Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach AnHai Doan Pedro Domingos Alon Halevy

Data Integration

Problem & Solution Problem Large-scale Data Integration Systems Bottleneck: Semantic Mappings 1-1 Mappings Solution Multi-strategy Learning Integrity Constraints XML Structure Learner

Learning Source Descriptions (LSD) Components Base learners Meta-learner Prediction converter Constraint handler Operations Training phase Matching phase

Learners Basic Learners Name Matcher (Whirl) Content Matcher (Whirl) Naïve Bayes Learner County-Name Recognizer XML Learner Meta-Learner (Stacking)

XML Learner

XML Learner (Cont.)

Constraint Handler Domain Constraints

Constraint Handler (Cont.) Search Heuristic Mapping Cost

Training Phase

Example1 (Training Phase)

Example1 (Cont.)

(“location” ， ADDRESS) (“Miami, FL”, ADDRESS)

Matching Phase

Example2 (Matching Phase)

Example2 (Cont.)

Empirical Evaluation

Measures Matching accuracy of a source Average matching accuracy of a source Average matching accuracy of a domain

Experiment Result

Experiment Result (Cont.) Contributions of base learners and the constraint handler

Experiment Result (Cont.) Contributions of Schema information and Data Instances

Experiment Result (Cont.) Performance sensitivity to the amount of data instances

Limitations Enough Training Data Domain Dependent Learners Ambiguities in Sources Efficiency Overlapping of Schemas

Conclusion and Future Work Improve over time Extensible framework Multiple types of knowledge Non 1-1 mapping ?