1. Integrating Taxonomies
瞿裕忠（Yuzhong Qu）

2. Outline Integrating catalogs Leaning to matching ontologies
WWW2001
Leaning to matching ontologies
VLDB2003
Merging ontologies
JWS2006

3. Reference
Agrawal R, Srikant R. On integrating catalogs. Proceedings of the 10th international conference on World Wide Web. ACM, 2001: [157]
Doan, A., Madhavan, J., Dhamankar, R., Domingos,P., and Halevy, A.Y. Learning to Match Ontologies on the Semantic Web. VLDB J. 12(4) (2003), [467]
Kotis K, Vouros G A, Stergiou K. Towards automatic merging of domain ontologies: The HCONE-merge approach. Web Semantics: Science, Services and Agents on the World Wide Web, 2006, 4(1): [91]

4. Integrating Catalogs S1 Sm C1 Cn d1 d2

5. Naive Bayes Classification

6. Enhanced Algorithm Similarity information implicit in the source
Documents in the same source category are more likely belong to the same target category (in the master catalog)

7. Enhanced Algorithm (ENB)

8. Enhanced Algorithm (ENB)

9. Analysis and Experiment
The highest accuracy achievable with the enhanced technique can be no worse than what can be achieved with the standard Naive Bayes classification.
Experiments indicate that the proposed technique can result in large accuracy improvements.

10. Learning to match ontologies

11. The GLUE architecture

12. Joint probability distribution

13. Estimating the joint distribution of concepts

14. The Learners
The Content Learner
The Name Learner
The Meta-Learner

15. Domain-Independent Constraints
Neighborhood
Two nodes match if their children also match.
Two nodes match if their parents match and at least x% of their children also match.
Two nodes match if their parents match and some of their descendants also match.
Union
If all children of node X match node Y, then X also matches Y.

16. Relaxation labeling

17. Experiments Removed all nodes with fewer than 5 instances
On average only 30 to 90 data instances per leaf node

18. Matching accuracy of GLUE
Intermediate approaches: firstly convert one data model to the other, and then reuse certain schema matching or ontology matching methods to discover
simple mappings

19. Ontology merging problem
Mapping them to an intermediate ontology.
Get the minimal union of their (translated) vocabularies and axioms with respect to their alignment.

20. S-morphism and the intermediate ontology
Semantic homomorphism (preserving partial order)

21. Entity category and taxonomy

22. More reference
Zhang D, Lee W S. Web taxonomy integration using support vector machines. Proceedings of the 13th international conference on World Wide Web. ACM, 2004: [60]
Flouris G, Manakanatas D, Kondylakis H, et al. Ontology change: Classification and survey. The Knowledge Engineering Review, 2008, 23(2): [189]

23. 致谢