CN103246731A - Web service semantic annotation method based on associated data - Google Patents
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Abstract
The invention relates to the technical field of Web services and provides a Web service semantic annotation method based on associated data and a method for analyzing the feasibility and effectiveness of the annotation method. The technical scheme is that the Web service semantic annotation method based on associated data comprises the following steps: (1) building a Web service annotation model based on DBpedia associated data; and (2) achieving Web service semantic annotation based on the DBpedia associated data: 2-1, analyzing Web services; 2-2; performing parameter refining on a Web service parameter layer; 2-3, performing parameter washing on the Web service parameter layer; 2-4, associating each washed datum of the Web services to a proper DBpedia instance datum; and 2-5, corresponding the parameters of the Web services to an ontic conception of the DBpedia. The Web service semantic annotation method is mainly applied to the Web service technology.
Description
Technical field
The present invention relates to web service technology field, specifically, relate to the Web service semantic tagger based on associated data.
Background technology
Service discovery result by traditional key word matching technique obtains can not satisfy given services request exactly, and simultaneously, the Web service that only has syntactic information is not enough to realize automatic Web service combination.Yet the Web service of the overwhelming majority all lacks sufficient semantic information on the current internet, and all Web services that can not provide user's request relevant in the process of service discovery are provided for this, and be the difficulty of having served combination results automatically.Therefore, in order to add the composition of disappearance to Web service, semantic tagger is necessary.
The proposition of Semantic Web Services is exactly in order to provide better support by semantic tagger for discovery, combination and the execution of serving.In conjunction with Web service and semantic network technology, the feature of Semantic Web Services is embodied by a series of conceptual model, OWL-S for example, Web service model language (WSML), Web service model ontology (WSMO), Web Services Description Language (WSDL) semanteme (WSDL-S) and be the semantic tagger (SAWSDL) of Web Services Description Language (WSDL).The semantic description of these Web services proposes to W3C, still, because different representation languages and notional difference make that they are inconsistent in essence.Thereby the application of Semantic Web Services has been subjected to influence, and the expert that its effect is also only trained realizes.
Especially, METEOR-S semantic tagger framework has proposed a kind of by body, and for semanteme marking method is semi-automatically carried out in Web service, it also utilizes domain body to Web service is categorized in the different fields.In addition, the ASSAM instrument is classified Web service by the applied for machines learning art, afterwards by using the character string similarity measurement that some service describings are mapped on the body.Similitude and the difference analyzed between WSDL and the OWL-S are also arranged, by some special transformation rules, the Web service mark is become the service of OWL-S form.Also have under the help of domain expert and WordNet, Web service is classified and mark, thus structure Semantic Web Services network.
Though above technology has proposed different solutions to the mark of Web service, has following problem:
(1) most of method need be classified to service by classification, could use domain body to serve mark.And the process of classifying is exactly consuming time loaded down with trivial details.
(2) automatic inadequately, they are too complicated and can not use in the practical Web service mark in other words, and automatic service discovery process is brought few benefit.
(3) certain methods need be used different bodies to different services, otherwise annotation results is just unreasonable and accurate.And different domain bodies needs different domain experts to create, the extra work that this process is brought.
For the world open on our internet, above method is to limit to some extent.Current, come from the research work of semantic net, associated data (Linked Data) becomes also the best realization of syndeton data of issue on the internet, caused that one covers wide spectrum, has comprised global metadata space---the generation of the WWW (Web of Data) of data of several hundred million RDF tlv triple.As the core associated data set of data WWW, the DBpedia knowledge base is just being brought into play more and more important effect, and the DBpedia body is consistent, and hierarchical structure is arranged.Therefore, this abundant associated data set has comprised a large amount of interconnective data, cross-cutting body, and based on the widespread use of DBpedia, for the Web service mark with abundant semanteme.Also do not work at present associated data is combined with the Web service mark.
Summary of the invention
The present invention is intended to overcome the deficiencies in the prior art, the method of a Web service semantic tagger is provided, thereby realize Web service with abundant semantic information, set up the bridge that concerns between associated data set and the Web service, for more automatic Web service discovery and combination are laid a good foundation.On the other hand, the present invention will provide a kind of evaluation framework corresponding to this mask method, and the annotation results of online a large amount of Web services is made evaluation, analyze feasibility and the validity of mask method.For this reason, the technical scheme that the present invention takes is that the Web service semanteme marking method based on associated data comprises the steps:
(1) foundation is based on the Web service marking model of DBpedia associated data;
(2) realization comprises following steps based on the Web service semantic tagger of DBpedia associated data:
2-1 is, and Web service is resolved, and mainly comprises following two tasks:
It is legal in the significant WSDL document of user to verify;
For each Web service, resolve the element of three semantic levels describing in the above-mentioned marking model of acquisition;
The Web service parameter of 2-2 layer carries out parameter and refines: for the input and output parameter collection that parsing obtains, the complex parameters type of decomposition that will have structure is simple parameter;
The Web service parameter of 2-3 layer carries out parameter and cleans;
2-4 utilizes DBpedia Spotlight to use, on parameter association to the suitable DBpedia instance data after each cleaning of Web service;
2-5 corresponds to the parameter of Web service on the Ontological concept of DBpedia.
The parameter cleaning process may further comprise the steps:
A. according to input and output information acquisition keyword, the input and output parameter of many services has the name form of getAbyB, and the output parameter that perhaps has is the form of getB, and A is the keyword of input parameter, and B is the keyword of output parameter;
B. realize participle: owing to do not have the space in the definition of parameter name, need carry out participle to parameter according to initial caps, in addition, also need to filter punctuation mark and link symbol;
C. filter insignificant word, obtained one group of independently word after the participle, some in them are nonsensical, need be left in the basket, and the nonsense words table is safeguarded in database;
D. recover abb., another table of safeguarding in our database is the dictionary of abbreviations table, and the final step of processing parameter is by comparing the dictionary of abbreviations table, the abb. in the reduction word array.
Index according to the evaluation framework that proposes is analyzed experimental result, and corresponding to the semanteme marking method based on the DBpedia associated data, this evaluation framework mainly comprises annotation results (RS) and two index: RS=＜A of evaluation result (EV), B, S, I, E, W＞, in this hexa-atomic group:
A is the set of all service parameters;
B has the set of the service parameter of example and Ontological concept mark for by behind this paper algorithm mark;
S is by behind this paper algorithm mark, the set of the service parameter that the instance concepts of mark are correct;
I is by behind this paper algorithm mark, the set of the service parameter that the Ontological concept of mark is correct;
E is for by behind this paper algorithm mark, and parameter is similar in appearance to the set of the service parameter of the mark example of correspondence or Ontological concept;
W is by behind this paper algorithm mark, the set of the service parameter of the instance concepts mistake of mark;
EV=＜Ar,Re,Am＞
Ar is the mark rate (AnnotationRate) of service mark, Ar=|B ∩ A|/| A|;
Re is the degree of accuracy (Precision) of service mark, Re=| (S ∪ I) ∩ A|/| B|;
Am is the blur level (Ambiguity) of service mark, Am=|E ∩ A|/| B|.
Technical characterstic of the present invention and effect:
Use the present invention can extensive, cross-cutting Web service be marked, can access the result of effective and high mark rate, for Web service is automatically found, service combination automatically, and the analysis of Semantic Web Services network provides solid semantic basis.Because the generality of DBpedia Ontological concept, the valuable service semantics that this method obtains can also realize the Web service navigation and recommend.
Combine current opening, the huge associated data of quantity, effect of the present invention also is, on the one hand, excavate the online abundant semanteme of semantic data and given Web service, conversely, produce the semantic network of the Web service on the data WWW, will bring important change for visit and the utilization of Semantic Web Services, semantic service network.
Description of drawings
Fig. 1 is based on the Web service semantic tagger model synoptic diagram of DBpedia associated data.
Fig. 2 is based on the service mask method general frame of DBpedia.
Fig. 3 shines upon the DBpedia instance data to the process synoptic diagram of parameter concept.
The distribution of the service parameter of Fig. 4 marking error in interface: (a) wrong parameter is labeled in 1897 distributions in the interface in the Travel300 services set; (b) wrong parameter is labeled in 1090 distributions in the interface in the OWLS-TC4 services set.
Embodiment
The present invention relates to a kind of method of Web service being carried out automatic semantic tagger, on the basis of open associated data set and body, as the DBpedia knowledge base, the corresponding Web service mark semantic model that has proposed has increased semantic information for lacking semantic Web service; For more automatic Web service discovery and combination are laid a good foundation.Simultaneously, the present invention also provides a kind of evaluation framework corresponding to this mask method.
Advantage of the present invention is, on the one hand, excavated semantic data on the net abundant semanteme give Web service, conversely, will be the visit of Semantic Web Services, semantic service network and utilize and produce important change.
Provide one based on the method for the Web service semantic tagger of DBpedia associated data, thereby the Web service that realization has abundant semantic information, set up the bridge that concerns between associated data set and the Web service, for more automatic Web service discovery and combination are laid a good foundation.On the other hand, the present invention will provide a kind of evaluation framework corresponding to this mask method, and use this semanteme marking method that the annotation results of online a large amount of Web services is made evaluation, analyze feasibility and the validity of mask method.
In order to realize purpose of the present invention, technical scheme of the present invention is as follows:
(1) foundation is based on the Web service marking model of DBpedia associated data.This model mainly comprises two parts, and a part is Semantic Web Services, and technically, the Web service that each WSDL describes can be divided into three layers of service, interface and parameters.Accordingly, from semantic level, each Web service also will be divided into service layer (Service Level), interface layer (Interface Level) and parameter layer (Parameter Level).Another part is the DBpedia body, has comprised the class of DBpedia instance data and body.Thereby two parts are to set up semantic relation by the DBpedia instance data to the Semantic mapping between the parameter layer.Like this, by reasoning in the DBpedia body, can obtain semantic relation potential between the Web service.
(2) realization is based on the Web service semantic tagger of DBpedia associated data.The key problem of mask method is how to utilize the associated data resource to increasing the semanteme of Web service, more particularly, is how suitable DBpedia URI to be mapped on the input and output parameter of Web service.Therefore, this semanteme marking method mainly comprises following steps:
2-1 is, and Web service is resolved, and mainly comprises following two tasks:
It is legal in the significant WSDL document of user to verify;
For each Web service, resolve the element of three semantic levels describing in the above-mentioned marking model of acquisition.
The Web service parameter of 2-2 layer carries out parameter and refines.For the input and output parameter collection that parsing obtains, the complex parameters type of decomposition that will have structure is simple parameter.
The Web service parameter of 2-3 layer carries out parameter and cleans.Because in the WSDL document, parameter name is irregular word, and not the form with participial construction, but write the two or more syllables of a word together or abbreviation, therefore, the parameter cleaning process is necessary, by to the service parameter analysis in the last WSDL document, the present invention proposes to handle the method for service parameter title, may further comprise the steps:
E. according to input and output information acquisition keyword.The input and output parameter of many services has the name form of getAbyB, and the output parameter that perhaps has is the form of getB, and obviously, in this case, A is the keyword of input parameter, and B is the keyword of output parameter;
F. realize participle.Because do not have the space in the definition of parameter name, we need carry out participle to parameter according to initial caps.In addition, also need to filter punctuation mark and link symbol;
G. filter insignificant word.Obtained one group of independently word after the participle, some in them are nonsensical, need be left in the basket.To surpass 90% word be nonsensical to the frequency of occurrences in the WSDL document, because their difference between being difficult to distinguish.The nonsense words table is safeguarded in our database;
H. recover abb..Another table of safeguarding in our database is the dictionary of abbreviations table, and the final step of processing parameter is by comparing the dictionary of abbreviations table, the abb. in the reduction word array.
2-4 utilizes DBpedia Spotlight to use, on parameter association to the suitable DBpedia instance data after each cleaning of Web service.
2-5 corresponds to the parameter of Web service on the Ontological concept of DBpedia.
In the whole process, the mark vocabulary has recorded all service markup informations, the parameter that before handling, marked, the mark vocabulary can play a role, simultaneously, along with constantly carrying out of mark, the mark vocabulary constantly enriches and is perfect, finally can become the knowledge base of Web service and associated data relation.
(3) analyze experimental result according to the index of the evaluation framework that proposes.Corresponding to the semanteme marking method based on the DBpedia associated data, this evaluation framework mainly comprises annotation results (RS) and two index: RS=＜A of evaluation result (EV), B, and S, I, E, W＞, in this hexa-atomic group:
A is the set of all service parameters.
B has the set of the service parameter of example and Ontological concept mark for by behind this paper algorithm mark.
S is by behind this paper algorithm mark, the set of the service parameter that the instance concepts of mark are correct.
I is by behind this paper algorithm mark, the set of the service parameter that the Ontological concept of mark is correct.
E is for by behind this paper algorithm mark, and parameter is similar in appearance to the set of the service parameter of the mark example of correspondence or Ontological concept.
W is by behind this paper algorithm mark, the set of the service parameter of the instance concepts mistake of mark.
EV=＜Ar,Re,Am＞
Ar is the mark rate (AnnotationRate) of service mark, Ar=|B ∩ A|/| A|.
Re is the degree of accuracy (Precision) of service mark, Re=| (S ∪ I) ∩ A|/| B|.
Am is the blur level (Ambiguity) of service mark, Am=|E ∩ A|/| B|.
The present invention mainly is the DBpedia knowledge base of utilizing as the associated data core data set, the model of Web service semantic tagger is proposed, realize a kind of automatic Web service semanteme marking method on this basis, and the method is proposed a kind of semantic tagger evaluation framework.Now, will set forth the embodiment of these three aspects.
Web service marking model based on the DBpedia associated data mainly comprises two parts, as shown in Figure 1.Be positioned at straight dashed line top and be the Web service on the semantic level, comprise following three layers:
Service layer: refer to the most original Web service.This layer comprised the essential information of a service, as the title of service, and the agreement that service is used, the URI of service, the descriptor of service, the execution time of service etc.A service comprises the interface that one or more realizes service function at least, represents as the number line among Fig. 1.
Interface layer: interface is the basic functional units of a Web service, has vital role in the process of service discovery and combination.Interface layer has also comprised all functions that Web service can provide.
The parameter layer: parameter derives from the input and output of Web service.The input of service refers to a Web service for the needed information of the answer that obtains expecting, the information that provides after the Web service response request is provided in the output of service.Simple parameter is the leaf node of a Web service.The parameter layer is add semantic place, just Biao Zhu main place.
Another part is the DBpedia body, comprises following two-layer:
DBpedia example: refer to the millions of entity that from wikipedia, extracts, the URI of DBpedia example is that the mode with http://dbpedia.org/resource/name defines, they have comprised encyclopaedia theme very widely, form by the RDF tlv triple is interrelated, and most instance data is to be mapped in the DBpedia Ontological concept.The DBpedia instance data has brought semanteme for the parameter layer.
The DBpedia class: the DBpedia body of latest edition has comprised 359 classes, is defined within the http://dbpedia.org/ontology/ NameSpace.At this layer, we can inquire about and reasoning the DBpedia body, thereby excavate potential relation between the service, and this also is the important step that makes up the Web service semantic network.
Based on the key step of the Web service semanteme marking method of DBpedia as shown in Figure 2.At first be according to marking model Web service to be verified and resolved.Have parameter layer, operation layer and service layer by the Web service after resolving, wherein, the parameter concept is the key of Web service semanteme.Be the basic procedure that associated data is matched the parameter concept in the frame of broken lines among Fig. 2.For each parameter concept, inquire about local vocabulary and whether have this parameter, if having, the result who then returns storage then marks end; Otherwise, then refine and cleaning parameters, for the parameter after handling, add its corresponding interface description, as the input of DBpedia Spotlight, thus the DBpedia instance data that obtains marking, inquiry obtaining corresponding Ontological concept again.At last, the result with mark deposits in the local vocabulary.The process of the semantic tagger of this concept that gets parms is seen algorithm 1.
Web service is obtained the process of semantic information shown in algorithm 2.At first obtain the total interface of this Web service, the parameter of each interface of reentrying is judged the type of parameter, if parameter is simple parameter, then calls and shown in the algorithm 1 the parameter concept is obtained the process of semantic information; If complex parameters then needs to find all simple parameters that define this parameter among the WSDL, recycling algorithm 1 obtains semantic.By above-mentioned steps, can obtain to be mapped with the Web service of associated data semanteme.
The process that matches the parameter concept from the DBpedia example as shown in Figure 3.Service by http protocol client utility bag HTTPClient request DBpedia Spotlight, by configurating filtered parameter, with refine and clean after parameter and interface describe as importing, the content of DBpedia Spotlight by returning an XML form is as corresponding, and be as follows:
<Resource?URI="http://dbpedia.org/resource/Tax_rate"support="44"
types=""surfaceForm="tax?rate"offset="54"
similarityScore="0.12459990382194519"percentageOfSecondRank="-
1.0"/>
<ResourceURI="http://dbpedia.org/resource/Individual"support="312"
types=""surfaceForm="individuals"offset="67"
similarityScore="0.13186876475811005"
percentageOfSecondRank="0.09825822049066152"/>
We recycling Domj4 extracts the DBpedia instance data from the XML file, by inquiry DBpedia body RDF type statements, instance data is mapped in the DBpedia Ontological concept.Because part DBpedia URI is not corresponded in the Ontological concept, some examples just can not find corresponding Ontological concept, in this case, we need use the DISCO calculated examples remember with gratitude and Ontological concept between distribution similarity in the wikipedia corpus, thereby select the Ontological concept of similarity maximum as the mark of parameter.
In order to estimate and verify applicability and validity based on the mask method of DBpedia associated data, we at this mask method the evaluation framework has been proposed, comprise hexa-atomic group annotation results RS and evaluation result EV.Among the RS, can obtain, B=S ∪ I ∪ E ∪ W, and A-B represents the set of the service parameter that can not be marked.In the evaluation index of EV, Re is service mark important evaluating index, has reflected validity and the accuracy of service mark well; Ar has then reflected for the applicability of a certain data set dimensioning algorithm and stability, has also just reflected the ability of mark; Am has reflected the ability of the out of true mark of dimensioning algorithm, though similar annotation results has not been added accurate semantic information for service, this result can be used for service fuzzy query or service recommendation.
This paper has realized the Web service semanteme marking method based on the DBpedia associated data of foregoing description at two data sets, and has utilized proposition to estimate framework and come method is estimated.These two data sets are respectively 300 true available Web services (Travel300) relevant with tour field grasping from the internet, had the semantic OWL-S service retrieval test set (edition 4 that has 9 fields, OWLS-TC4), three layers concrete quantity is shown in Table 1 in the semantic model.Experimental situation is Windows7,3G internal memory, Myeclipse9, Mysql5.5, and DBpedia3.7.
Table 1 experimental data details
| Title | Travel300 | OWLS-TC4 |
| Service (individual) | 300 | 1090 |
| Interface (individual) | 1897 | 1090 |
| Parameter (individual) | 27568 | 3175 |
Use above-mentioned mask method, we can obtain evaluation result RS, see Table shown in 2, can calculate annotation results evaluation EV by RS and be shown in Table 3.As can be seen from Table 3, this dimensioning algorithm has high mark rate, the Web service among the OWLS-TC4, and its mark rate has reached 100%.For Travel300, the visible bigger blur level of the result of its simple parameter, degree of accuracy is not high; Annotation results for complex parameters is more considerable, higher degree of accuracy is arranged, and for the annotation results of OWLS-TC4 higher accuracy is arranged.The OWLS-TC4 annotation results is better than the annotation results of tour field, possible reason, the one, DBpedia body itself is cross-cutting, the concept that defines in its body is also more summarized with respect to domain body, so very tiny parameter concept in a lot of field Web services, simple parameter particularly just is difficult to find in DBpedia and its accurate corresponding concept, this also be why among the Travel300 mark of complex parameters than the mark of simple parameter main cause more accurately.And 1090 services packages of OWLS-TC4 are contained in 9 fields, and the field is extensive relatively under the service.On the other hand, among the Travel300, the input of a lot of services can not be expressed its function, the abb. that has some to judge, and standard is compared in the definition of the service parameter among the OWLS-TC4, thus its annotation results is better relatively.
Table 2 annotation results
Table 3 evaluation result
The service parameter number of the marking error of the interface correspondence of Web service is seen Fig. 4, can see from (a), and for tourism weather, the interface more than 1/3 is that mark is rational, and 83.03% interface error number of parameters is to be less than to equal 3.In the remaining interface that is less than 2/3 wrong mark, have only 1 parameter marking error in 41.74% the interface, and for the increase of the number of parameters of marking error, corresponding interface is fewer and feweri.Result for the OWLS-TC4 data set is more considerable, and 83.85% interface mark is errorless.As seen, for most service, this mask method can access high mark rate and higher accuracy rate.
Claims (3)
1. the Web service semanteme marking method based on associated data is characterized in that, comprises the steps:
(1) foundation is based on the Web service marking model of DBpedia associated data;
(2) realization comprises following steps based on the Web service semantic tagger of DBpedia associated data:
2-1 is, and Web service is resolved, and mainly comprises following two tasks:
It is legal in the significant WSDL document of user to verify;
For each Web service, resolve the element of three semantic levels describing in the above-mentioned marking model of acquisition;
The Web service parameter of 2-2 layer carries out parameter and refines: for the input and output parameter collection that parsing obtains, the complex parameters type of decomposition that will have structure is simple parameter;
The Web service parameter of 2-3 layer carries out parameter and cleans;
2-4 utilizes DBpedia Spotlight to use, on parameter association to the suitable DBpedia instance data after each cleaning of Web service;
2-5 corresponds to the parameter of Web service on the Ontological concept of DBpedia.
2. the Web service semanteme marking method based on associated data as claimed in claim 1 is characterized in that the parameter cleaning process may further comprise the steps:
A. according to input and output information acquisition keyword, the input and output parameter of many services has the name form of getAbyB, and the output parameter that perhaps has is the form of getB, and A is the keyword of input parameter, and B is the keyword of output parameter;
B. realize participle: owing to do not have the space in the definition of parameter name, need carry out participle to parameter according to initial caps, in addition, also need to filter punctuation mark and link symbol;
C. filter insignificant word, obtained one group of independently word after the participle, some in them are nonsensical, need be left in the basket, and the nonsense words table is safeguarded in database;
D. recover abb., another table of safeguarding in our database is the dictionary of abbreviations table, and the final step of processing parameter is by comparing the dictionary of abbreviations table, the abb. in the reduction word array.
3. the Web service semanteme marking method based on associated data as claimed in claim 1, it is characterized in that, index according to the evaluation framework that proposes is analyzed experimental result, and corresponding to the semanteme marking method based on the DBpedia associated data, this evaluation framework mainly comprises annotation results (RS) and two index: RS=＜A of evaluation result (EV), B, S, I, E, W＞, in this hexa-atomic group:
A is the set of all service parameters;
B has the set of the service parameter of example and Ontological concept mark for by behind this paper algorithm mark;
S is by behind this paper algorithm mark, the set of the service parameter that the instance concepts of mark are correct;
I is by behind this paper algorithm mark, the set of the service parameter that the Ontological concept of mark is correct;
E is for by behind this paper algorithm mark, and parameter is similar in appearance to the set of the service parameter of the mark example of correspondence or Ontological concept;
W is by behind this paper algorithm mark, the set of the service parameter of the instance concepts mistake of mark;
EV=＜Ar,Re,Am＞
Ar is the mark rate (AnnotationRate) of service mark, Ar=|B ∩ A|/| A|;
Re is the degree of accuracy (Precision) of service mark, Re=| (S ∪ I) ∩ A|/| B|;
Am is the blur level (Ambiguity) of service mark, Am=|E ∩ A|/| B|.
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