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Mini-Ontology Generation from Canonicalized Tables

Mini-Ontology Generation from Canonicalized Tables. Stephen Lynn Data Extraction Research Group Department of Computer Science Brigham Young University. Supported by the. TANGO Overview. TANGO: Table ANalysis for Generating Ontologies Project consists of the following three components:.

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Mini-Ontology Generation from Canonicalized Tables

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

  2. TANGO Overview TANGO: Table ANalysis for Generating Ontologies Project consists of the following three components: • Transform tables into a canonicalized form • Generate mini-ontologies • Merge into a growing ontology

  3. 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. Sample Input Sample Output

  5. 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. Concept/Value Recognition • Lexical Clues • Data value assignment • Labels as data values • Default • Classifies any unclassified elements according to simple heuristic. Region State Concepts and Value Assignments Population Latitude Longitude Northeast Northwest Delaware Maine Oregon Washington 2,122,869 817,376 1,305,493 9,690,665 3,559,547 6,131,118 45 44 45 43 -90 -93 -120 -120

  7. Relationship Discovery • Dimension Tree Mappings • Lexical Clues • Generalization/Specialization • Aggregation • Data Frames • Ontology Fragment Merge

  8. Constraint Discovery • Generalization/Specialization • Computed Values • Functional Relationships • Optional Participation

  9. 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

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

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