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Extracting Semantic Constraint from Description Text for Semantic Web Service Discovery

Extracting Semantic Constraint from Description Text for Semantic Web Service Discovery. Dengping Wei, Ting Wang, Ji Wang, and Yaodong Chen Reporter: Ting Wang Department of Computer Science and Technology School of Computer National University of Defense Technology, China

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Extracting Semantic Constraint from Description Text for Semantic Web Service Discovery

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  1. Extracting Semantic Constraint from Description Text for Semantic Web Service Discovery Dengping Wei, Ting Wang, Ji Wang, and Yaodong Chen Reporter: Ting Wang Department of Computer Science and Technology School of Computer National University of Defense Technology, China tingwang@nudt.edu.cn

  2. Outline • Motivation • Semantic Constraint for SWS Discovery • Extracting Semantic Constraint • Matching Algorithm • Experiment Results • Conclusions and Future Work

  3. Motivation • Various Semantic Web Service (SWS) description ontologies or frameworks • e.g. OWL-S, WSMO, WSDL-S, SAWSDL. • Various SWS matchmakers • logic based semantic IOPE matching • inputs(I), outputs(O), preconditions/assumptions(P) and effects/postconditions(E) • logic based semantic Input/Output matching • …

  4. Motivation • Most current SWS matchmakers treat the SWS signature as a set of concepts • not sufficient to discover SWS • two services with similar semantics may fail to match • two services with the same input and output concepts may have essential differences in semantics • which may not be detected by logic based reasoning.

  5. Motivation • Many recent researches have exploredvarious information to complement service I/O concepts for SWS matchmaking • The ranked matching algorithm [Jaeger, et al. 2005] • A hybrid method for SWS discovery [Klusch, et al. 2006] • SWS matchmaking based on iSPARQL [Kiefer, et al. 2008] • [Hull, et al. 2006] describes the relationships and usesOWL ontologies

  6. Motivation • The relationships between the service I/O concepts can be helpful for expressing the semantics of services.

  7. Motivation • Our idea: • add some restriction relationships to the interface concepts • to enhance the semantic description of services. • extract restriction relationships • those relationships not defined in the domain ontology. • from the service description text • automatically • perform the matching on the service I/O concepts and their semantic constraints represented by a constraint graph

  8. Outline • Motivation • Semantic Constraint for SWS Discovery • Extracting Semantic Constraint • Matching Algorithm • Experiment Results • Conclusions and Future Work

  9. Semantic Constraint for SWS Discovery • Observation: • the domain of concept is not specified • the price of a book • the price of a flight ticket • the property of concept is not specified • the food with the maximum price • the food with brand “Coca Cola” • the relationship between concepts is not specified • the foodcontained in a certain grocery store • the foodsold by a certain grocery store

  10. Semantic Constraint for SWS Discovery • The semantics of SWS will be better clarified • if the constraint relationships of the concepts have been annotated

  11. Semantic Constraint for SWS Discovery • Definition of a statement <SC,CT,OC> • SC (Subject Concept) • subject of the statement • usually corresponds to the service I/O concepts. • OC (Object Concept) • object of statement • described as another concept or a literal. • CT (Constraint Type) • predicate of the statement • identifies the property or characteristic of the subject concept that the statement specifies.

  12. Constraint Types Definition • CT (Constraint Type) • three important abstract constraint types • isPropertyObjectOf Constraint: • triple <A, isPropertyObjectOf,B> means that concept A is a property object of concept B. • hasPropertyObject Constraint: • this constraint relation is the inverse of isPropertyObjectOf. • Operation Constraint: • triple <A, Operation, B> means that two concepts entities have a certain association between them • < Book, “published by”, “Springer” > • the books that are published by Springer

  13. Constraint Graph Definition • Definition • Let C be a set of concepts, a directed constraint graph can be described as ConstraintGraph(C) = {<SC,CT,OC>|SC ∈ C}.

  14. Outline • Motivation • Semantic Constraint for SWS Discovery • Extracting Semantic Constraint • Matching Algorithm • Experiment Results • Conclusions and Future Work

  15. Description text The service returns the price of the book published by Springer. S VP NP Preprocessing DT VBZ NN NP(price) returns service The PP NP(price) Parsing NP(book) DT NN IN price the of Syntactic Tree VP NP(book) Extracting PP VBD NN DT book the published Constraint Graph IN NN Springer. by <SC1,CT1,OC1> <SC2,CT2,OC2> …….. <SCn,CTn,OCn> <Price, isPropertyObjectOf , Book> <Book, “published by”, “Springer”> (b) example (a) semantic constraint extracting framework Extracting Semantic Constraint Stanford PCFG Parser Fig. 2. Semantic constraint extraction

  16. Extracting Semantic Constraint • Candidate Constituent Detection • Constraint Constituents Filtering • Extracting Modifier

  17. Extracting Semantic Constraint • Candidate Constituent Detection • observation: • the constraints of a key-word are probably contained in the phrase whose head word is the keyword. • detect all such phrases by propagating the key-word from the bottom to the top of the syntactic tree. • the propagation path is expressed as a sequence of interior nodes in the parsing tree • e.g. a node sequence “NP NP” in the example is the propagation path of key-word “price”.

  18. Constraint Constituents Filtering and Extracting Modifier

  19. Outline • Motivation • Semantic Constraint for SWS Discovery • Extracting Semantic Constraint • Matching Algorithm • Experiment Results • Conclusions and Future Work

  20. Matching Algorithm • Constraint Graph Matching(CGM) • where P is the number of triples in ConstraintGraph(Cr ) • P’ the number of triples in ConstraintGraph(Cs) • function TripleMatch(RTi, STi) to estimate the match between two triples RTi and STj.

  21. Matching Algorithm • Triples Matching • two triples are matched and the degree of match can be measured • if all the three elements in each triple are relative

  22. Matching Algorithm • Concept Matching • matching: five different levels • Exact match:r = s. • Plug-in match: r ∈Ascendant(s) ∨ s∈Descendant(r) • Subsumed-by match:s∈Ascendant(r)∨r∈Descendant(s) • Intersect match: • Fails

  23. Outline • Motivation • Semantic Constraint for SWS Discovery • Extracting Semantic Constraint • Matching Algorithm • Experiment Results • Conclusions and Future Work

  24. Experiment Results • OWL-S TC v2: • 576 web services from 7 domains • 28 queries with their relevance sets. • http://www-ags.dfki.uni-sb.de/∼klusch/owls-mx/ • Two sets of web services • dataset1: • the semantic constraints of the output concepts in request and web service are manually annotated by two people • mainly described by service I/O concepts • dataset2: • the semantic constraints of concepts are automatically extracted using the method represented above

  25. Experiment Results • [Klusch et al. 2006] • OWLS-M0 is a pure logic based matchmaker on the service I/O concepts • OWLS- M4 is reported to be the best-performing matchmaker variant of the OWLS-MX matchmaker • M0+InOutConstraint matchmaker uses CGM to filter the results of OWLS-M0 on Dataset1 • M0+AutoConstraint matchmaker uses CGM to filter the results of OWLS-M0 on Dataset2 • M4+InOutConstraint matchmaker uses CGM to filter the results of OWLS-M4 on Dataset1 • M4+AutoConstraint matchmaker uses CGM to filter the results of OWLS-M4 on Dataset2

  26. Experiment Results InOutConstraint OWLS-M4 OWLS-M0 The performance on Dataset1

  27. Experiment Results M4+InOutConstraint M0+InOutConstraint OWLS-M4 OWLS-M0 The performance on Dataset1

  28. Experiment Results M4+AutoConstraint M0+AutoConstraint OWLS-M4 AutoConstraint OWLS-M0 The performance on Dataset2

  29. Outline • Motivation • Semantic Constraint for SWS Discovery • Extracting Semantic Constraint • Matching Algorithm • Experiment Results • Conclusions and Future Work

  30. Conclusion • Introduce semantic constraints for service I/O concepts • enhancing the semantics of web service • Extract semantic constraints automatically from the parsing trees of the description text • Use constraint graph to describe the semantic constraints of the service I/O concepts • A matching algorithm for the constraint graph

  31. Future Work • Finding more effective extraction method • to get better results of extraction • Extract more constraint relationships for the concepts • web service can be represented by a more complicated graph • more sophisticate matching algorithm

  32. Thank you!

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