sequential query expansion using concept graph
TRANSCRIPT
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