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Applications

Applications. Chapter 9, Cimiano Ontology Learning Textbook Presented by Aaron Stewart. Typical Applications of Ontologies. Agent communication Data integration Description of service capabilities for matching and composition purposes Formal verification of process descriptions

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Applications

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  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 EvaluationRequirements • Algorithm output can be quantified • Task can use background knowledge • Ontology is an additional parameter • Output can be traced to the ontology

  6. Contents • Text Clustering and Classification • Information Highlighting for Supporting Search • Related Work

  7. Text Clustering and Classification • What is the difference?

  8. Text Clustering

  9. Text Classification Arrows Weather Flat shapes 3-D forms Smile!

  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? quick brown the fox

  13. Bag of Words the quick brown fox jumps over the lazy dog (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

  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

  23. 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. Natural Language Processing • 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

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