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Issues in Learning an Ontology from Text
- 1. Issues in Learning an Ontology from Text Christopher Brewster, Simon Jupp, Joanne Luciano, David Shotton, Robert Stevens, and Ziqi Zhang
- 2. The Use Case: Animal Behaviour • Animal behaviour community recognises the need for an ontology, e.g. for video annotation/retrieval • The community created an “Animal Behaviour Ontology” - 339 terms • Can we (semi-) automatically build from text?
- 3. Some Questions • Do we get a “good ontology”? • If not, is it useful? • Is it low-effort? • Should the result be “tidied up” or used as a donor?
- 4. Methodology: Dataset • Journal “Animal Behaviour” from Elsevier • 623 articles from Vol 71 (2006) - Vol 74 (2007) • 2.2 million words • Various formats - most usefully xml
- 5. We Want an Ontology of Green • An ontology of “animal behaviours” • Not an ontology of the corpus We want the green terms in the ontology
- 6. Processing Steps (1) 1. Text extracted from XML - excluding affiliations, acknowledgements, bibliography except for title etc. 2. Noise removed - person names, animal names, place names 3. Lemmatiser used to reduce data sparsity 4. Term extraction applied
- 7. Processing Steps (2) 5. Term selection Regular expression used to select terms ending in behaviour, display, construction, inspection plus generic -ing, -ism, etc. Build hierarchies using String Inclusion 5. Top level terms filtered using “Hearst Patterns” to test if X ISA behaviour/activity/etc. Walking Running Jumping Hunting Pecking Reed Bunting Corn Bunting Herring Courtship Studentship Cannibalism Dimorphism
- 8. Applying String Inclusion /Rules to Terms C BCAC ABC Selection Mate Selection Natural Selection Female Mate Selection
- 9. Lexico-Syntactic Patterns • X such as P, Q, R; X is a Y • Grooming is a behaviour • Copulation is an activity • Dimorphism is a behaviour • Calls such as trills, whistles, grunts
- 10. Results • 64,000 terms extracted • The regexp selected 10,335 terms • Step 6a resulted in an ontology with 17,776 classes and 1295 top level classes • Step 6b resulted in an ontology with 13,058 classes and 912 top level classes
- 11. Results (2) - Copulation Sub-tree
- 12. Results(3) • Evaluation of terms excluded by regexp: • 56,000 terms excluded • Random sample of 3140 terms evaluated by hand • 7 verbs and 42 nouns should not have been excluded • E.g., “interaction” • A recall of .905
- 13. Discussion: The problem of focus
- 14. Other Issues • More a vocabulary than an ontology • SKOS-like rather than OWL-like • Can deal with “selection”, “mate selection” and “natural selection • Highly compositional terms “Adult male grooming behaviour” • Cleanish list of top level terms: Canabalism, copulation, eating, foraging, fighting, grooming
- 15. Discussion: Is it useful? • Answers: No, yes, yes, donor • Useful ontological fragments • Bringing ontology to ontology learning is the research challenge • Limitations: noise; the problem of focus; only taxonomic relations • Advantages: speed; ease; a step towards formal ontologies
Editor's Notes
- copulation --> grandfather copulation, cannibalism copulation, harassment copulation, inferred copulation, long copulation, palp copulation, elements copulation, behavioural elements copulation, face to copulation