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 • Hybrid Matching – Classification – Hybrid Algorithms – Experiments – Involved Data • Related Work

Web Service Discovery and Selection ish d Preferences User experience bl Service Interface Descriptions Bin Service Quality (UDDI, eb. XML) Pu Service Behavior Descriptions Service Registry Service Description Service Provider Service Find Service Requestor Service-oriented application

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

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.

Web Service Description Language (WSDL) Google. Search. wsdl Wolfram. Search. wsdl <message name=”do. Google. Search. Response”> <part name=”return” type=”Google. Search. Result”/> </message>. . . <complex. Type name=”Google. Search. Result”> <all> <element name=”search. Comments” type=”string”/> <element name=”estimated. Total. Results. Count” type=”int”/> <element name=”result. Elements” type=”Result. Element. Array”/>. . . < /all> < /complex. Type> <message name=”Wolfram. Search. Response”> <part element=”Wolfram. Search. Return”/ > </message>. . . <element name=”Wolfram. Search. Return”> <complex. Type> <sequence> <element name=”Result” type=”Wolfram. Search. Result”/ > < /sequence> < /complex. Type> < /element>. . . <complex. Type name=”Wolfram. Search. Result”> <sequence> <element name=”Total. Matches” type=”int”/ > <element name=”Comment” type=”string”/ > <element name=”Matches” type=”Wolfram. Search. Match. Array”/ >. . . < /sequence> < /complex. Type>

Semantic Web Service Description • • • Managing End-To-End Ope. Rations (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 • …

WSDL-S (Semantics) Stock. Quotes. wsdls … xmlns: Ontology 4=http: //lsdis. cs. uga. edu/projects/meteor-s/wsdls/ontologies/LSDIS_Finance. owl xmlns: wssem="http: //www. ibm. com/xmlns/Web. Services/WSSemantics"> … <message name="Get. Quotes. Soap. In"> <part name="Quote. Ticker" type="string" wssem: model. Reference="Ontology 4#Stock. stock. Symbol"/> </message> … <operation name="Get. Stock. Quotes" wssem: model. Reference="Ontology 4#Delayed. Stock. Quote"> <input name="Get. Quotes" message="Get. Quotes. Soap. In" /> … </operation> …

Ontology Web Language (OWL) LSDIS_FInance. owl … <owl: Object. Property rdf: ID="stock. Symbol"> <rdfs: range rdf: resource="#Symbolic. String"/> <rdfs: domain rdf: resource="#Stock"/> </owl: Object. Property> … <owl: Object. Property rdf: ID="stock"> <rdfs: range> <owl: Class> <owl: union. Of rdf: parse. Type="Collection"> <owl: Class rdf: about="#Stock"/> <owl: Class rdf: about="#Security"/> </owl: union. Of> </owl: Class> </rdfs: range> <rdfs: domain rdf: resource="#Securities. Transaction"/> </owl: Object. Property>

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).

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

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

Lexical Matching 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 Tokenization – Example: ”tns: Get. DNSInfo. By. Web. Address. Response” {tns, get, dns, info, by, web, address, response}. 1. Vector-Space Model (VSM) (tf-idf) 2. VSM + synonyms from the Word. Net 3. Semantic – Seco, N. , Veale, T. , Hayes, J. : “An intrinsic information content metric for semantic similarity in Word. Net”, ECAI, 2004, pp. 1089 -1090

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 (Word. Net-based) • Word. Net 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 Word. Net synsets • Word. Net provides a limited set of relations (hyponyms, synonyms…) • Matching confidence often is very low

Matching results: service rating list Web service (. wsdl file) Hu. Zip. wsdl Query Currency. Exchange. Service. wsdl Services. wsdl Get. Currency. Exchange. wsdl Fast. Weather. wsdl … Zip. Distance. wsdl Similarity relevance score 1. 0 + 0. 121 0. 114 0. 083 0. 080 + - 0. 036 + Matching confidence

Hybrid Algorithms Hybridization Algorithms Mixed Data Switching Augmentation Combination Cascade

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

Hybrid Algorithms: Examples Mixed XH 1(q; γ) = {sim. H 1(q; x) > γ}, where sim. H 1(q; x) = F{sim. Sy(q; x), sim. Se(q; x)}, F(a, b)={min(a, b), max(a, b), w 1 a+w 2 b | w 1+w 2 = 1; 0 ≤ w 1; w 2 ≤ 1 • Cascade XH 2(q; γ) = {x | sim. H 2(q; x) > γ}, where sim. H 2(q; x) = sim. Sy(q; x), x XSe(q; γ)

Experimental Results 1. 2. 3. 4. Min(sim. Sy(q; x), sim. Se(q; x)) Max(sim. Sy(q; x), sim. Se(q; x)) 0. 8 sim. Sy(q; x) +0. 2 sim. Se(q; x) 0. 6 sim. Sy(q; x) +0. 4 sim. Se(q; x)

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

Client knowledge 24 / 40 services exist How many of them provide feasible results? <respond> Web Service <register> <respond> Registry Proxy (Axis) <invoke> Application Community <report> IC-Service <develop> <query> <recommend> <feedback> 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.

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

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 Qo. S parameters), – Submit query (request, preferences)

Matching + User experience Relevant Matching algorithm IC-Service: invoke-response S 2 S 1 0. 8 S 3 S 2 0. 75 S 2 0. 5 S 4 S 3 0. 5 S 1 0. 3 S 1 S 4 0. 25 S 4 0. 2 Final ranking IC-Service: feedback 0 -1 S 2 0. 75 S 3 0. 6 S 4 0. 5 S 1 0. 36 S 1 0 S 4 0. 31

Functional knowledge • Query knowledge-based reasoning response – Vocabularies • currency exchange [currency country] get. Exchange. Rate(country 1, country 2) – Composition • currency exchange get. Country. By. Currency(currency) + get. Exchange. Rate(country 1, country 2)

Matching: Related Work 1. 2. 3. 4. 5. 6. 7. 8. 9. [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 p 2 p 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.

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. ”

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.