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Seed-based Generation of Personalized Bio- Ontologies for Information Extraction

Seed-based Generation of Personalized Bio- Ontologies for Information Extraction. Cui Tao & David W. Embley Data Extraction Research Group Department of Computer Science Brigham Young University. Supported by NSF. Personalized Information Harvesting.

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Seed-based Generation of Personalized Bio- Ontologies for Information Extraction

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  1. Seed-based Generation of PersonalizedBio-Ontologies for Information Extraction Cui Tao & David W. Embley Data Extraction Research Group Department of Computer Science Brigham Young University Supported by NSF

  2. Personalized Information Harvesting • Biology domain  huge (other domains too) • Data collection • Many (web) sources • Only a tiny subpart wanted • Personalized view • Personalized extraction ontology • Creation: Form specification • Application: Seed-based harvesting

  3. Example • Harvest information about large proteins in humans and the functions of these proteins • Find proteins in humans that are >20 kDa • Find all the proteins in humans that serve as receptors • ... • Information sources  various online repositories • NCBI • Gene Cards • The Gene Ontology • GPM Proteomics Database • …

  4. Extraction Ontology T-complex protein 1 subunit theta TCP-1-theta CCT-theta Renal carcinoma antigen NY-REN-15 … Instance: ^\d{1,5}(\.\d{1,2})? Context: weight|wght|wt\. Unit: kilodaltons?|kdas?|kds?|das?|daltons? …

  5. Extraction Ontology Unfortunately Hard to Construct T-complex protein 1 subunit theta TCP-1-theta CCT-theta Renal carcinoma antigen NY-REN-15 … Instance: ^\d{1,5}(\.\d{1,2})? Context: weight|wght|wt\. Unit: kilodaltons?|kdas?|kds?|das?|daltons? …

  6. Can We Make Construction Easier? • Forms • General familiarity • Reasonable conceptual framework • Appropriate correspondence • Transformable to ontological descriptions • Capable of accepting source data • Instance recognizers • Some pre-existing instance recognizers • Lexicons • Need for a full extraction ontology?

  7. Form Creation User Interface • Basic form-construction facilities: • single-entry field • multiple-entry field • nested form • …

  8. Created Sample Form

  9. Generated Ontology View

  10. Source-to-Form Mapping Establishing a Seed

  11. Source-to-Form Mapping Establishing a Seed

  12. Source-to-Form Mapping Establishing a Seed

  13. Source-to-Form Mapping Establishing a Seed

  14. Almost Ready to Harvest … • Need reading path: DOM-tree structure • Need to resolve mapping problems • Split/Merge • Union/Selection

  15. Almost Ready to Harvest … • Need reading path: DOM-tree structure • Need to resolve mapping problems • Split/Merge • Union/Selection Name Voltage-dependent anion-selective channel protein 3 VDAC-3 hVDAC3 Outer mitochondrial membrane Protein porin 3

  16. Almost Ready to Harvest … • Need reading path: DOM-tree structure • Need to resolve mapping problems • Split/Merge • Union/Selection Name Voltage-dependent anion-selective channel protein 3 VDAC-3 hVDAC3 Outer mitochondrial membrane Protein porin 3

  17. Almost Ready to Harvest … • Need reading path: DOM-tree structure • Need to resolve mapping problems • Split/Merge • Union/Selection Name T-complex protein 1 subunit theta TCP-1-theta CCT-theta Renal carcinoma antigen NY-REN-15

  18. Almost Ready to Harvest … • Need reading path: DOM-tree structure • Need to resolve mapping problems • Split/Merge • Union/Selection Name T-complex protein 1 subunit theta TCP-1-theta CCT-theta Renal carcinoma antigen NY-REN-15

  19. Can Now Harvest Name

  20. Can Now Harvest Name 14-3-3 protein epsilon Mitochondrial import stimulation factor Lsubunit Protein kinase C inhibitor protein-1 KCIP-1 14-3-3E

  21. Can Now Harvest Name Voltage-dependent anion-selective channel protein 3 VDAC-3 hVDAC3 Outer mitochondrial membrane Protein porin 3

  22. Can Now Harvest Name Tryptophanyl-tRNA synthetase, mitochondrial precursor EC 6.1.1.2 Tryptophan—tRNA ligase TrpRS (Mt)TrpRS

  23. Harvesting Populates Ontology

  24. Harvesting Populates Ontology Also helps adjust ontology constraints

  25. Can Harvest from Additional Sites Name T-complex protein 1 subunit theta TCP-1-theta CCT-theta Renal carcinoma antigen NY-REN-15

  26. Larger Picture • Information Harvesting • Not only for biology, but for any application • Not only from one site, but from many sites • Opportunities • Extraction ontology creation • Automating site-to-site information harvesting • Automatic semantic annotation • Data/Ontology transformations

  27. Extraction Ontology Creation Lexicons Name 14-3-3 protein epsilon Mitochondrial import stimulation factor Lsubunit Protein kinase C inhibitor protein-1 KCIP-1 14-3-3E … 14-3-3 protein epsilon Mitochondrial import stimulation factor Lsubunit Protein kinase C inhibitor protein-1 KCIP-1 14-3-3E … T-complex protein 1 subunit theta TCP-1-theta CCT-theta Renal carcinoma antigen NY-REN-15 … Tryptophanyl-tRNA synthetase, mitochondrial precursor EC 6.1.1.2 Tryptophan—tRNA ligase TrpRS (Mt)TrpRS … Name T-complex protein 1 subunit theta TCP-1-theta CCT-theta Renal carcinoma antigen NY-REN-15 Name Tryptophanyl-tRNA synthetase, mitochondrial precursor EC 6.1.1.2 Tryptophan—tRNA ligase TrpRS (Mt)TrpRS

  28. Automatic Source-to-Form Mapping

  29. Automatic Semantic Annotation

  30. Extraction Ontology Creation Instance Recognizers Number Patterns Context Keywords and Phrases

  31. Automatic Source-to-Form Mapping

  32. Automatic Semantic Annotation Recognize and annotate with respect to an ontology

  33. Ontology Transformation OWL & RDF: standard ontology languages XML & XMLS: data exchange Forms: form filling to populate an ontology

  34. Ontology Transformation Transformations to and from all

  35. Contributions • Personalized ontology creation • Mapping from sources • Information harvesting • Opportunities for further work • Extraction ontology creation • Semantic Annotation • Data/Ontology transformations www.deg.byu.edu

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