1. Ontology Learning Mining Functional Dependencies from Data Hong Yao and Howard J. Hamilton Presented By Stephen Lynn

2. Ontology Learning Rule Mining  Algorithmic process that takes data as input and yields rules such as:  Association Rules  Implications  Functional dependencies

3. Ontology Learning Overview  Goals/Objectives  Implication/Functional Dependencies  Base Algorithm  4 Pruning Rules  Evaluation  Analysis

4. Ontology Learning Goals and Objectives Design an efficient rule discovery algorithm for mining functional dependencies from a dataset.

5. Ontology Learning Implication  Describes relationship between one specific combination of attribute-value pairs.  Binary Data  Propositional Logic {milk, eggs} → {bread}

6. Ontology Learning Functional Dependency  Describe relationship between all possible combinations of attribute-value pairs.  Disjoint attributes  True regardless of how many possible attribute values  antecedent → consequent postcode → areacode

7. Ontology Learning Search Space

8. Ontology Learning Armstrong’s Axioms

9. Ontology Learning Equivalent Attributes

10. Ontology Learning Nontrivial Closure

11. Ontology Learning Base Algorithm  Generate all possible antecedents then test with possible consequents (1 level at a time)

12. Ontology Learning Pruning Rules

13. Ontology Learning FD_Mine

14. Ontology Learning Experimental Summary  15 Datasets from UCI Machine Learning Repository (2005)

15. Ontology Learning Results

16. Ontology Learning Results

17. Ontology Learning Runtime

18. Ontology Learning Analysis  Strengths  Nicely drawn proofs  Weaknesses  Missing good example  Nice to show results with/without pruning  Future Work  Find multivalued dependencies  Find conditional dependencies  Data cleaning