Automated Conceptual Abstraction of Large Diagrams

plantationscarfAI and Robotics

Nov 25, 2013 (3 years and 7 months ago)

62 views

Automated Conceptual
Abstraction of Large Diagrams

By Daniel Levy and Christina Christodoulakis

December 2012

(2 days before the end of the world)


Introduction


Big picture


Clustering Algorithm


Experiment & Results


Conclusion

Outline


Introduction


Big picture


Clustering Algorithm


Experiments & Results


Conclusion

Outline


So what is this “clustering” you speak of?


Why do we need to cluster?


Reduce cognitive load

Introduction


Introduction


Big picture


Clustering Algorithm


Experiment + Results


Conclusion

Outline

Big Picture

Vision

Diagram Abstraction


Its been done before..

Related Works


Consider a diagram stripped of semantics, or pre
processed using methodologies in previous work



Cluster graph



Evaluate clusters proposed based on closeness of
meaning in the node names

Our Approach

Our Approach


Introduction


Big picture


Clustering Algorithm


Experiment + Results


Conclusion

Outline

Min
-
Cut

Naïve Min
-
Cut Algorithm

C

A

N

B

1

2

3

C

A

N

B

2

3

E

4

E

4

*Must result in exactly 2 partitions

Combinations / Creating partitions

*Assume there exist additional nodes

C

A

N

B

1

2

3

C

A

N

B

1

E

E

4

4

C

D

A

B

2

1

3

C

D

A

B

2

Minimum sets

C

D

A

B

2

1

3

C

D

A

B

2

3

D

A

B

1

3

2

D

A

B

3

2

D

A

B

1

3

2

D

A

B

2

Cycles

E

D

C

A

B

1

2

3

4

5

Listing the min
-
cuts

E

D

C

A

B

1

2

3

4

5

Listing the min
-
cuts

E

D

C

A

B

1

2

3

4

Listing the min
-
cuts

5

E

D

C

A

B

1

2

3

4

5

Listing the min
-
cuts

E

D

C

A

B

1

2

3

4

5

Listing the min
-
cuts

E

D

C

A

B

1

2

3

4

5

E

D

C

A

B

1

2

3

Outside
-
in approach

E

D

C

A

B

1

2

3

4

5

E

D

C

A

B

1

2

3

5

Outside
-
in approach

E

D

C

A

B

1

2

3

4

5

E

D

C

A

B

1

2

3

4

E

D

C

A

B

1

2

3

4

5


We use
RiTa

WordNet

getDistance
() function


We calculate pairwise distances between nodes.


Select for each node the smallest distance between it
and another node


Sum all minimum distances


Average over all nodes in candidate cluster

Cluster Distance Measure


Introduction


Big picture


Clustering Algorithm


Experiments + Results


Conclusion

Outline

Experiment 1

Experiment #1

Experiment # 1

User 1
abstraction

Experimentation

User 2
abstraction

Experiment # 1

automated
abstraction

Experiment 2

Experiment #2

Simplified version


Introduction


Big picture


Clustering Algorithm


Experiments + Results


Conclusion

Outline


Surprised at how similar manual clustering and
automated clustering were.


Suggested improvements:


Automatic distance threshold


Creating
subgraphs


Strictness of clustering (min # of clusters


Advanced min
-
cut discovery



Conclusions

Questions?

Merry Christmas!