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

Technology Education

Automatic Semantic Annotation of the
Cyttron Database
David Graus
@dvdgrs
http://graus.nu
Media Technology

What » How » Why

What?

Automatic Semantic Annotation of the Cyttron Database

What » How » Why

What?

Semantic Annotation

What » How » Why

Example

“ Company XYZ announced profits in Q3, planning to
build a $120M plant in Bulgaria.”

What » How » Why

It is like tagging

“ Company XYZ announced profits in Q3, planning to
build a $120M plant in Bulgaria.”

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

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?

What » How » Why

It adds context!

source: ontottext.com

What » How » Why

What?

Cyttron Database

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."

What » How » Why

NCI Thesaurus
Concept: http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#Brain

What » How » Why

NCI Thesaurus

Label: Brain

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.

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

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

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

What » How » Why

Evaluation I

Confusion Matrix

What » How » Why

Evaluation II

Semantic Similarity

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

What » How » Why

Results

1. No ‘agreement’ between experts

2. Annotation method I best approach

3. Both Annotation II & Random had no direct matches

What » How » Why

What is it good for? / Future Work

1. Domain independent method

2. Clustering topic identification

3. Subgraph similarity

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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?

Semantic Annotation of the Cyttron Database

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