Automated Conceptual Abstraction of Large Diagrams

AI and Robotics

Nov 25, 2013 (4 years and 7 months ago)

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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?

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

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