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Thesis Defense 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
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Thesis Defense 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:
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Thesis Defense 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
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Thesis Defense Mini-Ontology GeneratOr (MOGO) Concept/Value Recognition Relationship Discovery Constraint Discovery
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Thesis Defense Mini-Ontology GeneratOr (MOGO) Concept/Value Recognition Lexical Clues Labels as data values Data value assignment Data Frame Clues Labels as data values Data value assignment Default Classifies any unclassified elements according to simple heuristic. Concepts and Value Assignments Northeast Northwest Delaware Maine Oregon Washington Location 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 Year 2002 2003
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Thesis Defense Mini-Ontology GeneratOr (MOGO) Relationship Discovery Dimension Tree Mappings Lexical Clues Generalization/Specialization Aggregation Data Frames Ontology Fragment Merge
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Thesis Defense 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
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Thesis Defense 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 PrecisionRecallF-measure Concept Recognition 87%94%90% Relationship Discovery 73%81%77% Constraint Discovery 89%91%90%
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Thesis Defense Mini-Ontology GeneratOr (MOGO) Concept Recognition What we counted: Correct/Incorrect/Missing Concepts Correct/Incorrect/Missing Labels Data value assignments
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Thesis Defense Mini-Ontology GeneratOr (MOGO) Relationship Discovery What we counted: Correct/incorrect/missing relationship sets Correct/incorrect/missing aggregations and generalization/specializations
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Thesis Defense Mini-Ontology GeneratOr (MOGO) Constraint Discovery What we counted: Correct/Incorrect/Missing: Generalization/Specialization constraints Computed value constraints Functional constraints Optional constraints
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Thesis Defense Mini-Ontology GeneratOr (MOGO) Concept Recognition Successes 98% of concepts identified Missing label identification 97% of values assigned to correct concept Common problems Finding an appropriate label Duplicate concepts
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Thesis Defense Mini-Ontology GeneratOr (MOGO) Relationship Discovery Recall of 92% for relationship sets Missing aggregations and generalizations/specializations Only found in label nesting
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Thesis Defense Mini-Ontology GeneratOr (MOGO) Constraint Discovery F-measure of 98% for functional relationship sets Poor computed value discovery Rows/Columns with totals
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Thesis Defense Mini-Ontology GeneratOr (MOGO) Conclusions Tool to generate mini-ontologies Assessment of accuracy of automatic generation PrecisionRecallF-measure Concept Recognition 87%94%90% Relationship Discovery 73%81%77% Constraint Discovery 89%91%90%
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Thesis Defense Mini-Ontology GeneratOr (MOGO) Future Work Tool Enhancements Linguistic processing Data frame library Domain specific heuristics Alternate Uses Annotation for the Semantic Web
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