MWSAF MWSAF METEOR-S METEOR-S Web Web Service Service Annotation Annotation Framework Framework M M ETEOR-S ETEOR-S W W EB EB S S ERVICE ERVICE A A NNOTATION NNOTATION F F RAMEWORK RAMEWORK (MWSAF) (MWSAF) Abhijit Patil, Swapna Oundhakar, Abhijit Patil, Swapna Oundhakar, Amit Sheth, Kunal Verma Amit Sheth, Kunal Verma LSDIS Lab, Department of Computer Science, LSDIS Lab, Department of Computer Science, The University of Georgia The University of Georgia
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MWSAFMETEOR-SWebServiceAnnotationFramework M ETEOR-S W EB S ERVICE A NNOTATION F RAMEWORK (MWSAF) Abhijit Patil, Swapna Oundhakar, Amit Sheth, Kunal Verma.
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METEOR-S exploits Workflow, Semantic Web, Web Services, and Simulation technologies to meet these challenges in a practical and standards based approach.
Applying Semantics in Annotation, Quality of Service, Discovery, Composition, Execution of Web Services
Adding semantics to different layers of Web services conceptual stack
Use of ontologies to provide underpinning for information sharing and semantic interoperability
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Framework Semantics in METEOR-S and WS stack
Publication
Discovery
Description
Messaging
Network
Flow
MWSDI: Scalable Infrastructure of Registries for Semantic publication and discovery of Web Services
MWSAF: Semantic Annotation of WSDL (WSDL-S)
MWSCF: Semantic Web Process Composition Framework
METEOR-S at the LSDIS Lab exploits Workflow, Semantic Web, Web Services, and Simulation technologies to meet these challenges in a practical and
<input messageLabel = ”order” element = "rosetta:#PurchaseOrderRequest" /> <output messageLabel = ”orderConfirmation” element = "rosetta:#PurchaseOrderConfirmation" />
</operation> </interface></definitions>
P. Rajasekaran, J. Miller, K. Verma, A. Sheth, Enhancing Web Services Description and Discovery to Facilitate Composition, available at http://lsdis.cs.uga.edu/lib/download/swswpc04.doc
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Framework Outline
Introduction
METEOR-S Project @ UGA
SchemaGraph
Architecture
Matching algorithm
Results
Conclusions and Future Work
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Framework Matching Issues (WSDL and Ontologies)
Expressiveness Different reasons behind their development
XML Schema used in WSDL for providing basic structure to data exchanged by Web services
Ontologies are developed to capture real world knowledge and domain theory
Knowledge captured XML Schema has minimal containment relationship
Language used to describe ontologies model real world entities as classes, their properties and provides named relationships between them
Solution Use hueristics to create normalized
representation
We call it SchemaGraph
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Framework MWSAF – SchemaGraph
What is SchemaGraph ? Normalized representation to capture XML Schema and DAML
Ontology
How to use SchemaGraph Conversion functions convert both XML Schema and Ontology
to SchemaGraph representation
XML schema used by WSDL → W = {wc1, wc2, wc3, …, wcn} where, wci is an element in XML schema and n is the number of elements
Ontology → O = {oc1, oc2, oc3, …, ocm} where, oci is a concept in Ontology and m is the number of concepts
Match function takes both W and O and returns a set of mappings
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MWSAF – XML Schema to SchemaGraph
Rule XML Schema constructs SchemaGraph representation
1 Element, Node
2 simpleType Node
3 Enumeration values defined for simpleType S
Node with edge between simpleType S node and value node with name “hasValue”
4 ComplexType Node
5 Sub-elements of complexType C which have range as basic XML datatypes
Node with edge between complexType C node and this node with name “hasElement”
6 Sub-elements of complexType C which have range as complexTypes or simpleTypes or elements defined in same schema
Edge between complexType C node and the range type node
Ontology Store Categorize in domains Currently supports DAML and RDF formats Will be replaced in future with high quality ontology search mechanisms
Parser Library Parser used to generate SchemaGraphs Currently provides Ontology2Graph and WSDL2Graph parsers
Matcher Library Provides two types of Matching algorithms
Element level Matching algorithms – NGram, CheckSynonyms, CheckAbbreviations, TokenMatcher
Schema Matching algorithms
Allows to add new Algorithms
User Interface Displays the mappings and allows user to accept or reject it It also allows to match the concepts manually Displays the WSDL and ontology in tree format
NGram – This algorithms calculates similarity between two strings by considering the number of qgrams that they have in common. It uses dice coefficient to calculate this similarity.
CheckSynonyms – This algorithm uses WordNet to find synonyms. It also accounts for hypernyms and hyponyms matching.
CheckAbbreviation – This algorithm uses domain specific Abbreviation dictionary to expand the abbreviations
TokenMatcher – This algorithm uses the Porter Stemmer to find the roots of the words. It also uses tokenization based on punctuation and capitalization of letters.
Average Service Match ( avgServiceMatch ) Calculated as the average match of all the concepts of a WSDL
schema and a domain ontology The domain of the ontology corresponding to the best average
service match also represents the domain of the Web service Normalized on the scale of 0 to 1
n
mMSMatchavgService
k
1ii
where, k = number of mapped conceptsn = number of concepts in WSDL schema
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Framework MWSAF – Annotating WSDL
Average Concept Match ( avgConceptMatch ) Calculated as the average match of the mapped concepts of a WSDL
schema Based on this measure user can decide whether to accept mappings
for annotation or not It is normalized on the scale of 0 to 1
conceptsmappedofnok,wherek
mMS
MatchavgConcept
k
1i
i
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Framework Outline
Introduction
METEOR-S Project @ UGA
SchemaGraph
Architecture
Matching algorithm
Results
Conclusions and Future Work
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Framework MWSAF – Categorizing WSDL
6 different Web services are compared to 5 ontologies to get avgServiceMatch values for each of them. Service belongs to the domain of the ontology for which it gives best avgServiceMatch.
E.g. AirportWeather service best matches to weather-ont ontology and hence belongs to weather domain
24 Web services from Weather and Geographical domain are categorized with different threshold (CT) values. For CT = 0.4, two services are categorized wrongly For CT = 0.5, all the Web services are not categorized
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Weather Geo
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Categorization Threshold = 0.5Categorization Threshold = 0.4Actual Number of Services
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With original Geo ontologies, services gave low match scores By adding few more concepts, the match scores improved for many
services. Plot of number of mapped concepts strengthens this observation
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Services
Mapings with Original Geo ontology
Mappings with New Geo ontology
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Problems Match scores are low All concepts are not mapped
Reasons Match algorithms can be improved
Domain specific synonyms and abbreviations can improve avgConceptMatch
Domain specific match algorithms can be implemented
Ontologies are still in development stage and not comprehensive enough to contain all the concepts from the domain
Need ontologies specifically designed for Web services WSDL files are automatically generated by web servers and hence
not all IO parameters have meaningful names
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Framework Outline
Introduction
METEOR-S Project @ UGA
SchemaGraph
Architecture
Matching algorithm
Results
Conclusions and Future Work
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Framework Conclusions and Future Work
Conclusions Created an initial prototype for semi-automatic annotation of Web
services
Initial results promising, but a lot of improvement possible
WSDL-S adds semantics to Web services with minimal changes
Future Work Apply machine learning techniques to improve accuracy
Build a test bed for Semantic Web Services
Eclipse based tool release in 1 montg
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Framework References
1. D. Fensel, C. Bussler, "The Web Service Modeling Framework WSMF", Technical Report, Vrije Universiteit Amsterdam
2. METEOR-S: Semantic Web Services and Processes, http://swp.semanticweb.org
3. A. Ankolekar, M. Burstein, J. Hobbs, O. Lassila, D. Martin, D. McDermott, S. McIlraith, S. Narayanan, M. Paolucci, T. Payne, and K. Sycara, "DAML-S: Web service Description for the Semantic Web," in Proceedings of the 1st International Semantic Web Conference (ISWC 2002)
4. S. Agarwal, S. Handschuh, and S. Staab “Surfing the Service Web”, in Proceedings of the 2nd International Semantic Web Conference (ISWC 2003)
5. M. Klein, “Combining and relating ontologies: an analysis of problems and solutions”. in (IJCAI 2001)
6. E. Rahm and P. A. Bernstein. A survey of approaches to automatic schema matching. In The VLDB Journal: Volume 10 Issue , (2001), pages 334-350, 2001.
7. H. Do, S. Melnik, and E. Rahm. Comparison of schema matching evaluations. In Proceedings of the 2nd Int. Workshop on Web Databases (German Informatics Society), 2002
8. Pottinger, R. A. and P. A. Bernstein, “Merging Models Based on Given Correspondences.” Proc. 29th VLDB Conference