mapping between taxonomies
DESCRIPTION
Mapping Between Taxonomies. Elena Eneva 27 Sep 2001 Advanced IR Seminar. 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 - PowerPoint PPT PresentationTRANSCRIPT
Mapping Between Taxonomies
Elena Eneva
27 Sep 2001
Advanced IR Seminar
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 theinformation in several structures
Important to be able to automatically map between taxonomies
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.”
ApproachGerman
French
Textile
Automobile
By country
By industry
ApproachGerman
French
Textile
Automobile
By country
By industry
ApproachGerman
French
Textile
Automobile
By country
By industry
ApproachGerman
French
Textile
Automobile
By country
By industry
ApproachTextile
Automobile
By industry
ApproachTextile
Automobile
By industry
abcabcabcabcabcabc
abcabcabcabcabcabc
abcabcabcabcabcabc
abcabcabcabcabcabc
ApproachTextile
Automobile
By industry
abcabcabcabcabcabc
abcabcabcabcabcabc
abcabcabcabcabcabc
abcabcabcabcabcabc
ApproachTextile
Automobile
By industry
abcabcabcabcabcabc
abcabcabcabcabcabc
abcabcabcabcabcabc
abcabcabcabcabcabc
ApproachGerman
French
Textile
Automobile
By country
By industry
abc abc abc abc
ApproachGerman
French
Textile
Automobile
By country
By industry
abc abc abc abc
ApproachGerman
French
Textile
Automobile
By country
By industry
abc abc abc abc
abc abc abc abc
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%)
Learners
Decision Tree (C4.5)Naïve Bayes Classifier (Rainbow)Support Vector Machine (SVM-Light)KNN (from Yiming)
DatasetsTwo classification schemes:
Reuter 2001 Topics Industry categories
Hoovers-255 and Hoovers-28 28 industry categories 255 industry categories
Web pages from Google and Yahoo
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.
Questions and Ideas
Other possible datasets?
Other learners?
Other papers?
The end.