1. Xiaomeng Su & Jon Atle Gulla Dept. of Computer and Information Science Norwegian University of Science and Technology Trondheim Norway June 2004 Semantic Enrichment for Ontology Mapping

2. NLDB’04 Page 2 Semantic interoperability  The Semantic Web vision  Ontology heterogeneity problem  Comparison of ontologies should be aided by automatic process  Ontology mapping typically involves identifying correspondences between the source ontologies

3. NLDB’04 Page 3 Prerequisite  Focusing on light-weight ontology  The ontologies share the same domain  The same representation language is assumed  Our approach is based on Referent Modelling Language (RML)

4. NLDB’04 Page 4 Idea illustration  Enrich the concept with extension.  Documents (textual) that belong to a concept considered to be extension.  Using Information Retrieval techniques to compute a representative feature vector of the extension information.  When no extension available, using text categorization to assign documents to concepts.

5. NLDB’04 Page 5 Architecture

6. NLDB’04 Page 6 Functional view of the system  Document assignment (optional)  Feature vector construction Pre-processing Document representation Concept vector construction leaf node -- average vector of the documents vectors non-leaf node -- weighted sum of the documents vectors, sub concept vectors and related concept vectors

7. NLDB’04 Page 7 Functional view of the system  Similarity calculation The similarity of two concepts – cosine measure The similarity of relations – domain and range The similarity calculation of clusters and that of the two ontologies – based on the above two  Post-processing the assertions using WordNet to update the rank of assertions.  Mapping assertion generation and user feedback management

8. NLDB’04 Page 8 Post-processing  To update ranks according to the concept relatedness in WordNet  We use simple path length measurement  JWNL

9. NLDB’04 Page 9 A prototype -iMapper

10. NLDB’04 Page 10 Validation  To measure the accuracy of the mapping algorithm  Focusing on concepts  Using users manually identified mappings as gold standards  Measures Precision (the fraction of automatically discovered mappings that are correct) Recall (the fraction of the correct mappings that have been discovered)  Experiment on two domains Product catalogue integration Tourism Ontology comparison

11. NLDB’04 Page 11  Measure precision at 11 standard recall levels  Experiment in both domains  The algorithm has identified most of the mappings and ranked them in the correct order. Preliminary results

12. NLDB’04 Page 12 Preliminary results  Using WordNet to update the rank showed different effects on the two domains  No topic related semantics in WordNet

13. NLDB’04 Page 13 Evaluation remarks  Encouraging results  Failure analysis Quality of the gold standards Quality of the feature vector  Further evaluation Gold standard Sensitivity tests Alternative measures

14. NLDB’04 Page 14 Summing up  An approach to ontology mapping based on the idea of semantic enrichment  The approach has been implemented and evaluated  Explored the possiblity of incorporating WordNet into the system

15. NLDB’04 Page 15 The end... Thank you!