1. Efficient Semantic Web Service Discovery in Centralized and P2P Environments Dimitrios Skoutas 1,2 dskoutas@imis.athena-innovation.gr Dimitris Sacharidis 2 dsachar@dblab.ntua.gr Verena Kantere 3 verena.kantere@epfl.ch Timos Sellis 1,2 timos@imis.athena-innovation.gr 1 Inst. for the Mgmt of Inf. Systems (IMIS) - R.C. “Athena”, Greece 2 National Technical University of Athens (NTUA), Greece 3 Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland

2. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Outline  Introduction Encoding service descriptions Centralized search P2P search Experimental evaluation Conclusions

3. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Web Service Discovery Traditional approaches IR-style search (i.e., keyword matching) syntactic match Adding semantics to Web service descriptions increase the precision of the discovery process automate discovery, invocation, composition, execution monitoring SAWSDL, OWL-S, WSMO

4. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Semantic Matchmaking Based on logic inference service parameters are annotated with concepts from an associated ontology a reasoner is used to match, in a pairwise manner, parameters from the requested and offered the services exactplug-insubsumes [Paolucci et al. @ISWC’02]

5. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Semantic Matchmaking Need for ranking often deal with partial matches Web users survey ~80% viewed top-1 result <50% viewed beyond top-3 result top-1 result needed in fully automated scenarios We measure the similarity by the ratio of common subclasses [Skoutas et al. @SWSP’07] [Joachims, Radlinski @IEEE Computer ’07]

6. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Outline Introduction  Encoding service descriptions Centralized search P2P search Experimental evaluation Conclusions

7. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Example INPUTOUTPUT RC8C8 C 4, C 7 S1S1 C1C1 C 4, C 2 S2S2 C3C3 C 9, C 7 S3S3 C5C5 C1C1 an ontology snippet a sample service request and 3 advertisements

8. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions Labels are assigned to concepts based on their position within the concept hierarchy based on a labeling scheme labels are intervals of the form [begin, end] intervals are constructed by performing a depth-first traversal of the hierarchy [Christophides et al. @WWW’03]

9. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions ConceptInterval(s) C0C1C2C3C4C5C6C7C8C9C0C1C2C3C4C5C6C7C8C9

10. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions ConceptInterval(s) C0C1C2C3C4C5C6C7C8C9C0C1C2C3C4C5C6C7C8C9

11. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions ConceptInterval(s) C0C1C2C3C4C5C6C7C8C9C0C1C2C3C4C5C6C7C8C9 [1,

12. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions ConceptInterval(s) C0C1C2C3C4C5C6C7C8C9C0C1C2C3C4C5C6C7C8C9 [1, [2,

13. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions ConceptInterval(s) C0C1C2C3C4C5C6C7C8C9C0C1C2C3C4C5C6C7C8C9 [1, [2, [3,

14. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions ConceptInterval(s) C0C1C2C3C4C5C6C7C8C9C0C1C2C3C4C5C6C7C8C9 [1, [2, [3, [4,

15. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions ConceptInterval(s) C0C1C2C3C4C5C6C7C8C9C0C1C2C3C4C5C6C7C8C9 [1, [2, [3, [4,5]

16. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions ConceptInterval(s) C0C1C2C3C4C5C6C7C8C9C0C1C2C3C4C5C6C7C8C9 [1, [2, [3,6] [4,5]

17. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions ConceptInterval(s) C0C1C2C3C4C5C6C7C8C9C0C1C2C3C4C5C6C7C8C9 [1, [2, [3,6] [7, [4,5]

18. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions ConceptInterval(s) C0C1C2C3C4C5C6C7C8C9C0C1C2C3C4C5C6C7C8C9 [1, [2, [3,6] [7, [4,5] [8,

19. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions ConceptInterval(s) C0C1C2C3C4C5C6C7C8C9C0C1C2C3C4C5C6C7C8C9 [1, [2, [3,6] [7, [4,5] [8,9]

20. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions ConceptInterval(s) C0C1C2C3C4C5C6C7C8C9C0C1C2C3C4C5C6C7C8C9 [1, [2, [3,6] [7,10] [4,5] [8,9]

21. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions ConceptInterval(s) C0C1C2C3C4C5C6C7C8C9C0C1C2C3C4C5C6C7C8C9 [1, [2,11] [3,6] [7,10] [4,5] [8,9]

22. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions ConceptInterval(s) C0C1C2C3C4C5C6C7C8C9C0C1C2C3C4C5C6C7C8C9 [1,20] [2,11] [12,19] [3,6] [7,10] [13,14] [15,16] [17,18] [4,5] [8,9]

23. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions ConceptInterval(s) C0C1C2C3C4C5C6C7C8C9C0C1C2C3C4C5C6C7C8C9 [1,20] [2,11] [12,19] [8,9] [3,6] [7,10] [13,14] [8,9] [15,16] [17,18] [4,5] [8,9]

24. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions Matching is performed by comparing labels Ranking also determined by the labels type of matchcondition exactI R = I S plug-in I R I S subsumes I R I S C 1 : [2,11] |G C 1 | = 5

25. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions INPUTOUTPUT RC8C8 C 4, C 7 S1S1 C1C1 C 4, C 2 S2S2 C3C3 C 9, C 7 S3S3 C5C5 C1C1 ConceptInterval(s) C0C1C2C3C4C5C6C7C8C9C0C1C2C3C4C5C6C7C8C9 [1,20] [2,11] [12,19] [8,9] [3,6] [7,10] [13,14] [8,9] [15,16] [17,18] [4,5] [8,9] INPUTOUTPUT R[4,5][7,10],[17,18] S1S1 [2,11][7,10],[12,19],[8,9] S2S2 [3,6][8,9],[17,18] S3S3 [13,14],[8,9][2,11]

26. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Encoding Service Descriptions Input parameters Output parameters INPUTOUTPUT R[4,5][7,10],[17,18] S1S1 [2,11][7,10],[12,19],[8,9] S2S2 [3,6][8,9],[17,18] S3S3 [13,14],[8,9][2,11]

27. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Outline Introduction Encoding service descriptions  Centralized search P2P search Experimental evaluation Conclusions

28. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Centralized Search Index service representations using an R-tree points are partitioned in hierarchically nested minimum bounding rectangles (MBRs) leaf nodes contain data points internal nodes contain the MBRs of their children we use two R-trees, one for input and one for output parameters

29. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Centralized Search Input parameters INPUTOUTPUT R[4,5][7,10],[17,18] S1S1 [2,11][7,10],[12,19],[8,9] S2S2 [3,6][8,9],[17,18] S3S3 [13,14],[8,9][2,11]

30. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Centralized Search Input parameters INPUTOUTPUT R[4,5][7,10],[17,18] S1S1 [2,11][7,10],[12,19],[8,9] S2S2 [3,6][8,9],[17,18] S3S3 [13,14],[8,9][2,11]

31. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Centralized Search Input parameters INPUTOUTPUT R[4,5][7,10],[17,18] S1S1 [2,11][7,10],[12,19],[8,9] S2S2 [3,6][8,9],[17,18] S3S3 [13,14],[8,9][2,11]

32. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Centralized Search Output parameters INPUTOUTPUT R[4,5][7,10],[17,18] S1S1 [2,11][7,10],[12,19],[8,9] S2S2 [3,6][8,9],[17,18] S3S3 [13,14],[8,9][2,11]

33. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Centralized Search Output parameters INPUTOUTPUT R[4,5][7,10],[17,18] S1S1 [2,11][7,10],[12,19],[8,9] S2S2 [3,6][8,9],[17,18] S3S3 [13,14],[8,9][2,11]

34. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Centralized Search Output parameters INPUTOUTPUT R[4,5][7,10],[17,18] S1S1 [2,11][7,10],[12,19],[8,9] S2S2 [3,6][8,9],[17,18] S3S3 [13,14],[8,9][2,11]

35. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Centralized Search Output parameters INPUTOUTPUT R[4,5][7,10],[17,18] S1S1 [2,11][7,10], [12,19],[8,9] S2S2 [3,6][8,9],[17,18] S3S3 [13,14],[8,9][2,11]

36. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Basic Search Algorithm Input the request R the set of available (encoded) services S Output a subset of S containing those services that match R Function iterate over the request parameters for each parameter get the corresponding interval(s) for each interval issue 3 queries on the corresponding R-tree: one point query (exact matches) and two range queries (plug-in and subsumes matches) return the services that provide a match for all parameters

37. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Progressive Search Algorithm Input the request R the set of available (encoded) services S the number of results to return, k Output the top-k services matching R Function iterate over the request parameters for each parameter create a heap, and initialize it with the root entries of the corresponding R-tree (sorted) select the heap whose head entry has the minimum distance if it corresponds to an internal node, expand it; else mark that the corresponding service has a match for this parameter if the service has a match for all parameters, return it repeat until k services are found

38. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Outline Introduction Encoding service descriptions Centralized search  P2P search Experimental evaluation Conclusions

39. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany P2P Search SpatialP2P a structured, flat P2P overlay especially designed for the management of spatial data it partitions the space in a grid, and assigns (sets of) cells to peers each cell is stored and managed by the closest peer queries are routed according to locality and directionality [Kantere et al. @TKDE]

40. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany P2P Search SpatialP2P example

41. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany P2P Search Managing services in SpatialP2P inserting a service each service parameter is encoded, and then assigned to the closest peer in the 2-d space we maintain the type of parameter and to which service it belongs searching for a service as previously, based on point and range queries for top-k queries, the search space is incremented dynamically

42. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Outline Introduction Encoding service descriptions Centralized search P2P search  Experimental evaluation Conclusions

43. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Experimental Evaluation Experimental setup Real data set OWL-S service retrieval test collection OWLS-TC v2  ontologies from 7 different domains  576 OWL-S services  28 sample requests  the relevance set for each request Synthetic data set automatically generated based on OWLS-TC v2 10K services, created as variations of the original 576 services http://projects.semwebcentral. org/projects/owls-tc/

44. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Experimental Evaluation Effectiveness of ranking ~30% of the relevant services are retrieved with precision >80% to retrieve >70% of the relevant services the precision drops below 50%

45. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Experimental Evaluation Effectiveness of ranking ~30% of the relevant services are retrieved with precision >80% to retrieve >70% of the relevant services the precision drops below 50%

46. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Experimental Evaluation Search cost

47. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Outline Introduction Encoding service descriptions Centralized search P2P search Experimental evaluation  Conclusions

48. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Conclusions An approach for Semantic Web service matchmaking and ranking based on encoding and indexing the service descriptions both centralized and p2p search Future work consider other types of parameters (i.e., non- functional) further improve the p2p search

49. D. Skoutas - ISWC’08, Oct. 30, Karlsruhe, Germany Thank you