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

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

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

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

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

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

Ontology Learning Search Space

Ontology Learning Armstrong’s Axioms

Ontology Learning Equivalent Attributes

Ontology Learning Nontrivial Closure

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

Ontology Learning Pruning Rules

Ontology Learning FD_Mine

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

Ontology Learning Results

Ontology Learning Results

Ontology Learning Runtime

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