Automatic Semantic Annotation of the          Cyttron Database                              David Graus                   ...
Part IWhat?
What » How » WhyWhat?   Automatic Semantic Annotation of the Cyttron Database
What » How » WhyWhat?        Semantic Annotation
What » How » WhyExample“ Company XYZ announced profits in Q3, planning to         build a $120M plant in Bulgaria.”
What » How » WhyIt is like tagging“ Company XYZ announced profits in Q3, planning to         build a $120M plant in Bulgar...
What » How » WhyIt is not tagging“ Company XYZ announced profits in Q3, planning to         build a $120M plant in Bulgari...
What » How » WhyIt is not tagging“ Company XYZ announced profits in Q3, planning to         build a $120M plant in Bulgari...
What » How » WhyIt is not tagging“ Company XYZ announced profits in Q3, planning to         build a $120M plant in Bulgari...
What » How » WhyIt adds context!                             source: ontottext.com
What » How » WhyWhat?   Automatic Semantic Annotation of the Cyttron Database
What » How » WhyWhat?        Cyttron Database
What » How » WhyCyttron Database                   "The volume of the brain evaluated in this                   study. The...
What » How » WhyNCI Thesaurus
What » How » WhyNCI ThesaurusConcept:        http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#Brain
What » How » WhyNCI ThesaurusLabel:          Brain
What » How » WhyNCI ThesaurusDefinition:     An organ composed of grey and white matter                containing billions...
What » How » WhyNCI ThesaurusContext:           Brain               is a      Central Nervous System Part           Brain ...
What » How » WhyNCI Thesaurus
Part IIHow?
What » How » WhyApproach I
What » How » WhyKeyword Extraction
What » How » WhyKeyword Extraction   x6
What » How » WhySemantic Annotation
What » How » WhyApproach II
What » How » WhyTopic Classification
What » How » WhyEvaluation             I    I    I    I         I      I             I    I    I    I         I      I    ...
What » How » WhyEvaluation             1   I    I    I    I         I      I             2   I    I    I    I         I   ...
What » How » WhyEvaluation             ?             1   I    I    I    I         I      I             2   I    I    I    ...
What » How » WhyEvaluation IConfusion Matrix
What » How » WhyEvaluation IISemantic Similarity
What » How » WhyEvaluation IISemantic Similarity    Human                        Sagittal Plane    Brain                  ...
What » How » WhyEvaluation IISemantic Similarity    Human                        Sagittal Plane    Brain                  ...
What » How » WhyVisualization   DEMO
Part IIIWhy?
What » How » WhyEvaluation
What » How » WhyResults1. No ‘agreement’ between experts2. Annotation method I best approach3. Both Annotation II & Random...
What » How » WhyWhat is it good for? / Future Work1. Domain independent method2. Clustering topic identification3. Subgrap...
FinQuestions?
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Semantic Annotation of the Cyttron Database

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Final Presentation for my MSc Graduation Project.

Abstract:
"Semantic annotation uses human knowledge formalized in ontologies to enrich texts, by providing structured and machine-understandable information of its content. This paper proposes an approach for automatically annotating texts of the Cyttron Scientific Image Database, using the NCI Thesaurus ontology. Several frequency-based keyword extraction algorithms were implemented and evaluated, aiming to extract important concepts and exclude less relevant ones. Furthermore, topic classification algorithms were applied to identify important concepts which do not occur in the text. The algorithms were evaluated by comparison to annotations provided by experts. Semantic networks were generated from these annotations and an ontology-based similarity metric was applied to perform the comparison. Finally the networks were visualized to provide further insights into the differences of the semantic structure generated by humans, and the algorithms."

More information: http://graus.nu/category/thesis

Published in: Technology, Education
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Semantic Annotation of the Cyttron Database

  1. 1. Automatic Semantic Annotation of the Cyttron Database David Graus @dvdgrs http://graus.nu Media Technology
  2. 2. Part IWhat?
  3. 3. What » How » WhyWhat? Automatic Semantic Annotation of the Cyttron Database
  4. 4. What » How » WhyWhat? Semantic Annotation
  5. 5. What » How » WhyExample“ Company XYZ announced profits in Q3, planning to build a $120M plant in Bulgaria.”
  6. 6. What » How » WhyIt is like tagging“ Company XYZ announced profits in Q3, planning to build a $120M plant in Bulgaria.”
  7. 7. What » How » WhyIt is not tagging“ Company XYZ announced profits in Q3, planning to build a $120M plant in Bulgaria.”Tags: Company XYZ Plant Bulgaria
  8. 8. What » How » WhyIt is not tagging“ Company XYZ announced profits in Q3, planning to build a $120M plant in Bulgaria.”Meaning: What is Company XYZ? What is a Plant? What is Bulgaria?
  9. 9. What » How » WhyIt is not tagging“ Company XYZ announced profits in Q3, planning to build a $120M plant in Bulgaria.”Meaning: What is Company XYZ? What is a Plant? What is Bulgaria? How do they relate?
  10. 10. What » How » WhyIt adds context! source: ontottext.com
  11. 11. What » How » WhyWhat? Automatic Semantic Annotation of the Cyttron Database
  12. 12. What » How » WhyWhat? Cyttron Database
  13. 13. What » How » WhyCyttron Database "The volume of the brain evaluated in this study. The color scale represents the number of 4-mm voxels with data in at least 7 subjects along a 3-cm deep line into the brain. A three-dimensional rendering of a brain is shown in regions where insufficient data were obtained. The most superior regions of the frontal and parietal lobes and the most inferior regions of the temporal lobes were not evaluated. Imaging artifacts may also compromise the significance of results in the most inferior portions of the frontal lobe."
  14. 14. What » How » WhyNCI Thesaurus
  15. 15. What » How » WhyNCI ThesaurusConcept: http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#Brain
  16. 16. What » How » WhyNCI ThesaurusLabel: Brain
  17. 17. What » How » WhyNCI ThesaurusDefinition: An organ composed of grey and white matter containing billions of neurons that is the center for intelligence and reasoning. It is protected by the bony cranium.
  18. 18. What » How » WhyNCI ThesaurusContext: Brain is a Central Nervous System Part Brain is a Organ Brain part of Central Nervous System Basal Ganglia part of Brain Base of the Brain part of Brain Brain Nucleus part of Brain
  19. 19. What » How » WhyNCI Thesaurus
  20. 20. Part IIHow?
  21. 21. What » How » WhyApproach I
  22. 22. What » How » WhyKeyword Extraction
  23. 23. What » How » WhyKeyword Extraction x6
  24. 24. What » How » WhySemantic Annotation
  25. 25. What » How » WhyApproach II
  26. 26. What » How » WhyTopic Classification
  27. 27. What » How » WhyEvaluation I I I I I I I I I I I I I I I I I I I I I I I I II II II II II
  28. 28. What » How » WhyEvaluation 1 I I I I I I 2 I I I I I I 3 I I I I I I I I I I I I II II II II II
  29. 29. What » How » WhyEvaluation ? 1 I I I I I I 2 I I I I I I 3 I I I I I I I I I I I I II II II II II
  30. 30. What » How » WhyEvaluation IConfusion Matrix
  31. 31. What » How » WhyEvaluation IISemantic Similarity
  32. 32. What » How » WhyEvaluation IISemantic Similarity Human Sagittal Plane Brain Magnetic Resonance Imaging Magnetic Resonance Imaging Cingulate Gyrus Cingulate Gyrus Corpus Callosum Lateral Ventricle Thalamus Mamillary Body Cerebral Fornix White Matter
  33. 33. What » How » WhyEvaluation IISemantic Similarity Human Sagittal Plane Brain Magnetic Resonance Imaging Magnetic Resonance Imaging Cingulate Gyrus Cingulate Gyrus Corpus Callosum Lateral Ventricle Thalamus Mamillary Body Cerebral Fornix White Matter
  34. 34. What » How » WhyVisualization DEMO
  35. 35. Part IIIWhy?
  36. 36. What » How » WhyEvaluation
  37. 37. What » How » WhyResults1. No ‘agreement’ between experts2. Annotation method I best approach3. Both Annotation II & Random had no direct matches
  38. 38. What » How » WhyWhat is it good for? / Future Work1. Domain independent method2. Clustering topic identification3. Subgraph similarity
  39. 39. FinQuestions?

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