Mapping Between Taxonomies

1. Mapping Between Taxonomies Elena Eneva 27 Sep 2001 Advanced IR Seminar

2. Taxonomies • Formal systems of orderly classification of knowledge, which are designed for a specific purpose • Change of purpose, change of taxonomies • Businesses often need and keep the information in several structures • Important to be able to automatically map between taxonomies

3. Useful Mappings • Companies, organizing information in various ways (eg. one for marketing, another for product development) • Personal online bookmark classification • Search engines (eg. Google <-> Yahoo) • EU Committee for Standardization “detailed overview of the existing taxonomies officially used in the EU, in order to derive general concepts such as: information organisation, properties, multilinguality, keywords, etc. and, last but not least, the mapping between.”

4. German Textile Approach French Automobile By country By industry

5. German Textile Approach French Automobile By country By industry

6. German Textile Approach French Automobile By country By industry

7. German Textile Approach French Automobile By country By industry

8. Textile Approach Automobile By industry

9. abc abc abc abc abc abc Textile Approach Automobile abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc By industry

10. Textile Approach Automobile abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc By industry

11. Textile Approach Automobile abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc abc By industry

12. German Textile Approach French Automobile By country abc abc abc abc By industry

13. German Textile Approach French Automobile By country abc abc abc abc By industry

14. German Textile Approach French Automobile By country abc abc abc abc By industry abc abc abc abc

15. Learning Algorithms • 2 separate learners for the documents • Old doc category -> new doc category • Doc contents -> new category • Weighted average based on confidence • Final result determined by a decision tree • One combined learner – used both old category and contents as features • Use the unlabeled data for bootstrapping (eg. top 1%)

16. Learners • Decision Tree (C4.5) • Naïve Bayes Classifier (Rainbow) • Support Vector Machine (SVM-Light) • KNN (from Yiming)

17. Datasets Two classification schemes: • Reuter 2001 • Topics • Industry categories • Hoovers-255 and Hoovers-28 • 28 industry categories • 255 industry categories • Web pages from Google and Yahoo

18. Related Literature • Reconciling Schemas of Disparate Data Sources: A Machine Learning Approach, A. Doan, P. Domingos, and A. Halevy. Proceedings of the ACM SIGMOD Conf. on Management of Data (SIGMOD-2001) • Learning Source Descriptions for Data Integration, A. Doan, P. Domingos, and A. Levy. Proceedings of the Third International Workshop on the Web and Databases (WebDB-2000), pages 81-86, 2000. Dallas, TX: ACM SIGMOD. • Learning Mappings between Data Schemas , A. Doan, P. Domingos, and A. Levy. Proceedings of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data, 2000, Austin, TX.

19. Questions and Ideas • Other possible datasets? • Other learners? • Other papers? The end.