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Leveraging Web Services Discovery with Customizable Hybrid Matching

Leveraging Web Services Discovery with Customizable Hybrid Matching. Natallia Kokash, Willem-Jan van den Heuvel (University of Tilburg, the Netherlands), Vincenzo D'Andrea (ICSOC 2007). Introduction. Web Service Discovery Web Service Interface Description Web Service Matching

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Leveraging Web Services Discovery with Customizable Hybrid Matching

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  1. Leveraging Web Services Discovery with Customizable Hybrid Matching Natallia Kokash, Willem-Jan van den Heuvel (University of Tilburg, the Netherlands), Vincenzo D'Andrea (ICSOC 2007)

  2. Introduction • Web Service Discovery • Web Service Interface Description • Web Service Matching • Hybrid Matching • Classification • Hybrid Algorithms • Experiments • Involved Data • Related Work

  3. Service Registry (UDDI, ebXML) Bind Publish Find Service Provider Service Requestor Web Service Discovery and Selection Service Description Service Behavior Descriptions Service Interface Descriptions Service Quality Preferences User experience Service Service-oriented application

  4. Web Services Discovery • Matching – meeting the functionality required by a user with specifications of existing services • Generic (heuristics, domain-independent ontologies) • Community (domain-specific ontologies) • Personal (preferences, specific functions and patterns for comparing requests and existing services) • Selection – choosing a service with the best quality among those able to satisfy a user goal

  5. Service Interface Description • (Syntactic) • Identity – unique identity of the interface • Input/Output– the meaning of input and output parameters • Faults – specify the abstract message format for any error messages that may be output as the result of the operation • Types – declare data types used in the interface (XML Schema) • Documentation – natural language service description and usage guide • (Semantic) • Preconditions – a set of semantic statements that are required to be true before an operation can be successfully invoked • Effects – a set of semantic statements that must be true after an operation completes execution after being invoked. • Restrictions – a set of assumptions about the environment that must be true • Quality of Service – non-functional parameters such as response time, execution cost, capacity, etc.

  6. Web Service Description Language (WSDL)

  7. Semantic Web Service Description • Managing End-To-End OpeRations (METEOR-S) • Semantic Web Services Framework (SWSF) • Web Service Modelling Ontology (WSMO) • Ontology Web Language for Services (OWL-S) • Semantic Web Rule Language (SWRL) • Web Service Description Language – Semantics (WSDL-S) • The semantics of operations are directly added to WSDL files • Easy to deploy and use • Does not support full features of OWL-S process ontology • Universal Service-Semantics Description Language (USDL) • Based on Web Ontology Language (OWL) • Employs Word-Net as a common basis for understanding the meaning of services • Side effects: create, update, delete, find, affects • …

  8. WSDL-S (Semantics) StockQuotes.wsdls … xmlns:Ontology4=http://lsdis.cs.uga.edu/projects/meteor-s/wsdl-s/ontologies/LSDIS_Finance.owl xmlns:wssem="http://www.ibm.com/xmlns/WebServices/WSSemantics"> … <message name="GetQuotesSoapIn"> <part name="QuoteTicker" type="string" wssem:modelReference="Ontology4#Stock.stockSymbol"/> </message> … <operation name="GetStockQuotes" wssem:modelReference="Ontology4#DelayedStockQuote"> <input name="GetQuotes" message="GetQuotesSoapIn" /> … </operation> …

  9. Ontology Web Language (OWL) LSDIS_FInance.owl … <owl:ObjectProperty rdf:ID="stockSymbol"> <rdfs:range rdf:resource="#SymbolicString"/> <rdfs:domain rdf:resource="#Stock"/> </owl:ObjectProperty> … <owl:ObjectProperty rdf:ID="stock"> <rdfs:range> <owl:Class> <owl:unionOf rdf:parseType="Collection"> <owl:Class rdf:about="#Stock"/> <owl:Class rdf:about="#Security"/> </owl:unionOf> </owl:Class> </rdfs:range> <rdfs:domain rdf:resource="#SecuritiesTransaction"/> </owl:ObjectProperty>

  10. Semantic Web Services… • Vast semantic descriptions are required • Fits only easily formalized domains • High complexity of matching • Do not describe what services do • Do not provide context identification • Do not describe objects used by the service but not provided by the client • Logic-based approaches are difficult to use • Do not reflect real world scenarios sufficiently • Users must guess which ontology is used to write requests • … Open Directory Project (human-edited directory of the Web, constructed and maintained by a vast community of volunteer editors) vs. (Google) search engine that tries to find a document that can satisfy the information need (regardless of its format).

  11. Matching Evaluation Hybrid VSM Semantic Other WS Matching Algorithms Kokash, N.: "A Comparison of Web Service Interface Similarity Measures", Proceedings of STAIRS, 2006, pp. 220--231. Registry Parsing Tagging Indexing Query Ontology Meta- data http://dit.unitn.it/~kokash/sources

  12. Requested service Requested operation Similarity ? Provided operation Provided service Name, Description, Operations …. Name, Description, Operations … A X B Y Z Web Service Web Service wij wij Similarity ? Name, Description, Input, Output … Name, Description, Input, Output …. a x b y c Operation Operation Structural Matching Maximum weight bipartite matching: Kuhn’s Hungarian method Define overall similarity score: Matching average, Dice, Simpson coefficients…

  13. Service Operation Message Part Type Element Name Operations Description Name Input message Output message Description Name Parts Description Name Type Description Name Elements Description Name Description Lexical Matching Tokenization • Example: ”tns:GetDNSInfoByWebAddressResponse”  {tns, get, dns, info, by, web, address, response}. • Vector-Space Model (VSM) (tf-idf) • VSM + synonyms from the WordNet • Semantic • Seco, N., Veale, T., Hayes, J.: “An intrinsic information content metric for semantic similarity in WordNet”, ECAI, 2004, pp. 1089-1090

  14. Experimental Results • Collection 1 • Criterion: One semantically equivalent operation • 40 web services, 5 groups • Collection 2 • Criterion: Related domains • 371 web services, 68 groups • VSM is better than Semantic (WordNet-based) • WordNet is too general • Lexical difference between queries and existing services • The Longman defining vocabulary • make it easier to create logically precise definitions • 2 200 words (~4 000 senses) • Corresponds to 10 000 WordNet synsets • WordNet provides a limited set of relations (hyponyms, synonyms…) • Matching confidence often is very low

  15. Matching results: service rating list Query Matching confidence

  16. Hybrid Algorithms Hybridization Algorithms Data Mixed Switching Augmentation Combination Cascade

  17. Notation • q – query • x – web service (operation, message, part, data type, element) • γ – similarity threshold • simA(q; x) – similarity between a query q and a web service x • XA(q; γ) = {x | simA(q; x) > γ} denotes a set of services (operations,…) found by the algorithm A. • simSy(q; x) – syntactic similarity • simSe(q; x) – semantic similarity

  18. Hybrid Algorithms: Examples Mixed XH1(q; γ) = {simH1(q; x) > γ}, where simH1(q; x) = F{simSy(q; x), simSe(q; x)}, F(a,b)={min(a,b), max(a,b), w1a+w2b | w1+w2 = 1; 0 ≤ w1;w2 ≤ 1 • Cascade XH2(q; γ) = {x | simH2(q; x) > γ}, where simH2(q; x) = simSy(q; x), x  XSe(q; γ)

  19. Experimental Results • Min(simSy(q; x), simSe(q; x)) • Max(simSy(q; x), simSe(q; x)) • 0.8simSy(q; x) +0.2 simSe(q; x) • 0.6simSy(q; x) +0.4 simSe(q; x)

  20. Hybrid data • Service knowledge • Features of existing services (service documentation, specification, interface description, ontology-based semantic extension, service reputation, monitored information) • Client knowledge • Client's profile: area of expertise, location, history of searches and previously used web services • Functional knowledge • Knowledge required by the matching algorithm to map between the client needs and the services that might satisfy those needs

  21. Web Service Proxy (Axis) Application <respond> <invoke> <develop> Community <register> <report> Registry IC-Service <respond> <query> <recommend> <feedback> Client knowledge 24 / 40 services exist How many of them provide feasible results? Birukou, A., Blanzieri, E., D'Andrea, V., Giorgini, P., Kokash, N., Modena, A.: "IC-Service: A Service-Oriented Approach to the Development of Recommendation Systems", The ACM Symposium on Applied Computing, Special Track on Web Technologies (WT), March 2007.

  22. System for Implicit Culture Support Produce a theory about common user behavior Recommend actions Stores information about actions http://dit.unitn.it/~implicit

  23. Observation of web service invocations • Actors: • Applications (application name, user name, location) • Users (user name, location) • Objects: • Operation (operation name, web service name, category) • Inputs/Outputs (parameter name, parameter value) • Requests (operation names, input/output parameters, category) • Actions: • Bind (timestamp, web service), • Invoke (timestamp, operation, input), • Get response (timestamp, operation, output, response time), • Raise exception (timestamp, operation, exception type, input), • Provide feedback (report about contract violations, domain-specific QoS parameters), • Submit query (request, preferences)

  24. Matching + User experience

  25. Functional knowledge • Query  knowledge-based reasoning  response • Vocabularies • currency exchange[currency  country]  getExchangeRate(country1, country2) • Composition • currency exchangegetCountryByCurrency(currency) + getExchangeRate(country1, country2)

  26. Matching: Related Work • [Sajjanhar’04] Sajjanhar, A., Hou, J., Zhang, Y.: ”Algorithm for Web Services Matching”, APWeb, 2004, pp. 665–670. • [Bruno ’05] Bruno, M., Canfora, G. et al.: ”An Approach to support Web Service Classification and Annotation”, IEEE International Conference on e-Technology, e-Commerce and e-Service, 2005. • [Corella’06] Corella, M.A., Castells, P.: “Semi-automatic semantic-based web service classification”, International Conference on Knowledge-Based Intelligent Information and Engineering Systems, 2006. • [Dong’04] Dong, X.L. et al.: ”Similarity Search for Web Services”, VLDB, 2004. • [Platzer’05] Platzer, C.; Dustdar, S.: “A vector space search engine for Web services”, Proceedings of IEEE European Conference on Web services (ECOWS), 2005. • [Stroulia’05] Stroulia, E., Wang, Y.: ”Structural and Semantic Matching for Accessing Web Service Similarity”, International Journal of Cooperative Information Systems, Vol. 14, No. 4, 2005, pp. 407-437. • [Wu’05] Wu, J., Wu, Z.: ”Similarity-based Web Service Matchmaking”, IEEE International Conference on Services Computing, 2005, pp. 287-294. • [Zhuang’05] Zhuang, Z., Mitra, Pr., Jaiswal, A.: ”Corpus-based Web Services Matchmaking”, AAAI, 2005. • [Verma’05] Verma, K., Sivashanmugam, K., et al.: “Meteors wsdi: A scalable p2p infrastructure of registries for semantic publication and discovery of web services.” Journal of Information Technology and Management. Special Issue on Universal Global Integration, Vol. 6, No.1, 2005, pp. 17-39.

  27. Hybrid algorithms: Related work • Syeda-Mahmood, T., Shah, G., et al.: “Searching service repositories by combining semantic and ontological matching”, International Conference on Web Services, 2005, pp. 13-20. “(1) The domain-independent relationships are derived using an English thesaurus… (2) The domain-specific ontological similarity is derived by inferencing the semantic annotations associated with web service descriptions. …better relevancy results can be obtained for service matches from a large repository, than could be obtained using any one cue alone.” • Klusch, M. Fries, B., Sycara, K.: “Automated Semantic Web Service Discovery with OWLS-MX”, AAMAS, 2006. “…under certain constraints logic based only approaches to OWLS service I/O matching can be significantly outperformed by hybrid ones.”

  28. Hybrid matching: Related work • Rocha, C. et al.: “A Hybrid Approach for Searching in the Semantic Web”, International World Wide Web Conference, 2004, pp. 374-383) • Castells, P., Fernandez, M., Vallet, D.: “An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval”, IEEE Transactions on Knowledge and Data Engineering, 2007, to appear.

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