Semantic Annotation of the Cyttron Database
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
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Semantic Annotation of the Cyttron Database
- 1. Automatic Semantic Annotation of the Cyttron Database David Graus @dvdgrs http://graus.nu Media Technology
- 2. Part I What?
- 3. What » How » Why What? Automatic Semantic Annotation of the Cyttron Database
- 4. What » How » Why What? Semantic Annotation
- 5. What » How » Why Example “ Company XYZ announced profits in Q3, planning to build a $120M plant in Bulgaria.”
- 6. What » How » Why It is like tagging “ Company XYZ announced profits in Q3, planning to build a $120M plant in Bulgaria.”
- 7. What » How » Why It is not tagging “ Company XYZ announced profits in Q3, planning to build a $120M plant in Bulgaria.” Tags: Company XYZ Plant Bulgaria
- 8. What » How » Why It 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. What » How » Why It 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. What » How » Why It adds context! source: ontottext.com
- 11. What » How » Why What? Automatic Semantic Annotation of the Cyttron Database
- 12. What » How » Why What? Cyttron Database
- 13. What » How » Why Cyttron 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. What » How » Why NCI Thesaurus
- 15. What » How » Why NCI Thesaurus Concept: http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#Brain
- 16. What » How » Why NCI Thesaurus Label: Brain
- 17. What » How » Why NCI Thesaurus Definition: 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. What » How » Why NCI Thesaurus Context: 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. What » How » Why NCI Thesaurus
- 20. Part II How?
- 21. What » How » Why Approach I
- 22. What » How » Why Keyword Extraction
- 23. What » How » Why Keyword Extraction x6
- 24. What » How » Why Semantic Annotation
- 25. What » How » Why Approach II
- 26. What » How » Why Topic Classification
- 27. What » How » Why Evaluation 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. What » How » Why Evaluation 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. What » How » Why Evaluation ? 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. What » How » Why Evaluation I Confusion Matrix
- 31. What » How » Why Evaluation II Semantic Similarity
- 32. What » How » Why Evaluation II Semantic 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. What » How » Why Evaluation II Semantic 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. What » How » Why Visualization DEMO
- 35. Part III Why?
- 36. What » How » Why Evaluation
- 37. What » How » Why Results 1. No ‘agreement’ between experts 2. Annotation method I best approach 3. Both Annotation II & Random had no direct matches
- 38. What » How » Why What is it good for? / Future Work 1. Domain independent method 2. Clustering topic identification 3. Subgraph similarity
- 39. Fin Questions?