Abstract. The increasing importance and use of Web services have resulted in a number of efforts targeted at automating Web service discovery and composition based on semantic descriptions of their properties. However, the progress in the automation of Web service discovery is still held back by the fact that the description of Web services in terms of semantic metadata is still mainly manually. This Ph.D. thesis addresses this problem by developing an approach for the acquisition and management of semantic Web service descriptions in order to facilitate efficient service discovery and composition. Specifically, this involves the collection of information about a Web service, the acquisition of semantic descriptions based on the collected information, and the structured storage of the generated semantic descriptions. 

Maleshkova, Maria

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Open Research OnlineThe Open University’s repository of research publicationsand other research outputsAcquisition and management of semantic web servicedescriptionsConference or Workshop ItemHow to cite:Maleshkova, Maria (2008). Acquisition and management of semantic web service descriptions. In: The ESWC2008 PhD Symposium, 2 Jun 2008, Tenerife, Spain.For guidance on citations see FAQs.c© 2008 Springer-VerlagVersion: Accepted ManuscriptCopyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyrightowners. For more information on Open Research Online’s data policy on reuse of materials please consult the policiespage.oro.open.ac.ukAcquisition and Management of Semantic WebService DescriptionsMaria MaleshkovaInstitute of Applied Informatics and Formal Description Methods (AIFB),University of Karlsruhe (TH), Germanymaleshkova@aifb.uni-karlsruhe.deAbstract. The increasing importance and use of Web services have re-sulted in a number of efforts targeted at automating Web service discov-ery and composition based on semantic descriptions of their properties.However, the progress in the automation of Web service discovery is stillheld back by the fact that the description of Web services in terms ofsemantic metadata is still mainly manually. This Ph.D. thesis addressesthis problem by developing an approach for the acquisition and manage-ment of semantic Web service descriptions in order to facilitate efficientservice discovery and composition. Specifically, this involves the collec-tion of information about a Web service, the acquisition of semantic de-scriptions based on the collected information, and the structured storageof the generated semantic descriptions.1 IntroductionA clearly developing trend in the recent years is towards exposing the functional-ities provided by existing software components in the form of services and facil-itating in this way software reuse and added value through service composition.This development is strongly supported by the Web, which enables ubiquitousaccess via a set of Web standards and protocols to software components resid-ing on different platforms. As a result, Web services are seen increasingly asthe basic construct for the development of rapid, low-cost and easy-to-composedistributed applications in heterogeneous environments.When it comes to finding appropriate services and composing distributedapplications, current technologies still require a large amount of human interac-tion. This results in the restricted number of use cases for service integration andthe limited scalability of solutions involving manual activities in the process ofservice discovery and compositions. In order to address these problems, the ideaof supplementing Web services with a semantic description of their functionality,that could facilitate their discovery and integration, has been developed. How-ever, the information, on which these semantic descriptions are based, is rarelydiscussed. In addition, there is still no established method for acquiring semanticWeb service descriptions, which not only describe the functionality but also spec-ify functional and non-functional properties in a way that supports automaticmatchmaking processes. Therefore, there is the strong need of developing an au-tomatic approach, which enables the acquisition and management of semanticWeb Service descriptions with the goal of supporting efficient service discoveryand composition.Technical Service DescriptionsNatural Language DescriptionsUser-provided Tags, Ratings, CommentsExtraction of Functional and Non-Functional PropertiesSemantic Web ServiceDescriptions123Fig. 1. Acquisition and Management of Semantic Web Service DescriptionsThis Ph.D. thesis targets to explore the problem of semi-automatically ac-quiring and publishing semantic descriptions for Web services and aims to useSemantic Web techniques to develop a methodology to enhance the situation.Figure 1 shows the three main challenges connected with the acquisition andmanagement of semantic Web Service descriptions.1. How can information about Web services be collected? To answerthis question, the relevant sources of information have to be identified, in-cluding not only technical and text descriptions, but also information in-spired by Web 2.0 such as user tags, ratings and comments. After this, au-tomatic mechanisms for data collection from the Web have to be developed.2. How can semantic Web service descriptions be automatically ac-quired? This question is in the focus of the Ph.D. thesis and is addressedby exploring existing methods for metadata acquisition and identifying theiradvantages and disadvantages. The main goal is to develop a method, whichsemi-automatically acquires metadata by extracting functional and non-functional service properties in order to facilitate discovery and composition.3. How can semantic Web service descriptions be stored? The acquiredsemantic descriptions have to be stored in a way that facilitates the manualas well as the automatic matchmaking processes. It is important to employthe metadata’s syntactical or semantical interconnections in order to enableindexing and efficient service search.2 Related WorkThere is already some work done related to the automatic creation of semanticWeb service descriptions. In particular, there are two main areas of research: theacquisition of a suitable Web service domain ontology and the actual process ofannotating Web services. Sabou et al. [1] present two ontology building processin the context of two concrete research projects, revealing some of the majoraspects that make Web service ontology building difficult.Focusing on the Web service annotation task, Patil et al. [2] apply graphsimilarity techniques to select a relevant domain ontology for a given WSDL filefrom a collection of ontologies. Hess and Kushmerick [3] employ Naive Bayes andSVM machine learning methods to classify WSDL files in manually defined taskhierarchies. However, none of the developed approaches focuses on facilitatingWeb service discovery by specifying functional and non-functionally propertiesand in the same time taking into consideration temporal conditions on effects,trust and access control policies.There are a number of state of the art Web service repositories includingUDDI, Bindingpoint, .NET XML Web Services Repertory, WebserviceX.NET,Web Service List, Xmethods and SalCental. An overview of these repositoriespresented in [4] shows that in contrast to traditional software libraries, Webservice repositories rely on little metadata to support service discovery, mainlybecause of the difficulty of automatically deriving metadata describing Web ser-vice collections. In order to overcome the lack of metadata, there are a number ofapproaches which aim to enhance existing Web service repositories, in particularUDDI, with complex semantic markup [5]. Still, the existing repositories do nottarget the structured storage of semantic Web service descriptions in order tofacilitate the effective service discovery but rather use semantic information asan extension to stored descriptions.3 ContributionsA number of Web service tasks can be automated by using semantic descrip-tions. In particular, service offers and requests can be matched automatically.However, semantic descriptions still have to be manually generated. The ma-jor goal of this Ph.D. thesis is to develop an semi-automatic method for theacquisition of semantic descriptions of Web services, based on metadata andtags extracted from the Web. The resulting semantic descriptions will be storedin an online semantic Web service descriptions repository, which can be usedfor both manual and automatic service discovery. The acquisition of semanticdescriptions facilitates matchmaking approaches and can reduce the requiredhuman interaction to a minimum. Especially in use cases, where Web servicesare used as building blocks in distributed applications or where Web servicesprovide functionality integrated in business processes, the automated derivingof semantic description plays a key role.There are three main contributions, which are to be archived. First, the au-tomatic collection of information describing a Web service is facilitated. Second,semantic descriptions of Web services are semi-automatically acquired from thecollected information, in the form of semantic annotations. Finally, Web servicesemantic annotations are stored in an online repository, which provides semanticindexing to allow for efficient queries and support matchmaking processes. Eachof these contributions is described in detail in the following sections.Web Service Crawlers are developed to support the automatic collec-tion of information about Web services. Web service descriptions are usuallygiven in a standardized form such as WSDL. Unfortunately, WSDL providesmainly technical description of services and this information is insufficient forautomatic service discovery because it presumes increased human interaction.Therefore, it is necessary to automatically collect additional information. A Webservice crawler can effectively be used to perform this task. In particular, this in-volves the identification of relevant sources and types of Web service information.Sources include natural language text descriptions, for example, documentationon provider Web-sites, and technical Web service descriptions such as WSDL, orWSMO variants. In addition, sources of information inspired by Web 2.0 suchas user tags, ratings, and community inputs are also considered. The definitionof the Web service crawlers is based on semantic attributes for service descrip-tion, based on information needed for service discovery and composition. Thisinvolves mainly the specification of functional and non-functional Web serviceproperties.Web Service Semantic Annotations comprise the acquired semantic Webservices descriptions, which are based on the data collected by the Web servicecrawlers. The main focus of this Ph.D. thesis is on developing a mechanismfor the acquisition of semantic descriptions from the collected data, based on adetermined formalism for the specification of semantic information. This mech-anism is developed by using existing techniques for data mining and ontologylearning and thereby, automatically constructing service annotations. In addi-tion, the formalism, chosen as the basis for the semantic annotation, annotatesfunctional and non-functional properties of Web services in a unifying way. Theapproach described in [6] will be used as a basis where semantic, temporal andsecurity constraints are described by using a combination of the pi-calculus anddescription logics and will be adjusted based on requirements resulting from thespecification of the developed mechanism for acquisition of semantic descriptions.Semantic Indexing is necessary in order to facilitate efficient queries ontop of the semantic annotations. The structured storage of the acquired seman-tic annotations is targeted to improve performance of automatic matchmakingprocesses and speed up manual search. During semantic indexing, the collectionof Web service semantic annotations will be analyzed and organized in a waythat allows the efficient answering of expressive queries.4 EvaluationThe planned evaluation consists of three main steps. First, the developed mech-anism for data collection is evaluated by determining a fixed domain of sourcedata and assessing how much data was correctly retrieved. This evaluation caneasily be performed by using the well-established recall/precision metrics. Sec-ond, semantic annotations evaluation assesses the performance of extracting Webservice properties relevant for matchmaking from the corpus of collected data.Naturally, the quality of semantic annotation extraction has a direct influenceon the quality of the service discovery and composition, which are based onthese semantic annotations. This evaluation can be done by comparing the Webservice functional and non-functional properties present in the collected datato the ones present in the acquired semantic descriptions. Third, the chosenstructure for storage of Web service semantic annotations is evaluated by com-paring the performance of matchmaking algorithms ran on the repository to theperformance of these matchmaking algorithms ran on the same semantic anno-tations but in unstructured list collection. This evaluation will point out whatimprovements the developed repository structure brings in means of search per-formance. Finally, the best evaluation for the developed approach would be themaking public of the semantic annotations repository so that other researcherscan use it to test their discovery and composition algorithms.5 Work PlanThe work plan is divided into three stages, each stage marking one year of thePh.D. studies. Initialization State of the art report on formalisms for Web ser-vice semantic descriptions, in the context of service discovery (M6). State of theart report on data-mining techniques and ontology learning (M6). State of theart report on possible structures for semantic annotations storage (M6). Identi-fied sources of Web service information (M12). Identified formalism for semanticannotations (M12). Development Definition of semantic attributes for the Webservice crawlers. First Web service crawlers prototype (M18). Identified struc-ture for annotations storage (M18). Identified data-mining and ontology learningapproach (M24). First prototype implementation of acquisition of semantic an-notations (M24). Evaluation of the first prototypes (M24). Refinement Firstprototype of semantic annotations repository (M30). Refined methodology basedon the first prototypes (M30). Refined implementation (M36). Evaluation (M36).References1. Sabou, M., Wroe, C., Goble, C., Stuckenschmidt, H.: Learning Domain Ontologiesfor Semantic Web Service Descriptions. Journal of Web Semantics 3(4) (2005)2. Patil, P., Oundhakar, S., Sheth, A., Verma, K.: METEOR-S Web service Annota-tion Framework. In Proceedings of the 13th World Wide Web Conference (2004)3. Hess, A., N. Kushmerick, N.: Machine Learning for Annotating Semantic WebServices. In AAAI Spring Symposium on Semantic Web Services (March 2004)4. Sabou M., Pan, J.: Toward Improving Web Service Repositories through Seman-tic Web Techniques. Workshop on Semantic Web Enabled Software Engineering(SWESE) at ISWC (November 2005)5. Akkiraju, R., Goodwin, R., Doshi, P., Roeder, S.: A Method for SemanticallyEnhancing the Service Discovery Capabilities of UDDI. IIWeb 87-92 (2003)6. Agarwal, S., Studer, R.: Automatic Matchmaking of Web Services. InternationalConference on Web Services (ICWS’06). IEEE Computer Society (September 2006)

Acquisition and management of semantic web service descriptions

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Acquisition and management of semantic web service descriptions

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