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Learning to attach semantic metadata to Web Services Andreas He, - - PowerPoint PPT Presentation

Aug 25, 2023 351 likes •799 views

Learning to attach semantic metadata to Web Services Learning to attach semantic metadata to Web Services Learning to attach semantic metadata to Web Services Andreas He, Nicholas Kushmerick University College Dublin, Ireland {andreas.hess,

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1 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Learning to attach semantic metadata to Web Services Learning to attach semantic metadata to Web Services

Learning to attach semantic metadata to Web Services

Andreas Heß, Nicholas Kushmerick University College Dublin, Ireland {andreas.hess, nick}@ucd.ie

US Office of Naval Research Science Foundation Ireland

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2 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Learning to attach semantic metadata to Web Services Learning to attach semantic metadata to Web Services

  • 1. Introduction
  • 2. Our Machine Learning Approach
  • 3. Classification Tasks

a) Domain & Datatype (Web Forms) b) Category (Web Services)

  • 4. Conclusion & Discussion
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3 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Web Services Web Services

  • Web Services
  • Web-accessible

computational entities

  • Loosely coupled
  • Just RPC? Web forms?
  • Data Integration?
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4 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Web Services Web Services

  • Tasks / Problems
  • Automatic Discovery
  • Automatic Composition
  • Automatic Invocation
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5 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Scenario Scenario

Congo Winding Stair Teatime

  • author
  • title
  • quantity
  • authName
  • bookT
  • ISBN
  • region
  • qlty
  • qty

? ?

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6 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Scenario Scenario

Congo Winding Stair Teatime

  • author
  • title
  • quantity
  • authName
  • bookT
  • ISBN
  • region
  • qlty
  • qty

Global Ontology

  • Item

➢ Quantity ➢ Price

  • Book

➢ Author ➢ Title ➢ ISBN

  • Tea

➢ Region ➢ Quality

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7 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Scenario Scenario

Congo Winding Stair Teatime

  • author
  • title
  • quantity
  • authName
  • bookT
  • ISBN
  • region
  • qlty
  • qty

Global Ontology

  • Item

➢ Quantity ➢ Price

  • Book

➢ Author ➢ Title ➢ ISBN

  • Tea

➢ Region ➢ Quality

Semantic Metadata e.g. DAML-S / OWL-S (handcrafted)

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8 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Scenario Scenario

Congo Winding Stair Teatime

  • author
  • title
  • quantity
  • authName
  • bookT
  • ISBN
  • region
  • qlty
  • qty

Global Ontology

  • Item

➢ Quantity ➢ Price

  • Book

➢ Author ➢ Title ➢ ISBN

  • Tea

➢ Region ➢ Quality

!

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9 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Learning to attach semantic metadata to Web Services Learning to attach semantic metadata to Web Services

  • Assumes:
  • semantic annotation
  • a shared ontology
  • Semantic metadata needs to be handcrafted!!
  • Our contribution: Use machine learning!
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10 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Learning to attach semantic metadata to Web Services Learning to attach semantic metadata to Web Services

✔ Introduction

  • 2. Our Machine Learning Approach
  • 3. Classification Tasks

a) Domain & Datatype (Web Forms) b) Category (Web Services)

  • 4. Conclusion & Discussion
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11 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Machine Learning Machine Learning

Global Ontology

  • Item
➢

Quantity

➢

Price

  • Book
➢

Author

➢

Title

➢

ISBN

  • Tea
➢

Region

➢

Quality

Congo

  • author
  • title
  • quantity

Winding Stair Teatime

  • authName
  • bookT
  • ISBN
  • region
  • qlty
  • qty

Learning algorithm Training data Semantic Metadata (handcrafted!)

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12 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Machine Learning Machine Learning

Global Ontology

  • Item
➢

Quantity

➢

Price

  • Book
➢

Author

➢

Title

➢

ISBN

  • Tea
➢

Region

➢

Quality

BookMaster

  • writer
  • bookName
  • librNumber ?

Learning algorithm

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13 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Machine Learning Machine Learning

Global Ontology

  • Item
➢

Quantity

➢

Price

  • Book
➢

Author

➢

Title

➢

ISBN

  • Tea
➢

Region

➢

Quality

BookMaster

  • writer
  • bookName
  • librNumber

Learning algorithm Semantic Metadata (automatic)

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14 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Key Assumption Key Assumption

public int nbgfuibhuf(int nvzfdubzuf, int cnuzdc) { int vfddf = 0; for (int ujz = 0; ujz < nvzfdubzuf; ujz++) { vfddf += cnuzdc; } return vfddf; }

What does this function do?

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15 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Key Assumption Key Assumption

public int multiply(int factor1, int factor2) { int product = 0; for (int n = 0; n < factor1; n++) { product += factor2; } return product; }

What does this function do?

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16 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Key Assumption Key Assumption

/** * This function multiplies two numbers in a very * inefficent way. It serves only as an example. */ public int multiply(int factor1, int factor2) { int product = 0; for (int n = 0; n < factor1; n++) { product += factor2; } return product; }

What does this function do?

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17 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

A Text Classification Problem A Text Classification Problem

Web Service classification == Text classification

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18 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Definitions Definitions

Category

  • Broad description of

service as a whole

  • e.g. e-commerce,

weather, finance

  • Profile hierarchy

Domain

  • Purpose of single
  • peration
  • e.g. query price,

purchase book

  • Atomic process

Datatype

  • Meaning of single

parameter

  • e.g. author name,

credit card number

  • Property

?

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19 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Learning to attach semantic metadata to Web Services Learning to attach semantic metadata to Web Services

✔ Introduction ✔ Our Machine Learning Approach

  • 3. Classification Tasks

a) Domain & Datatype (Web Forms) b) Category (Web Services)

  • 4. Conclusion & Discussion
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20 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Assumption Assumption

Dependencies between CategoryDomainDatatype

?

Books Category Query book price Domain Datatype Book title

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21 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Assumption Assumption

Dependencies between CategoryDomainDatatype

Tea Category Domain Datatype Order tea

Book title??

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22 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Assumption Assumption

Dependencies between CategoryDomainDatatype

Tea Category Domain Datatype Order tea

Credit card number

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23 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Bayesian Networks Bayesian Networks

  • Bayesian Networks
  • Nodes are random variables
  • Edges indicate conditional probabilites
  • Flow of evidence
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24 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Bayesian Networks Bayesian Networks domain datatype1 datatype2 datatype3 term1

1

Pr[title | BookTitle] = 0.39 Pr[title | DestAirport] = 0.02 Pr[city | BookTitle] = 0.01 Pr[city | DestAirport] = 0.47 ... ... ...

Pr[BookTitle | SEARCHBOOK] = 0.42

Pr[Airport | SEARCHBOOK] = 0.001 Pr[BookTitle | QUERYFLIGHT] = 0.001 Pr[Airport | QUERYFLIGHT] = 0.73 ... ... ...

term1

K

term2

1

term2

K

term3

1

term3

K

… … …

Pr[SEARCHBOOK] = 0.51

Pr[QUERYFLIGHT] = 0.28

Pr[FINDCOLLEGE] = 0.03

... ... ...

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25 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Flow of evidence Flow of evidence

Dependencies between CategoryDomainDatatype

Category Domain Datatype Book title

?

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26 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Flow of evidence Flow of evidence

Dependencies between CategoryDomainDatatype

?

Category Query book price Domain Datatype Book title

?

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27 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Flow of evidence Flow of evidence

Dependencies between CategoryDomainDatatype

?

Books Category Query book price Domain Datatype Book title

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28 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Web Forms Web Forms

domain datatype1 datatype2 datatype3

departure

station time calendar

arrival

station

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29 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Evaluation Evaluation

129 real Web forms 656 fields

Search Book (44) Find College (2) Search College Book (17) Query Flight (34) Find Job (28) Find Stock Quote (9)

6 domains (number of services in domain)

Book Details Book Edition Book Format Book Search Type Book Title Number of Children City Class College Subject Company Name Country Currency ... ... ... ... Ticker Symbol Time Time Return Time Period Travel Type US State ZIP

72 datataypes (illustrative sample)

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30 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Evaluation Evaluation

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31 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Web Forms? Web Forms?

  • Why forms? Not talking about services?
  • Fully annotated Web Services not available

at time of experiments

  • Web Services actually easier:

HTML parsing causes noise

  • Next experiment will show real Web Services!
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32 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Learning to attach semantic metadata to Web Services Learning to attach semantic metadata to Web Services

✔ Introduction ✔ Our Machine Learning Approach

  • 3. Classification Tasks

✔ Domain & Datatype (Web Forms) b) Category (Web Services)

  • 4. Conclusion & Discussion
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33 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

A Text Classification Problem A Text Classification Problem

  • Text sources:

A) Service Description (plain text, e.g. from UDDI) B) WSDL: service, portType, operation C) WSDL: Input message D) WSDL: Output message

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34 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Ensemble Learning Ensemble Learning

  • Ensemble Learning
  • Each text source contains different words

(e.g. operation “buyBook”, message part “author”)

  • Using seperate learners is more accurate
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35 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Evaluation Evaluation

391 real Web Services 11 categories (number of services in category)

Business (22) Communication (44) Converter (43) Country Information (62) Developers (34) Finder (44) Games (9) Mathematics (10) Money (54) News (30) Web (39) discarded (33)

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36 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Evaluation Evaluation

WSDL only

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37 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Evaluation Evaluation

WSDL plus plain text descriptions (easier)

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38 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Clustering Clustering

  • Clustering
  • Unsupervised – no training data needed
  • Algorithm discovers meaningful groups of services
  • Evaluation
  • All clustering algorithms outperform baseline
  • See paper for details
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39 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Learning to attach semantic metadata to Web Services Learning to attach semantic metadata to Web Services

✔ Introduction ✔ Our Machine Learning Approach ✔ Classification Tasks

✔ Domain & Datatype (Web Forms) ✔ Category (Web Services)

  • 4. Conclusion & Discussion
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40 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Contribution Contribution

  • Summary
  • Bayesian inference algorithm for classifying forms
  • Ensemble learning for classifying Web Services
  • Clustering of Web Services
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41 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Open Issues & Limitations Open Issues & Limitations

  • Open issues
  • Larger Web Service corpus  training data
  • Predictions not good enough yet
  • Limitations: We do not handle...
  • Process Model
  • Compostion & Workflow
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42 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Current & Future Work Current & Future Work

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43 Andreas Heß, Nicholas Kushmerick: Learning to attach semantic metadata to Web Services

Discussion & Questions Discussion & Questions

  • We are gathering annotated Web Services
  • Contact us to use them, or to contribute:
  • (Partially) annotated Web Services in large numbers?
  • Global / Shared Profile Hierarchy?
  • Questions?