1. Similarity Measures for Query Expansion in TopX Caroline Gherbaoui Universität des Saarlandes Naturwissenschaftlich-Technische Fak. I Fachrichtung 6.2 - Informatik Max-Planck-Institut für Informatik AG 5 - Datenbanken und Informationssysteme Prof. Dr. Gerhard Weikum

2. Overview background knowledge similarity measures for the query expansion evaluation of the computed similarity values changes in TopX conclusion

3. Background top-k query processing  provides k most relevant results query expansion  extends source query terms word sense disambiguation  extracts correct meaning ontology  amount of terms with their meanings and semantic relations

4. Word Sense Disambiguation „java, coffee“ „java “ „island“ „coffee“ „programming language“ …

5. Query Expansion „COFFEE“„drink, espresso“

6. TopX top-k retrieval engine text and XML data word sense disambiguation query expansion ontology

7. TopX – WordNet Ontology lexicon for the English language hierarchical relations one relation  one direction ~160,000 words ~120,000 synsets ~210,000 relations

8. TopX – YAGO Ontology Wikipedia and WordNet hierarchical and not hierarchical relations one relation  two directions ~2,100,000 words ~2,200,000 concepts ~6,000,000 relations

9. Similarity Measures Dice similarity  the already used measure in TopX NAGA similarity  applied measure for YAGO Best WordNet similarity  measure with best result among WordNet measures

10. Dice Similarity Measure sdfsdf measures the intersection of two regions

11. NAGA Similarity Measure sdfasfsdf combination of the confidence of a relation and the informativeness of a relation

12. Best WordNet Similarity Measure sdfsdfsdf product of the transfer function of the path length and the transfer function of the concept depth

13. Evaluation

14. DICE measure  applicable  also on the YAGO ontology NAGA measure  applicable  with omitting of the forward direction Best WordNet measure  not applicable  due to the density of YAGO

15. Changes for TopX tuning of some procedures  Dijkstra algorithm  word sense disambiguation  query expansion extension of configuration file

16. Conclusion larger knowledge base more flexibility increased complexity further measure for the similarity computation  NAGA similarity

17. Questions?