1/20

Introduction Method Experiments Conclusions

Sequential Query Expansionusing Concept Graph

Saeid Balaneshin-kordan Alexander Kotov

Wayne State University

October 25, 2016

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

2/20

Introduction Method Experiments Conclusions

Introduction

Method

Experiments

Conclusions

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

3/20

Introduction Method Experiments Conclusions

Introduction

Method

Experiments

Conclusions

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

4/20

Introduction Method Experiments Conclusions

Concept Graph - Example

§ Query: poach wildlife preserve.§ Concept Graph: ConceptNet 5§ The first number in parenthesis indicates concept layer, the second number is the

index of a concept in the concept layer.

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

4/20

Introduction Method Experiments Conclusions

Concept Graph - Example

§ Query: poach wildlife preserve.§ Concept Graph: ConceptNet 5§ The first number in parenthesis indicates concept layer, the second number is the

index of a concept in the concept layer.

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

4/20

Introduction Method Experiments Conclusions

Concept Graph - Example

§ Query: poach wildlife preserve.§ Concept Graph: ConceptNet 5§ The first number in parenthesis indicates concept layer, the second number is the

index of a concept in the concept layer.

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

4/20

Introduction Method Experiments Conclusions

Concept Graph - Example

§ Query: poach wildlife preserve.§ Concept Graph: ConceptNet 5§ The first number in parenthesis indicates concept layer, the second number is the

index of a concept in the concept layer.

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

4/20

Introduction Method Experiments Conclusions

Concept Graph - Example

§ Query: poach wildlife preserve.§ Concept Graph: ConceptNet 5§ The first number in parenthesis indicates concept layer, the second number is the

index of a concept in the concept layer.

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

4/20

Introduction Method Experiments Conclusions

Concept Graph - Example

§ Query: poach wildlife preserve.§ Concept Graph: ConceptNet 5§ The first number in parenthesis indicates concept layer, the second number is the

index of a concept in the concept layer.

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

5/20

Introduction Method Experiments Conclusions

Challenges

§ The number of candidate concepts to evaluate increasesexponentially with the number of layers to consider.

§ Only a small fraction of hundreds or potentiallythousands of concepts that can improve retrieval results,while others need to be discarded to avoid noise andconcept drift.

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

6/20

Introduction Method Experiments Conclusions

Optimization Problem§ Objective Function: total number of evaluated concepts

Constraint: precision of retrieval results

minC̃ut

k

" kÿ

i=1

Ni

*

s.t. E(R̃Λ;T) ą θQ ,

§ Approximate Solution:

Decision CriterionSelect concept C(i,j) &

If Q̃r(C(i,j)) ě βUcontinue with the sameconcept layerDiscard concept C(i,j) &

If βL ď Q̃r(C(i,j)) ă βUcontinue with the sameconcept layerDiscard concept C(i,j) &

If Q̃r(C(i,j)) ă βLmove to the nextconcept layer

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

7/20

Introduction Method Experiments Conclusions

Introduction

Method

Experiments

Conclusions

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Selection

Region

Reje

ctio

n

Re

gio

n

Selection

Region

Uncert

ain

ty

Re

gio

n

Rejection

Region

Se

lectio

n

Re

gio

n

Uncert

ain

ty

Re

gio

n

Reje

ctio

n

Re

gio

n

Rejection

Region

(1,1)(1,2)

(1,3)

(2,1)(2,2)

(2,3)

(2,4) (2,5)(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.200

0.225

0.250

0.275

0.300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qr(

c)

Thresholds

Upper Thresholds

Lower Thresholds

Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage II: Sequential Concept Selection

Decision CriterionSelect concept & Q̃r(C(i,j)) ě βUcontinueDiscard concept &

βL ď Q̃r(C(i,j)) ă βUcontinueDiscard concept & Q̃r(C(i,j)) ă βLmove to next layer

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Selection

Region

Reje

ctio

n

Re

gio

n

Selection

Region

Uncert

ain

ty

Re

gio

n

Rejection

Region

Se

lectio

n

Re

gio

n

Uncert

ain

ty

Re

gio

n

Reje

ctio

n

Re

gio

n

Rejection

Region

(1,1)(1,2)

(1,3)

(2,1)(2,2)

(2,3)

(2,4) (2,5)(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.200

0.225

0.250

0.275

0.300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qr(

c)

Thresholds

Upper Thresholds

Lower Thresholds

Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage II: Sequential Concept Selection

Decision CriterionSelect concept & Q̃r(C(i,j)) ě βUcontinueDiscard concept &

βL ď Q̃r(C(i,j)) ă βUcontinueDiscard concept & Q̃r(C(i,j)) ă βLmove to next layer

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Selection

Region

Reje

ctio

n

Re

gio

n

Selection

Region

Uncert

ain

ty

Re

gio

n

Rejection

Region

Se

lectio

n

Re

gio

n

Uncert

ain

ty

Re

gio

n

Reje

ctio

n

Re

gio

n

Rejection

Region

(1,1)(1,2)

(1,3)

(2,1)(2,2)

(2,3)

(2,4) (2,5)(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.200

0.225

0.250

0.275

0.300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qr(

c)

Thresholds

Upper Thresholds

Lower Thresholds

Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage II: Sequential Concept Selection

Decision CriterionSelect concept & Q̃r(C(i,j)) ě βUcontinueDiscard concept &

βL ď Q̃r(C(i,j)) ă βUcontinueDiscard concept & Q̃r(C(i,j)) ă βLmove to next layer

Action Select

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Selection

Region

Reje

ctio

n

Re

gio

n

Selection

Region

Uncert

ain

ty

Re

gio

n

Rejection

Region

Se

lectio

n

Re

gio

n

Uncert

ain

ty

Re

gio

n

Reje

ctio

n

Re

gio

n

Rejection

Region

(1,1)(1,2)

(1,3)

(2,1)(2,2)

(2,3)

(2,4) (2,5)(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.200

0.225

0.250

0.275

0.300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qr(

c)

Thresholds

Upper Thresholds

Lower Thresholds

Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage II: Sequential Concept Selection

Decision CriterionSelect concept & Q̃r(C(i,j)) ě βUcontinueDiscard concept &

βL ď Q̃r(C(i,j)) ă βUcontinueDiscard concept & Q̃r(C(i,j)) ă βLmove to next layer

Action Select

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Selection

Region

Reje

ctio

n

Re

gio

n

Selection

Region

Uncert

ain

ty

Re

gio

n

Rejection

Region

Se

lectio

n

Re

gio

n

Uncert

ain

ty

Re

gio

n

Reje

ctio

n

Re

gio

n

Rejection

Region

(1,1)(1,2)

(1,3)

(2,1)(2,2)

(2,3)

(2,4) (2,5)(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.200

0.225

0.250

0.275

0.300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qr(

c)

Thresholds

Upper Thresholds

Lower Thresholds

Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage II: Sequential Concept Selection

Decision CriterionSelect concept & Q̃r(C(i,j)) ě βUcontinueDiscard concept &

βL ď Q̃r(C(i,j)) ă βUcontinueDiscard concept & Q̃r(C(i,j)) ă βLmove to next layer

Action SelectContinue

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Selection

Region

Reje

ctio

n

Re

gio

n

Selection

Region

Uncert

ain

ty

Re

gio

n

Rejection

Region

Se

lectio

n

Re

gio

n

Uncert

ain

ty

Re

gio

n

Reje

ctio

n

Re

gio

n

Rejection

Region

(1,1)(1,2)

(1,3)

(2,1)(2,2)

(2,3)

(2,4) (2,5)(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.200

0.225

0.250

0.275

0.300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qr(

c)

Thresholds

Upper Thresholds

Lower Thresholds

Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage II: Sequential Concept Selection

Decision CriterionSelect concept & Q̃r(C(i,j)) ě βUcontinueDiscard concept &

βL ď Q̃r(C(i,j)) ă βUcontinueDiscard concept & Q̃r(C(i,j)) ă βLmove to next layer

Action SelectContinue

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Selection

Region

Reje

ctio

n

Re

gio

n

Selection

Region

Uncert

ain

ty

Re

gio

n

Rejection

Region

Se

lectio

n

Re

gio

n

Uncert

ain

ty

Re

gio

n

Reje

ctio

n

Re

gio

n

Rejection

Region

(1,1)(1,2)

(1,3)

(2,1)(2,2)

(2,3)

(2,4) (2,5)(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.200

0.225

0.250

0.275

0.300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qr(

c)

Thresholds

Upper Thresholds

Lower Thresholds

Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage II: Sequential Concept Selection

Decision CriterionSelect concept & Q̃r(C(i,j)) ě βUcontinueDiscard concept &

βL ď Q̃r(C(i,j)) ă βUcontinueDiscard concept & Q̃r(C(i,j)) ă βLmove to next layer

Action SelectContinueSelect

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Selection

Region

Reje

ctio

n

Re

gio

n

Selection

Region

Uncert

ain

ty

Re

gio

n

Rejection

Region

Se

lectio

n

Re

gio

n

Uncert

ain

ty

Re

gio

n

Reje

ctio

n

Re

gio

n

Rejection

Region

(1,1)(1,2)

(1,3)

(2,1)(2,2)

(2,3)

(2,4) (2,5)(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.200

0.225

0.250

0.275

0.300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qr(

c)

Thresholds

Upper Thresholds

Lower Thresholds

Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage II: Sequential Concept Selection

Decision CriterionSelect concept & Q̃r(C(i,j)) ě βUcontinueDiscard concept &

βL ď Q̃r(C(i,j)) ă βUcontinueDiscard concept & Q̃r(C(i,j)) ă βLmove to next layer

Action SelectContinueSelect

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Selection

Region

Reje

ctio

n

Re

gio

n

Selection

Region

Uncert

ain

ty

Re

gio

n

Rejection

Region

Se

lectio

n

Re

gio

n

Uncert

ain

ty

Re

gio

n

Reje

ctio

n

Re

gio

n

Rejection

Region

(1,1)(1,2)

(1,3)

(2,1)(2,2)

(2,3)

(2,4) (2,5)(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.200

0.225

0.250

0.275

0.300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qr(

c)

Thresholds

Upper Thresholds

Lower Thresholds

Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage II: Sequential Concept Selection

Decision CriterionSelect concept & Q̃r(C(i,j)) ě βUcontinueDiscard concept &

βL ď Q̃r(C(i,j)) ă βUcontinueDiscard concept & Q̃r(C(i,j)) ă βLmove to next layer

Action SelectContinueSelectContinue

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Selection

Region

Reje

ctio

n

Re

gio

n

Selection

Region

Uncert

ain

ty

Re

gio

n

Rejection

Region

Se

lectio

n

Re

gio

n

Uncert

ain

ty

Re

gio

n

Reje

ctio

n

Re

gio

n

Rejection

Region

(1,1)(1,2)

(1,3)

(2,1)(2,2)

(2,3)

(2,4) (2,5)(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.200

0.225

0.250

0.275

0.300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qr(

c)

Thresholds

Upper Thresholds

Lower Thresholds

Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage II: Sequential Concept Selection

Decision CriterionSelect concept & Q̃r(C(i,j)) ě βUcontinueDiscard concept &

βL ď Q̃r(C(i,j)) ă βUcontinueDiscard concept & Q̃r(C(i,j)) ă βLmove to next layer

Action SelectContinueSelectContinueDiscard

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Selection

Region

Reje

ctio

n

Re

gio

n

Selection

Region

Uncert

ain

ty

Re

gio

n

Rejection

Region

Se

lectio

n

Re

gio

n

Uncert

ain

ty

Re

gio

n

Reje

ctio

n

Re

gio

n

Rejection

Region

(1,1)(1,2)

(1,3)

(2,1)(2,2)

(2,3)

(2,4) (2,5)(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.200

0.225

0.250

0.275

0.300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qr(

c)

Thresholds

Upper Thresholds

Lower Thresholds

Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage II: Sequential Concept Selection

Decision CriterionSelect concept & Q̃r(C(i,j)) ě βUcontinueDiscard concept &

βL ď Q̃r(C(i,j)) ă βUcontinueDiscard concept & Q̃r(C(i,j)) ă βLmove to next layer

Action SelectContinueSelectContinueDiscard

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Selection

Region

Reje

ctio

n

Re

gio

n

Selection

Region

Uncert

ain

ty

Re

gio

n

Rejection

Region

Se

lectio

n

Re

gio

n

Uncert

ain

ty

Re

gio

n

Reje

ctio

n

Re

gio

n

Rejection

Region

(1,1)(1,2)

(1,3)

(2,1)(2,2)

(2,3)

(2,4) (2,5)(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.200

0.225

0.250

0.275

0.300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qr(

c)

Thresholds

Upper Thresholds

Lower Thresholds

Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage II: Sequential Concept Selection

Decision CriterionSelect concept & Q̃r(C(i,j)) ě βUcontinueDiscard concept &

βL ď Q̃r(C(i,j)) ă βUcontinueDiscard concept & Q̃r(C(i,j)) ă βLmove to next layer

Action SelectContinueSelectContinueDiscardContinue

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Selection

Region

Reje

ctio

n

Re

gio

n

Selection

Region

Uncert

ain

ty

Re

gio

n

Rejection

Region

Se

lectio

n

Re

gio

n

Uncert

ain

ty

Re

gio

n

Reje

ctio

n

Re

gio

n

Rejection

Region

(1,1)(1,2)

(1,3)

(2,1)(2,2)

(2,3)

(2,4) (2,5)(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.200

0.225

0.250

0.275

0.300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qr(

c)

Thresholds

Upper Thresholds

Lower Thresholds

Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage II: Sequential Concept Selection

Decision CriterionSelect concept & Q̃r(C(i,j)) ě βUcontinueDiscard concept &

βL ď Q̃r(C(i,j)) ă βUcontinueDiscard concept & Q̃r(C(i,j)) ă βLmove to next layer

Action SelectContinueSelectContinueDiscardContinueSelect

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Selection

Region

Reje

ctio

n

Re

gio

n

Selection

Region

Uncert

ain

ty

Re

gio

n

Rejection

Region

Se

lectio

n

Re

gio

n

Uncert

ain

ty

Re

gio

n

Reje

ctio

n

Re

gio

n

Rejection

Region

(1,1)(1,2)

(1,3)

(2,1)(2,2)

(2,3)

(2,4) (2,5)(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.200

0.225

0.250

0.275

0.300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qr(

c)

Thresholds

Upper Thresholds

Lower Thresholds

Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage II: Sequential Concept Selection

Decision CriterionSelect concept & Q̃r(C(i,j)) ě βUcontinueDiscard concept &

βL ď Q̃r(C(i,j)) ă βUcontinueDiscard concept & Q̃r(C(i,j)) ă βLmove to next layer

Action SelectContinueSelectContinueDiscardContinueSelectDiscard

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Selection

Region

Reje

ctio

n

Re

gio

n

Selection

Region

Uncert

ain

ty

Re

gio

n

Rejection

Region

Se

lectio

n

Re

gio

n

Uncert

ain

ty

Re

gio

n

Reje

ctio

n

Re

gio

n

Rejection

Region

(1,1)(1,2)

(1,3)

(2,1)(2,2)

(2,3)

(2,4) (2,5)(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.200

0.225

0.250

0.275

0.300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qr(

c)

Thresholds

Upper Thresholds

Lower Thresholds

Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage II: Sequential Concept Selection

Decision CriterionSelect concept & Q̃r(C(i,j)) ě βUcontinueDiscard concept &

βL ď Q̃r(C(i,j)) ă βUcontinueDiscard concept & Q̃r(C(i,j)) ă βLmove to next layer

Action SelectContinueSelectContinueDiscardContinueSelectDiscard

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Selection

Region

Reje

ctio

n

Re

gio

n

Selection

Region

Uncert

ain

ty

Re

gio

n

Rejection

Region

Se

lectio

n

Re

gio

n

Uncert

ain

ty

Re

gio

n

Reje

ctio

n

Re

gio

n

Rejection

Region

(1,1)(1,2)

(1,3)

(2,1)(2,2)

(2,3)

(2,4) (2,5)(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.200

0.225

0.250

0.275

0.300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qr(

c)

Thresholds

Upper Thresholds

Lower Thresholds

Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage II: Sequential Concept Selection

Decision CriterionSelect concept & Q̃r(C(i,j)) ě βUcontinueDiscard concept &

βL ď Q̃r(C(i,j)) ă βUcontinueDiscard concept & Q̃r(C(i,j)) ă βLmove to next layer

Action SelectContinueSelectContinueDiscardContinueSelectDiscardDiscard

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Selection

Region

Reje

ctio

n

Re

gio

n

Selection

Region

Uncert

ain

ty

Re

gio

n

Rejection

Region

Se

lectio

n

Re

gio

n

Uncert

ain

ty

Re

gio

n

Reje

ctio

n

Re

gio

n

Rejection

Region

(1,1)(1,2)

(1,3)

(2,1)(2,2)

(2,3)

(2,4) (2,5)(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.200

0.225

0.250

0.275

0.300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qr(

c)

Thresholds

Upper Thresholds

Lower Thresholds

Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage II: Sequential Concept Selection

Decision CriterionSelect concept & Q̃r(C(i,j)) ě βUcontinueDiscard concept &

βL ď Q̃r(C(i,j)) ă βUcontinueDiscard concept & Q̃r(C(i,j)) ă βLmove to next layer

Action SelectContinueSelectContinueDiscardContinueSelectDiscardDiscardSTOP

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

8/20

Introduction Method Experiments Conclusions

Proposed Method

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qs(c

) Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage I: Initial Concept Sorting

Sort Concepts at Each Layeraccording to Q̃s(C(i,j))

Selection

Region

Reje

ctio

n

Re

gio

n

Selection

Region

Uncert

ain

ty

Re

gio

n

Rejection

Region

Se

lectio

n

Re

gio

n

Uncert

ain

ty

Re

gio

n

Reje

ctio

n

Re

gio

n

Rejection

Region

(1,1)(1,2)

(1,3)

(2,1)(2,2)

(2,3)

(2,4) (2,5)(2,6)

(3,1)

(3,2)

(3,3) (4,1) (4,2)(4,3)

0.200

0.225

0.250

0.275

0.300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qr(

c)

Thresholds

Upper Thresholds

Lower Thresholds

Relationship Layers

Layer 1

Layer 2

Layer 3

Layer 4

Stage II: Sequential Concept Selection

Decision CriterionSelect concept & Q̃r(C(i,j)) ě βUcontinueDiscard concept &

βL ď Q̃r(C(i,j)) ă βUcontinueDiscard concept & Q̃r(C(i,j)) ă βLmove to next layer

Action SelectContinueSelectContinueDiscardContinueSelectDiscardDiscardSTOP

Number of Evaluated

Concepts: 11 (26% less)

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

9/20

Introduction Method Experiments Conclusions

Summary of the proposed method and the baselines

MethodOptimization Problem Criteria in the Approximate Solution

Objective Constraint Selecting Rejecting StoppingMethod A mint

řki=0 Liu E(R̃k

Λ;T) ą θ Qb(c) ą βQ Qb(c) ă βQ i ą kMethod B maxtE(R̃k

Λ;T)uřk

i=0 Li ă θ Ii(c) ă βI Ii(c) ą βI i ą kMethod C mint

řki=0 Liu E(R̃k

Λ;T) ą θ Qb(c) ą βQ Qb(c) ă βQ i ą kMethod D maxtE(R̃k

Λ;T)uřk

i=0 Li ă θ I(c) ă βI I(c) ą βI i ą kProposed mint

řki=0 Niu E(R̃k

Λ;T) ą θ Qr(c) ą βU Qr(c) ă βL Li = 0

§ Qb(C(i,j)): Quality measure computed as a linear weighted combination of thefeature functions.

§ I(c): Index of a concept in the sorted set of concepts§ Li: Number of selected concepts from the i-th concept layer.§ the set of features used to calculate the quality measure Qb(c) for the baselines

is the same as the set of features used to calculate Qs(c) in for our proposedmethod.

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

10/20

Introduction Method Experiments Conclusions

Baselines (With Fixed Number of Layers)

Selection

Region Selection

Region

Re

jectio

n

Re

gio

n

Selection

Region

Re

jectio

n

Re

gio

n

Rejection

Region

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)

(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3)(4,1)

(4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qb(c

)

Selection

Region Selection

Region

Rejection

Region

Se

lectio

n

Re

gio

n

Rejection

RegionRejection

Region

(1,1)

(1,2)

(1,3)

(2,1)

(2,2)

(2,3)

(2,4) (2,5)

(2,6)

(3,1)

(3,2)

(3,3)(4,1)

(4,2)(4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qb(c

)

Selection

Region

Rejection

Region

(1,1)

(3,1)(2,1)

(1,2)

(2,2)

(3,2) (2,3)

(2,4) (2,5)(1,3)

(3,3)(4,1)

(4,2)(2,6) (4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qb(c

)

Selection

Region

Rejection

Region

(1,1)

(3,1)(2,1)

(1,2)

(2,2)

(3,2) (2,3)

(2,4) (2,5)(1,3)

(3,3)(4,1)

(4,2)(2,6) (4,3)

0.24

0.28

0.32

0.36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

concepts

Qb(c

)

Sing

leTh

resh

old

onEa

chLa

yer

Sing

leTh

resh

old

onA

llLa

yers

A B

C D

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

11/20

Introduction Method Experiments Conclusions

Introduction

Method

Experiments

Conclusions

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

12/20

Introduction Method Experiments Conclusions

Inexpensive Features

§ Retrieval score of the highest ranked document containing C(i,j)§ Avg retrieval score of all documents containing C(i,j)§ Variance of retrieval score of all documents containing C(i,j)§ Avg retrieval scores of the top documents containing C(i,j)§ Number of occurrences of C(i,j) in the top documents§ Number of top documents containing C(i,j)§ Node degree of C(i,j) in the concept graph§ Avg number of paths between C(i,j) and query concepts§ Max number of paths between C(i,j) and query concepts

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

13/20

Introduction Method Experiments Conclusions

Expensive Features§ Avg co-occurrence of C(i,j) with query concepts§ Max co-occurrence of C(i,j) with query concepts§ Avg co-occurrence of C(i,j) with query concepts in top-docs§ Max co-occurrence of C(i,j) with query concepts in top-docs§ Avg co-occurrence of C(i,j) with at least a pair of query concepts in top-docs§ Max co-occurrence of C(i,j) with at least a pair of query concepts in top-docs§ Avg co-occurrence of C(i,j) with all previously selected concepts in top-docs§ Max co-occurrence of C(i,j) with all previously selected concepts in top-docs§ Avg co-occurrence of C(i,j) with selected concepts in concept layer i´ 1§ Max co-occurrence of C(i,j) with selected concepts in concept layer i´ 1§ Avg co-occurrence of C(i,j) with selected concepts in concept layer i´ 1 in

top-docs§ Max co-occurrence of C(i,j) with selected concepts in concept layer i´ 1 in

top-docs§ Avg mutual information of C(i,j) with at least a pair of query concepts in top-docs§ Max mutual information of C(i,j) with at least a pair of query concepts in

top-docs§ Avg mutual information of C(i,j) with selected concepts in concept layer i´ 1 in

top-docs§ Max mutual information of C(i,j) with selected concepts in concept layer i´ 1 in

top-docs

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

14/20

Introduction Method Experiments Conclusions

Feature Ablation on ROBUST04 collection

hgstDocScoremaxTCooccur

maxCooccurL*nodeDegree

maxTCooccurL*maxNumLinksmaxTCooccur*

maxCooccur*varDocScore

avgTCooccurL*avgCooccurL*

maxTMiL*avgCooccur*

avgTMiL*avgNumLinks

maxTCooccurP*avgTCooccur*

maxTMiP*avgDocScore

avgTDocScoreavgTCooccurP*

avgTCooccuravgTMiP*

docFreqTpDoctermFreqTpDoc

none

0.1

5

0.2

0

0.2

5

0.3

0

MAP

Fe

atu

re

The feature that results in the highestreduction of MAP after being removedfrom the feature set:

§ The features that are utilized inboth stages of the proposedmethod

§ The features that are dependent onthe collection

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

15/20

Introduction Method Experiments Conclusions

The impact of the upper and lower thresholds onMAP (i.e., βU and βL) at the concept layer i = 2

(e) TREC7-8 (f) ROBUST04 (g) GOV

§ Upper threshold < the optimum value: more non-useful concepts are added tothe candidate list of expansion concepts.

§ Upper threshold > the optimum value: some useful concepts may not beselected as expansion concepts.

§ Lower threshold < the optimum value: the selection process may terminateearlier and a number of useful concepts may not be examined at all.

§ Lower threshold > the optimum value: the proposed method will evaluatemore concepts in total, which is against its main objective.

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

15/20

Introduction Method Experiments Conclusions

The impact of the upper and lower thresholds onMAP (i.e., βU and βL) at the concept layer i = 2

(h) TREC7-8 (i) ROBUST04 (j) GOV

§ Overall, although the upper and lower thresholds are dependent on each other,the upper threshold has the main effect on the accuracy of selected concepts,while the lower threshold has the main effect on the number of examinedconcepts.

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

16/20

Introduction Method Experiments Conclusions

Retrieval Performance

Col

.Method Concept Layer

1st 2nd 3rd 4th

TREC

7-8

Method D-HAL 0.2220 0.2239 0.2155 0.2120Method D-CNet 0.2205 0.2245 0.2214 0.2183Method C-HAL 0.2152 0.2227 0.2185 0.2133Method C-CNet 0.2182 0.2265 0.2225 0.2218Method B-HAL 0.2207 0.2171 0.2266 0.2236Method B-CNet 0.2188 0.2294 0.2255 0.2294Method A-HAL 0.2172 0.2251 0.2290 0.2282Method A-CNet 0.2183 0.2290 0.2329 0.2335Proposed-HAL 0.2249 0.2348 0.2418 0.2457Proposed-CNet 0.2222 0.2377 0.2449 0.2484

SDM 0.2124 —— —— ——

§ Best performing baseline: Method A§ Methods with multiple thresholds tend to perform better.§ The methods using ConceptNet-based concept graph (CNet) obtain higher

MAP than the ones using HAL.

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

16/20

Introduction Method Experiments Conclusions

Retrieval Performance

Col

.Method Concept Layer

1st 2nd 3rd 4thR

OBU

ST04

Method D-HAL 0.2660 0.2644 0.2569 0.2554Method D-CNet 0.2640 0.2651 0.2568 0.2555Method C-HAL 0.2675 0.2655 0.2608 0.2516Method C-CNet 0.2637 0.2628 0.2683 0.2695Method B-HAL 0.2684 0.2718 0.2598 0.2535Method B-CNet 0.2616 0.2710 0.2665 0.2675Method A-HAL 0.2614 0.2758 0.2757 0.2764Method A-CNet 0.2689 0.2732 0.2851 0.2793Proposed-HAL 0.2721 0.2786 0.2865 0.2898Proposed-CNet 0.2748 0.2814 0.2889 0.2963

SDM 0.2359 —— —— ——

§ Best performing baseline: Method A§ Methods with multiple thresholds tend to perform better.§ The methods using ConceptNet-based concept graph (CNet) obtain higher

MAP than the ones using HAL.

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

16/20

Introduction Method Experiments Conclusions

Retrieval Performance

Col

.Method Concept Layer

1st 2nd 3rd 4thG

OV

Method D-HAL 0.2337 0.2428 0.2355 0.2319Method D-CNet 0.2348 0.2396 0.2355 0.2382Method C-HAL 0.2404 0.2406 0.2459 0.2322Method C-CNet 0.2416 0.2451 0.2378 0.2379Method B-HAL 0.2359 0.2466 0.2418 0.2397Method B-CNet 0.2420 0.2452 0.2484 0.2421Method A-HAL 0.2434 0.2442 0.2491 0.2420Method A-CNet 0.2365 0.2455 0.2524 0.2422Proposed-HAL 0.2455 0.2429 0.2570 0.2578Proposed-CNet 0.2449 0.2514 0.2575 0.2591

SDM 0.2184 —— —— ——

§ Best performing baseline: Method A§ Methods with multiple thresholds tend to perform better.§ The methods using ConceptNet-based concept graph (CNet) obtain higher

MAP than the ones using HAL.

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

17/20

Introduction Method Experiments Conclusions

Retrieval Performance

Without PRF Concepts

CollectionEvaluation

QL SDMMethod A Method A Proposed Proposed

Metric HAL CNet HAL CNet

TREC7-8MAP 0.1982 0.2124

0.2282˚: 0.2335˚: 0.2457˚: 0.2484˚:(7.44%) (9.93%) (15.68%/7.67%) (16.95%/6.38%)

P@20 0.3540 0.37650.3762 0.3783 0.3785˚ 0.3796˚

(-0.08%) (0.48%) (0.53%/0.61%) (0.82%/0.34%)

ROBUST04MAP 0.2359 0.2510

0.2764˚: 0.2851˚: 0.2898˚: 0.2963˚:(10.12%) (13.59%) (15.46%/4.85%) (18.05%/3.93%)

P@20 0.3339 0.36670.3679 0.3773˚: 0.3802˚: 0.3795˚:

(0.33%) (2.89%) (3.68%/3.34%) 3.49%/0.58%

GOVMAP 0.2184 0.2333

0.2491˚ 0.2524˚: 0.2578˚: 0.2591˚:(6.77%) (8.19%) (10.5%/3.49%) 11.06%/2.65%

P@20 0.0389 0.04510.0476 0.0493˚ 0.0558˚: 0.0552˚:

(5.54%) (9.31%) (23.73%/17.23%) 22.39%/11.97%

§ Method A provides a significant improvement over SDM in the 5 cases.§ Although the parameters are estimated with the goal of maximizing MAP, the

proposed method demonstrates significant improvement over the baselines (QEand SDM) also in terms of P@20.

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

17/20

Introduction Method Experiments Conclusions

Retrieval Performance

With PRF Concepts

CollectionEvaluation

RM LCEMethod A* Method A* Proposed* Proposed*

Metric HAL CNet HAL CNet

TREC7-8MAP 0.2151 0.2423

0.2503˚ 0.2558˚: 0.2642˚: 0.2672˚:(3.3%) (5.57%) (9.04%/5.55%) 10.28%/4.46%

P@20 0.3641 0.38360.3883 0.3927˚ 0.3934˚: 0.4035˚:

(1.23%) (2.37%) (2.55%/1.31%) 5.19%/2.75%

ROBUST04MAP 0.2683 0.2826

0.2935˚ 0.2979˚ 0.3034˚: 0.3053˚:(3.86%) (5.41%) (7.36%/3.37%) 8.03%/2.48%

P@20 0.3561 0.37850.3826˚ 0.3834˚ 0.3893˚: 0.3965˚:(1.08%) (1.29%) (2.85%/1.75%) 4.76%/3.42%

GOVMAP 0.2403 0.2678

0.2693 0.2730˚ 0.2793˚: 0.2811˚:(0.56%) (1.94%) (4.29%/3.71%) 4.97%/2.97%

P@20 0.0483 0.05660.0583 0.0617˚ 0.0706˚ 0.0720˚:

(3.00%) (9.01%) (24.73%/21.1%) 27.21%/16.69%

§ The proposed method has significant improvements over the baselines QL andSDM whether the concept graph is generated by HAL or ConceptNet.

§ Method A provides a significant improvement over LCE only in one of the caseswhen it uses PRF concepts.

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

18/20

Introduction Method Experiments Conclusions

Introduction

Method

Experiments

Conclusions

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

19/20

Introduction Method Experiments Conclusions

Conclusions

§ The main contribution of this work:§ A two-stage method for sequential selection of effective concepts for

query expansion from the concept graph.§ The optimization problem of the proposed method:

§ Objective: having least possible number of candidate concepts§ Constraint: achieve a given precision of retrieval results.

§ Stages of the proposed method:§ First Stage: the candidate concepts are sorted using a number of

computationally inexpensive features.§ Second Stage: This sorting is utilized in the second stage to sequentially

select expansion concepts by using computationally expensive features.

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

19/20

Introduction Method Experiments Conclusions

Conclusions

§ Experimental evaluation indicates that:§ The proposed method outperforms state-of-the-art baselines, which

instead of minimizing the number of evaluated concepts, aim to minimizethe number of selected concepts or maximize a concept quality measure.

§ The proposed method and the baselines produce more accurate resultsusing ConceptNet-based than collection-based concept graph.

§ Future Work:§ We believe that applying the proposed method to the case of entity-based

queries and knowledge graphs is an interesting future direction forextending this work.

Balaneshin, Kotov Wayne State University

Sequential Query Expansion using Concept Graph

Many Thanks to the ACM SIGIR Student Travel Grant!

20/20