Iterative Ensemble Classification for Relational Data A Case Study - PowerPoint PPT Presentation

Outline Introduction Iterative Ensemble Classification Conclusion Iterative Ensemble Classification for Relational Data A Case Study of Semantic Web Services Andreas Heß and Nicholas Kushmerick University College Dublin, Ireland September 18, 2004 4 , Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Introduction Iterative Ensemble Classification Conclusion Outline Introduction 1 Relational Learning (simplified) Motivation: Semantic Web Services Relational Learning for Web Services Iterative Ensemble Classification 2 Iterative Algorithms Specialised Classifiers Evaluation Conclusion 3 Summary Current and Future Work Discussion Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Outline Introduction 1 Relational Learning (simplified) Motivation: Semantic Web Services Relational Learning for Web Services Iterative Ensemble Classification 2 Iterative Algorithms Specialised Classifiers Evaluation Conclusion 3 Summary Current and Future Work Discussion Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Relational Learning Relational Data consists of objects and relations between objects can be represented as a graphs Three Types of Learning Tasks (following Slattery) classify nodes classify graphs classify subgraphs Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Relational Learning Relational Data consists of objects and relations between objects can be represented as a graphs Three Types of Learning Tasks (following Slattery) classify nodes classify graphs classify subgraphs Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Classifying Nodes Task Learn labels for nodes Instances Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Classifying Subgraphs Task Learn labels for subgraphs Instances Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Classifying Graphs Task Learn labels for graphs Instances Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Relational Learning: Examples and Methods Examples for Relational Learning Classical task: classify web pages Methods for relational learning Iterative algorithms Statistical methods Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Two Views Intrinsic View Features inherent to instance e.g. text from web page Extrinsic View Relations between instances e.g. class labels of linked web pages (Following Neville and Jensen) Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Two Views Intrinsic View Features inherent to instance e.g. text from web page Extrinsic View Relations between instances e.g. class labels of linked web pages (Following Neville and Jensen) Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Two Views Intrinsic View Features inherent to instance e.g. text from web page Extrinsic View Relations between instances e.g. class labels of linked web pages (Following Neville and Jensen) Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Two Views Intrinsic View Features inherent to instance e.g. text from web page Extrinsic View Relations between instances e.g. class labels of linked web pages (Following Neville and Jensen) Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Now for Something Completely Different Introduction 1 Relational Learning (simplified) Motivation: Semantic Web Services Relational Learning for Web Services Iterative Ensemble Classification 2 Iterative Algorithms Specialised Classifiers Evaluation Conclusion 3 Summary Current and Future Work Discussion Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Web Services Web Services Web-accessible software XML (SOAP) over HTTP Just RPC? Forms? Data Integration? Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Web Service Descriptions (WSDL) Web Services consist of: Operations (methods) Messages (parameters) Complex types (structures) Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Semantic Web Services Desired Features Automatic discovery Automatic composition Automatic invocation Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Simple Scenario Congo ? ● author ● title ● quantity ? WindingStair ● authName ● bookT Scenario: ● ISBN Buying a book Teatime ● region ● qlty ● qty Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Simple Scenario Congo ● author ● title ● quantity WindingStair Global Ontology ● authName ● bookT ● Item ● ISBN ➢ Quantity ➢ Price ● Book ➢ Author Teatime ➢ Title ➢ ISBN ● region ● Tea ➢ Region ● qlty ➢ Quality ● qty Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Simple Scenario Congo ● author ● title ● quantity Semantic Metadata WindingStair Global Ontology ● authName ● bookT ● Item ● ISBN ➢ Quantity ➢ Price (handcrafted) ● Book ➢ Author Teatime ➢ Title ➢ ISBN ● region ● Tea ➢ Region ● qlty ➢ Quality ● qty Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Simple Scenario Congo ! ● author ● title ● quantity WindingStair Global Ontology ● authName ● bookT ● Item ● ISBN ➢ Quantity ➢ Price ● Book ➢ Author Teatime ➢ Title ➢ ISBN ● region ● Tea ➢ Region ● qlty ➢ Quality ● qty Andreas Heß Iterative Ensemble Classification for Relational Data

Outline Relational Learning (simplified) Introduction Motivation: Semantic Web Services Iterative Ensemble Classification Relational Learning for Web Services Conclusion Semantic Metadata Assumptions Semantic annotation Shared ontology Problem Hand-crafting annotations can be tedious Our Solution Learn mappings from text to ontology Andreas Heß Iterative Ensemble Classification for Relational Data