using more special information；Second is to coming using existing matching
It is alignment to determine its corresponding information；3rd is to regard a pair of examples of similarity highest as matching pair.
Validator, the instances of ontology matching pair for rejecting mistake, validator is used for the sheet for rejecting part apparent error
Body example match pair, above-mentioned mistake is mainly due to when similarity is calculated, most starting each matching to whether aliging and being
Unknown, the similarity calculated this when is incredible.But due to have found more number matching pair when last,
And now similarity be considered it is believable, so can now reject the low matching of those similarities according to similarity
It is right.
Outcome evaluation device, for being estimated according to the evaluation index specified to the instances of ontology matching result after optimization.
Preferably, the atom component in the assembly module also includes translater, and the body for handling different language is real
Language is unitized during example matching task.The matching of the instances of ontology of different language can be handled using the method for translation.
When facing across the instances of ontology alignment task of language, because current people are seldom to the research of the instances of ontology across language, lead
The method without the directly similarity of two examples across language of calculating is caused, therefore can be by two kinds of different language conversions into one kind
Language, then does the task across language using the knowledge base alignment scheme of same language.
In addition, embodiment of the present invention additionally provides a kind of method that above-mentioned system carries out instances of ontology matching, including：
S1：User inputs customized information, and generates matching task customization text according to the customized information by configuration module
Part, and the atom component according to needed for the matching task customized file chooses user from the assembly module；Specifically,
System is since configuration module, and user can select ensuing matching module and each seed ginseng according to matching task herein
Number；
S2：Initialize term in matching task, including parsing body, standardization body, go noise and specific matching
The foundation of used data structure in device；If across the task of language, in addition to translater is built
S3：Candidate couple between generation instances of ontology is operated using inverted index, candidate is obtained to set and unique letter
Cease example collection；Specifically, data enter disabler, inverted index operation are carried out herein, it can be by each entity herein
Each object of triple take out 5 TFIDF highest words, be then used as the index key assignments of inverted index plus predicate, it
Also whole object and predicate can be together as key assignments.Finally candidate is generated to set and unique using both inverted lists
Information instances set；Wherein, when step S3 carries out inverted index to data, 5 words of each object can be extracted, he
Tfidf values be to come preceding 5. so to prevent influences of other hardly important words to the quality of inverted index, and most
Inverted index can be participated in together with reference to predicate eventually, and do not limit in the present invention is implemented and take out 5 words, as long as the word taken out is accounted for
The proportion of whole object is fewer, can all greatly reduce the number of generation candidate couple after inverted index.
S4：Predicate Similarity Measure is carried out to set to candidate, predicate similarity is then subjected to similarity polymerization, then
Using candidate couple and its fraction as node, enter Priority Queues from high to low by fraction；Specifically, candidate is passed through into phase to set
Each predicate Similarity Measure is carried out like degree calculating instrument, it is similar that predicate similarity then is imported into the progress of similarity polymerizer
Then candidate matches pair and their fraction are entered Priority Queues by degree polymerization from high to low as node by fraction.
S5：To the unique information example collection newly produced, the alignment operation of example is carried out using unique subject matching strategy,
Then by the example of the alignment newly produced to producing new example match pair using a surplus object matching strategy, according to what is newly produced
Example match is to updating unique information example collection, and then the example match using the new generation is to updating phase in Priority Queues
The fraction of candidate couple is closed, and produces new candidate couple and calculates its fraction, the step is repeated until new unique without producing
Information instances set；
S6：For candidate to set, the candidate couple of highest scoring is obtained using score matching strategy, if the candidate obtained
To fraction less than default threshold value then jump to S7, otherwise using the candidate of the acquisition to as example match pair, then updating
The fraction of corresponding candidate couple, and new candidate couple is produced, and unique information example collection is updated, jump to S5；
S7：To the example match of alignment to verifying.
Wherein, the mode being polymerize in the step S4 to similarity is：
Wherein S is the set of the value of the similarity of all predicates, wi' be i-th of predicate similarity weight, wi" be
The weight of i-th of predicate.The aggregate function can be partial to the number bigger to score value and give very high weight, can so reduce that
The influence of the small number of a little score values.
Wherein, above-mentioned steps S5 and S6 can select a variety of strategies to come together to determine matching pair when example is matched
Selection, and the comparison based on similarity is can not be when most starting and choosing, due to most similarity at first
Calculate dependent on matching pair, the situation of this coupling can cause most that Similarity Measure is unreliable at first, pass through preceding two
Individual strategy come choose matching to after, we using based on similarity relatively come determine matching pair, finally these three strategies be
One iterative process.Step S5 and step S6 constitute an iterative process, and such alternative manner helps to obtain high precision
Matching pair, due to being most that, based on the matching pair that relatively special information is found, this matching is to being very accurate at first
's.This iterative process only can not be found by specific information matching to when can just be looked for similarity matching pair,
And look for every time matching to when be all only extract a fraction highest as matching pair, the scheme of such progressive alternate,
Enable the matching found afterwards to similarity reflect their real similarities substantially.
Wherein, block techniques have been used in order to reduce the number of candidate matches pair in step s3, has made full use of example
A description information characteristic, but be due to that presumable example does not compare special description characteristic really, will be at this
Step is missed, thus next finding every time matching to after can increase candidate couple, it be might have by example it is very rich
Rich relation information comes increased.For example when it is understood that A graduates from Tsing-Hua University, B graduates from C, if A and B are alignment,
We just speculate C and Tsing-Hua University is probably alignment, so they should be added to candidate to set the inside.
In the instances of ontology matching system that embodiment of the present invention is provided, user can be right according to the feature of instances of ontology
Pre-defined module is assembled, to carry out the matching of instances of ontology.It matches the pattern that flow is an iteration, this mould
Formula can effectively control the propagation of matching error.The blocking method of the present invention can substantially reduce the number of matching candidate pair, make
The system of obtaining can efficiently handle the example match of extensive body, and similarity polymerization therein can be high with logarithm value
Similarity gives rational weight, is conducive to eliminating the influence of the noise of Ontology Matching., can be with by three kinds of strategy combination modes
The cold start-up problem of Similarity Measure is prevented effectively from, can tackle different matchings according to user-defined matching scheme appoints
Business, and obtain accurate matching result.
Embodiment of above is merely to illustrate the present invention, and not limitation of the present invention, about the common of technical field
Technical staff, without departing from the spirit and scope of the present invention, can also make a variety of changes and modification, therefore all
Equivalent technical scheme falls within scope of the invention, and scope of patent protection of the invention should be defined by the claims.
Claims (6)
1. a kind of instances of ontology matching system customized based on user, it is characterised in that including assembly module and configuration module；
The assembly module, including multiple atom component；
The configuration module, the customized information for receiving user's input, and determined according to customized information generation matching task
File processed, and atom component according to needed for the matching task customized file chooses user from the assembly module is to hold
Row instances of ontology matching task, the matching task customized file includes：Body O to be matched1And O2URI positions, match parameter
With matching flow；
The atom component includes：
Preprocessor, for completing initialization matching task before actual matching operation is performed, including, parsing body, specification
Change term in body, go the foundation of data structure used in noise and specific adaptation；
Disabler, the generation for realizing the candidate couple between instances of ontology is operated using inverted index；
Similarity Measure instrument, the similarity of the value for calculating alignment predicate；
Similarity polymerizer, polymerize for the similarity for multiple attributes；
Matching strategy device, the strategy for the matching strategy based on information and based on similarity-rough set carries out example alignment；
Validator, the instances of ontology matching pair for rejecting mistake；
Outcome evaluation device, for being estimated according to the evaluation index specified to the instances of ontology matching result after optimization.
2. the instances of ontology matching system according to claim 1 customized based on user, it is characterised in that the assembly mould
Atom component in block also includes translater, is unified language during instances of ontology matching task for handling different language
Change.
3. the instances of ontology matching system according to claim 1 customized based on user, it is characterised in that the similarity
Calculating instrument includes：Cosine similarity calculating instrument based on tfidf, the Similarity Measure instrument based on editing distance, is based on
WordNet Similarity Measure instruments.
4. the instances of ontology matching system according to claim 1 customized based on user, it is characterised in that the similarity
Polymerizer includes for the method that the similarity of multiple attributes is polymerize：Average polymerization method, sigomid polymerizations, plus
Weigh exponential average polymerization.
5. a kind of method that system as described in Claims 1-4 is any carries out instances of ontology matching, it is characterised in that including：
S1：User inputs customized information, and generates matching task customized file according to the customized information by configuration module, with
And the atom component according to needed for the matching task customized file chooses user from the assembly module；
S2：Initialize term in matching task, including parsing body, standardization body, go in noise and specific adaptation
The foundation of used data structure；
S3：Candidate couple between generation instances of ontology is operated using inverted index, candidate is obtained real to set and unique information
Example set；
S4：Predicate Similarity Measure is carried out to set to candidate, predicate similarity is then subjected to similarity polymerization, then will be waited
Choosing pair and its fraction enter Priority Queues from high to low as node by fraction；
S5：To the unique information example collection newly produced, the alignment operation of example is carried out using unique subject matching strategy, it is described
Unique subject matching strategy is used for the matching strategy based on information and the strategy based on similarity-rough set carries out example alignment；Then
By the example of the alignment newly produced to producing new example match pair, the surplus object matching using a surplus object matching strategy
Strategy is used for the instances of ontology matching pair that part apparent error is rejected according to similarity；According to the example match newly produced to more
New unique information example collection, then the example match using the new generation is to updating correlation candidate pair point in Priority Queues
Number, and produce new candidate couple and calculate its fraction, the step is repeated until without the new unique information example collection of generation；
S6：For candidate to set, the candidate couple of highest scoring is obtained using score matching strategy, if the candidate couple obtained
Fraction then jumps to S7 less than default threshold value, otherwise using the candidate of the acquisition to as example match pair, then updating corresponding
Candidate couple fraction, and produce new candidate couple, and update unique information example collection, jump to S5；
S7：To the example match of alignment to verifying.
6. method according to claim 5, it is characterised in that the mode being polymerize in the step S4 to similarity
For：
<mrow>
<mi>E</mi>
<mi>x</mi>
<mi>p</mi>
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ExpAgg (S) is similarity polymerizing value, and wherein S is the set of the value of the similarity of all predicates, SiFor i-th of predicate
Similarity value, w 'iIt is the weight of the similarity of i-th of predicate, w "iFor the weight of i-th of predicate.
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| CN103294791A (en) * | 2013-05-13 | 2013-09-11 | 西安电子科技大学 | Extensible markup language pattern matching method |
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| CN102135905A (en) * | 2011-03-17 | 2011-07-27 | 清华大学 | User customization-based body matching system and method |
| CN103294791A (en) * | 2013-05-13 | 2013-09-11 | 西安电子科技大学 | Extensible markup language pattern matching method |
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