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    2. Web Service Search

    13. How to Improve Web Service Search?

    14. 1) Provide Similar WS Operations

    15. 2) Provide Operations with Similar Inputs/Outputs

    16. 3) Provide Composable WS Operations

    19. Elementary Problems

    20. Can We Apply Previous Work?

    21. Why Text Matching Does not Apply?

    23. Why Text Matching Does not Apply?

    24. Operations Have More Complex Structures

    25. Our Solution Part 1: Exploit Structure

    26. Why Text Matching Does not Apply?

    27. Parameter Names Are Highly Varied

    28. Our Solution Part 2: Cluster Parameters into Concepts

    29. Outline

    30. Clustering Parameter Names

    31. Criteria for an Ideal Clustering

    32. Clustering Algorithm (I)

    33. Clustering Algorithm (II)

    34. Clustering Algorithm (III)

    35. Clustering Algorithm (IV)

    36. Clustering Algorithm (V)

    37. Outlines

    38. Experiment Data and Clustering Results

    39. Example Clusters Mainly good Tolerant of misspelling Some noise Merged after the fourth run How many ops use itMainly good Tolerant of misspelling Some noise Merged after the fourth run How many ops use it

    40. Example Clusters Mainly good Tolerant of misspelling Some noise Merged after the fourth run How many ops use itMainly good Tolerant of misspelling Some noise Merged after the fourth run How many ops use it

    41. Example Clusters Mainly good Tolerant of misspelling Some noise Merged after the fourth run How many ops use itMainly good Tolerant of misspelling Some noise Merged after the fourth run How many ops use it

    42. Measuring Top-K Precision

    43. Top-k Precision for Operation Matching

    44. Top-k Precision for Input/output Matching

    45. Measuring Precision and Recall

    46. Impact of Multiple Sources of Evidences in Operation Matching

    47. Impact of Parameter Clustering in Input/output Matching

    48. Conclusions

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