1. Thesis Proposal Mini-Ontology GeneratOr (MOGO) Mini-Ontology Generation from Canonicalized Tables Stephen Lynn Data Extraction Research Group Department of Computer Science Brigham Young University Supported by the

2. Thesis Proposal Mini-Ontology GeneratOr (MOGO) TANGO Overview 1.Transform tables into a canonicalized form 2.Generate mini-ontologies 3.Merge into a growing ontology TANGO: Table ANalysis for Generating Ontologies Project consists of the following three components:

3. Thesis Proposal Mini-Ontology GeneratOr (MOGO) Thesis Statement  Proposed Solution  Develop a tool to accurately generate mini-ontologies from canonicalized tables of data automatically, semi- automatically, or manually.  Evaluation  Evaluate accuracy of tool with respect to: concept/value recognition, relationship discovery, and constraint discovery.

4. Thesis Proposal Mini-Ontology GeneratOr (MOGO) Sample Input Region and State Information LocationPopulation (2000)LatitudeLongitude Northeast2,122,869 Delaware817,37645-90 Maine1,305,49344-93 Northwest9,690,665 Oregon3,559,54745-120 Washington6,131,11843-120 Sample Output

5. Thesis Proposal Mini-Ontology GeneratOr (MOGO)  Concept/Value Recognition  Relationship Discovery  Constraint Discovery NOTE: MOGO implements a base set of algorithms for each step of the process and allows for runtime integration of new algorithms.

6. Thesis Proposal Mini-Ontology GeneratOr (MOGO) Concept/Value Recognition  Lexical Clues  Data value assignment  Labels as data values  Default  Classifies any unclassified elements according to simple heuristic. Concepts and Value Assignments Northeast Northwest Delaware Maine Oregon Washington PopulationLatitudeLongitude 2,122,869 817,376 1,305,493 9,690,665 3,559,547 6,131,118 45 44 45 43 -90 -93 -120 RegionState

7. Thesis Proposal Mini-Ontology GeneratOr (MOGO) Relationship Discovery  Dimension Tree Mappings  Lexical Clues  Generalization/Specialization  Aggregation  Data Frames  Ontology Fragment Merge

8. Thesis Proposal Mini-Ontology GeneratOr (MOGO) Constraint Discovery  Generalization/Specialization  Computed Values  Functional Relationships  Optional Participation Region and State Information LocationPopulation (2000)LatitudeLongitude Northeast2,122,869 Delaware817,37645-90 Maine1,305,49344-93 Northwest9,690,665 Oregon3,559,54745-120 Washington6,131,11843-120

9. Thesis Proposal Mini-Ontology GeneratOr (MOGO) Validation  Concept/Value Recognition  Correctly identified concepts  Missed concepts  False positives  Data values assignment  Relationship Discovery  Valid relationship sets  Invalid relationship sets  Missed relationship sets  Constraint Discovery  Valid constraints  Invalid constraints  Missed constraints PrecisionRecall Concept Recognition Relationship Discovery Constraint Discovery

10. Thesis Proposal Mini-Ontology GeneratOr (MOGO) Contribution  Tool to generate mini-ontologies  Assessment of accuracy of automatic generation