Mini-Ontology Generation from Canonicalized Tables

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Mini-Ontology Generation from Canonicalized Tables

• Stephen Lynn
• Data Extraction Research Group
• Department of Computer Science
• Brigham Young University

Supported by the
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TANGO Overview
TANGO Table ANalysis for Generating
Ontologies Project consists of the following
three components

1. Transform tables into a canonicalized form
2. Generate mini-ontologies
3. Merge into a growing ontology

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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.

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Sample Input
Region and State Information Region and State Information Region and State Information Region and State Information
Location Population (2000) Latitude Longitude
Northeast 2,122,869
Delaware 817,376 45 -90
Maine 1,305,493 44 -93
Northwest 9,690,665
Oregon 3,559,547 45 -120
Washington 6,131,118 43 -120
Sample Output
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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.
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Concept/Value Recognition

• Lexical Clues
• Data value assignment
• Labels as data values
• Default
• Classifies any unclassified elements according to
  simple heuristic.

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Relationship Discovery

• Dimension Tree Mappings
• Lexical Clues
• Generalization/Specialization
• Aggregation
• Data Frames
• Ontology Fragment Merge

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Constraint Discovery

• Generalization/Specialization
• Computed Values
• Functional Relationships
• Optional Participation

Region and State Information Region and State Information Region and State Information Region and State Information
Location Population (2000) Latitude Longitude
Northeast 2,122,869
Delaware 817,376 45 -90
Maine 1,305,493 44 -93
Northwest 9,690,665
Oregon 3,559,547 45 -120
Washington 6,131,118 43 -120
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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

Precision Recall
Concept Recognition
Relationship Discovery
Constraint Discovery
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Contribution

• Tool to generate mini-ontologies
• Assessment of accuracy of automatic generation