Masters thesis presentation

1. A COMPARISON OF SAWSDL BASED SEMANTIC WEB SERVICE DISCOVERY ALGORITHMS By Shiva SandeepGarlapati Graduate Student Dept. of Computer Science University of Georgia

3. Purpose

4. Goal

5. Discovery Algorithms

6. Comparative Evaluation

7. Conclusions

9. Semantic Web Services

11. Semantic Web Services Extensions to the web services description by adding semantic annotations, which make the web services more machine-understandable and allows machines to perform automated discovery, compositions of web services.

13. Achieved by adding these attributes to elements

14. modelReference

15. liftingSchemaMapping

17. Purpose No study on comparison of semantic web service discovery algorithms Algorithm improvement suggestions

20. TVERSKY Model Based

22. 3 variants

23. SAWSDL-MX1 (Logic based + text similarity)

24. SAWSDL-M0 + WA (Logic based + WSDL analyzer tool)

25. SAWSDL-MX2 (SVM based matching)[1] Klusch, Patrick Kapahnke and Ingo Zinnikus: Hybrid Adaptive Web Service Selection with SAWSDL-MX and WSDL Analyzer. The 6th Annual European Semantic Web Conference (ESWC 2009)

27. SAWSDL-M0 (Logic based only)

28. SAWSDL-M1 (Logic based + Loss-Of-Information text similarity)

29. SAWSDL-M2(Logic based + Extended Jaccard text similarity)

30. SAWSDL-M3 (Logic based + Cosine text similarity)

33. Nodes are operation of the service

34. edges are degree of match calculated using the Logic-Based Matching

36. Similarity is a weighted average of match between pair (Request, Service) of

37. Input concepts

38. Output concepts

39. Functionality concepts

40. Matching algorithm is based on annotations to

41. Common ontology

42. Multiple ontologies

43. N-Gram algorithm for syntactic similarity[2] Jorge Cardoso, John Miller, SavithaEmani: Web Service Discovery using Annotated WSDL, Reasoning Web Fourth International Summer School 2008 Published BY Springer 2008.

44. TVERSKY (Common ontology) p(C) is the defined as the set of properties of the concept C in the ontology

45. TVERSKY (Multiple ontologies)

46. TVERSKY (Multiple ontologies) Hungarian Algorithm

48. Similarity is a weighted average of match between pair (Request, Service) of

49. Input concepts

50. Output concepts

51. Functionality concepts

52. Syntactic Similarity

53. N-Gram algorithm for syntactic similarity[3] KunalVerma, et al. "Allowing the Use of Multiple Ontologies for Discovery of Web Services in Federated Registry Environment," Technical Report #UGA-CS-LSDIS-TR-07-011, Department of Computer Science, University of Georgia, Athens, Georgia (February 2007) pp. 1-27.

57. Statistical measures

59. The collection has 26 requests, 895 services and relevance set of 10-75 services for each request

60. We chose services that have annotations to single ontology

63. Precision graph for the three algorithms

64. Recall graph for the three algorithms

66. SAWSDL-M1

67. SAWSDL-M2

68. SAWSDL-M3

69. SAWSDL-M4

70. Average for SAWSDL-MX

71. TVERSKY

72. MWSDI

73. Syntactic Similarity (Extended Jaccard)

74. Precision graph

75. Recall graph

76. F-measure graph

77. Comparison of the algorithms

79. MWSDI calculates the concept similarity as a weighted average of coverage, property and syntatic similarity which is a more detailed approach compared to Tversky

80. MWSDI has deeper hierarchy of comparison since it use Hungarian algorithm at every level of concept match, which make it slow

81. MWSDI is limited to services with single ontology for reference

84. Use a Machine learning algorithm to assign weights for input, output and functional similarity

85. Use Hungarian algorithm in Tversky Model for mapping inputs and outputs for the request and service

88. Extended Jaccard

89. Cosine

90. Jensen-Shannon

92. Loss-Of-Information Similarity

93. N-Gram Similarity n-gram is a subsequence of n items from a given sequence N-GramSim(R.G,S.G) = Where R.G is the set of N-Grams for the unfolded Concept expression of R

95. eXist XML database as registry for storing services

97. eXist XML database as registry for storing services

98. SAWSDL Parser to get parameters required from the service