1. Applications Chapter 9, Cimiano Ontology Learning Textbook Presented by Aaron Stewart

2. Typical Applications of Ontologies Agent communication Data integration Description of service capabilities for matching and composition purposes Formal verification of process descriptions Unification of terminology across communities

3. Text Applications of Ontologies Information Retrieval (IR) Clustering and Classification of Documents Semantic Annotation Natural Language Processing

4. Task-Based Evaluation (Porzel and Malaka 2005)

5. Task-Based Evaluation Requirements 1.Algorithm output can be quantified 2.Task can use background knowledge 3.Ontology is an additional parameter 4.Output can be traced to the ontology

6. Contents 1.Text Clustering and Classification 2.Information Highlighting for Supporting Search 3.Related Work

7. Text Clustering and Classification What is the difference?

8. Text Clustering

9. Text Classification ArrowsWeatherFlat shapes3-D formsSmile!

10. Dot Kom Project One of many competitions

11. Approaches Bag of words Manually engineered MeSH Tree Structures Automatically constructed ontologies

12. What is a “Bag of Words” anyway? the quick brown fox

13. Bag of Words thequickbrownfoxjumpsoverthelazydog (2)

14. Building Hierarchies

15. Note on Ontologies Our ontologies (“micro”) – Like a database record schema Their ontologies (“macro”) – Like WordNet

16. Clustering Hierarchical Agglomerative Clustering Bi-Section K-means “A Comparison of Document Clustering Techniques” – www.cs.sfu.ca/~wangk/894report/chen1.pdf www.cs.sfu.ca/~wangk/894report/chen1.pdf

17. Document Representations Bag of Words Certain words + ontology -> extended features Strategies: add, replace, only

18. Vectors and Cosine Similarity

19. Classification Results (Categories)

20. Classification Results (Documents)

21. Cluster Metrics P : computer-generated clusters L : human-created clusters P, L : sets of clusters (partitioning)

22. Clustering Results

24. Information Highlighting for Supporting Search Challenge: – 10 minute limit – KMi Planet News web site – Compile a list of important People Technologies

25. Information Highlighting for Supporting Search Tools: – Regular browser – Magpie – ESpotter – C-PANKOW

26. Teams A : web browser only B : web browser with AKT information C : web browser with AKT++ information

27. AKT++ Lexicon

28. Scores

29. Conclusions (for this section) Generated ontologies can be comparable to hand-crafted ontologies Humans can trust the computer too much! (Group C drop in score)

30. Related Work Query Expansion Information Retrieval Text Clustering and Classification Natural Language Processing

31. Ambiguity resolution – Bank Compounds – Headache medicine Vague words – With, of, has – Selectional restrictions Anaphora

32. More Applications Word sense disambiguation Classification of unknown words Named Entity Recognition (NER) Anaphora Resolution Question Answering – Who wrote the Hobbit? – Tolkien is the author of the Hobbit. Information Extraction – AUTOSLOG, ASIUM

33. Analysis/Conclusion Pro/con: – Focused on two systems – Passing survey of others