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 explored various 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 uses OWL 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 food contained in a certain grocery store the food sold 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 (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 means that concept A is a property object of concept B. hasPropertyObject Constraint:  this constraint relation is the inverse of isPropertyObjectOf. Operation Constraint:  triple means that two concepts entities have a certain association between them   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 ∈ C}.

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

15. Extracting Semantic Constraint NP VP DT NN VBZ NP(price) PP IN NP(book) DT NN service returns the price book S The NP(book) VP VBD PP published IN NN DT the NN Springer. by Preprocessing Parsing Extracting Constraint Graph Syntactic Tree The service returns the price of the book published by Springer.Description text …….. (a) semantic constraint extracting framework (b) example of Fig. 2. Semantic constraint extraction Stanford PCFG Parser

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(C r )  P’ the number of triples in ConstraintGraph(C s )  function TripleMatch(RT i, ST i ) to estimate the match between two triples RT i and ST j.

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 The performance on Dataset1 InOutConstraint OWLS-M4 OWLS-M0

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

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

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!