The document describes a proposed method for sequential query expansion using a concept graph. The method involves two stages: 1) initially sorting concepts in each layer of the concept graph by predicted quality score, and 2) sequentially selecting concepts using a decision criterion that either continues with the current concept layer, moves to the next layer, or stops expansion. The goal is to optimize retrieval performance while minimizing the number of evaluated concepts. The method is intended to address challenges in efficiently exploring a large concept graph during query expansion.

1. 1/20
Introduction Method Experiments Conclusions
Sequential Query Expansion
using 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. 2/20
Introduction Method Experiments Conclusions
Introduction
Method
Experiments
Conclusions
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

3. 3/20
Introduction Method Experiments Conclusions
Introduction
Method
Experiments
Conclusions
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

4. 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. 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

6. 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

7. 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

8. 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

9. 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

10. 5/20
Introduction Method Experiments Conclusions
Challenges
§ The number of candidate concepts to evaluate increases
exponentially with the number of layers to consider.
§ Only a small fraction of hundreds or potentially
thousands of concepts that can improve retrieval results,
while others need to be discarded to avoid noise and
concept drift.
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

11. 6/20
Introduction Method Experiments Conclusions
Optimization Problem
§ Objective Function: total number of evaluated concepts
Constraint: precision of retrieval results
min
˜Cut
k
" kÿ
i=1
Ni
*
s.t. E(˜RΛ; T) ą θQ ,
§ Approximate Solution:
Decision Criterion
Select concept C(i,j) &
If ˜Qr(C(i,j)) ě βUcontinue with the same
concept layer
Discard concept C(i,j) &
If βL ď ˜Qr(C(i,j)) ă βUcontinue with the same
concept layer
Discard concept C(i,j) &
If ˜Qr(C(i,j)) ă βLmove to the next
concept layer
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

12. 7/20
Introduction Method Experiments Conclusions
Introduction
Method
Experiments
Conclusions
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

13. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

14. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Selection
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
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)
Stage II: Sequential Concept Selection
Decision Criterion
Select concept & ˜Qr(C(i,j)) ě βU
continue
Discard concept &
βL ď ˜Qr(C(i,j)) ă βU
continue
Discard concept & ˜Qr(C(i,j)) ă βL
move to next layer
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

15. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Selection
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
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)
Stage II: Sequential Concept Selection
Decision Criterion
Select concept & ˜Qr(C(i,j)) ě βU
continue
Discard concept &
βL ď ˜Qr(C(i,j)) ă βU
continue
Discard concept & ˜Qr(C(i,j)) ă βL
move to next layer
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

16. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Selection
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
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)
Stage II: Sequential Concept Selection
Decision Criterion
Select concept & ˜Qr(C(i,j)) ě βU
continue
Discard concept &
βL ď ˜Qr(C(i,j)) ă βU
continue
Discard concept & ˜Qr(C(i,j)) ă βL
move to next layer
Action Select
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

17. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Selection
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
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)
Stage II: Sequential Concept Selection
Decision Criterion
Select concept & ˜Qr(C(i,j)) ě βU
continue
Discard concept &
βL ď ˜Qr(C(i,j)) ă βU
continue
Discard concept & ˜Qr(C(i,j)) ă βL
move to next layer
Action Select
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

18. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Selection
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
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)
Stage II: Sequential Concept Selection
Decision Criterion
Select concept & ˜Qr(C(i,j)) ě βU
continue
Discard concept &
βL ď ˜Qr(C(i,j)) ă βU
continue
Discard concept & ˜Qr(C(i,j)) ă βL
move to next layer
Action SelectContinue
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

19. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Selection
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
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)
Stage II: Sequential Concept Selection
Decision Criterion
Select concept & ˜Qr(C(i,j)) ě βU
continue
Discard concept &
βL ď ˜Qr(C(i,j)) ă βU
continue
Discard concept & ˜Qr(C(i,j)) ă βL
move to next layer
Action SelectContinue
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

20. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Selection
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
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)
Stage II: Sequential Concept Selection
Decision Criterion
Select concept & ˜Qr(C(i,j)) ě βU
continue
Discard concept &
βL ď ˜Qr(C(i,j)) ă βU
continue
Discard concept & ˜Qr(C(i,j)) ă βL
move to next layer
Action SelectContinueSelect
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

21. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Selection
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
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)
Stage II: Sequential Concept Selection
Decision Criterion
Select concept & ˜Qr(C(i,j)) ě βU
continue
Discard concept &
βL ď ˜Qr(C(i,j)) ă βU
continue
Discard concept & ˜Qr(C(i,j)) ă βL
move to next layer
Action SelectContinueSelect
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

22. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Selection
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
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)
Stage II: Sequential Concept Selection
Decision Criterion
Select concept & ˜Qr(C(i,j)) ě βU
continue
Discard concept &
βL ď ˜Qr(C(i,j)) ă βU
continue
Discard concept & ˜Qr(C(i,j)) ă βL
move to next layer
Action SelectContinueSelectContinue
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

23. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Selection
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
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)
Stage II: Sequential Concept Selection
Decision Criterion
Select concept & ˜Qr(C(i,j)) ě βU
continue
Discard concept &
βL ď ˜Qr(C(i,j)) ă βU
continue
Discard concept & ˜Qr(C(i,j)) ă βL
move to next layer
Action SelectContinueSelectContinueDiscard
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

24. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Selection
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
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)
Stage II: Sequential Concept Selection
Decision Criterion
Select concept & ˜Qr(C(i,j)) ě βU
continue
Discard concept &
βL ď ˜Qr(C(i,j)) ă βU
continue
Discard concept & ˜Qr(C(i,j)) ă βL
move to next layer
Action SelectContinueSelectContinueDiscard
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

25. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Selection
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
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)
Stage II: Sequential Concept Selection
Decision Criterion
Select concept & ˜Qr(C(i,j)) ě βU
continue
Discard concept &
βL ď ˜Qr(C(i,j)) ă βU
continue
Discard concept & ˜Qr(C(i,j)) ă βL
move to next layer
Action SelectContinueSelectContinueDiscardContinue
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

26. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Selection
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
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)
Stage II: Sequential Concept Selection
Decision Criterion
Select concept & ˜Qr(C(i,j)) ě βU
continue
Discard concept &
βL ď ˜Qr(C(i,j)) ă βU
continue
Discard concept & ˜Qr(C(i,j)) ă βL
move to next layer
Action SelectContinueSelectContinueDiscardContinueSelect
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

27. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Selection
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
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)
Stage II: Sequential Concept Selection
Decision Criterion
Select concept & ˜Qr(C(i,j)) ě βU
continue
Discard concept &
βL ď ˜Qr(C(i,j)) ă βU
continue
Discard concept & ˜Qr(C(i,j)) ă βL
move to next layer
Action SelectContinueSelectContinueDiscardContinueSelectDiscard
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

28. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Selection
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
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)
Stage II: Sequential Concept Selection
Decision Criterion
Select concept & ˜Qr(C(i,j)) ě βU
continue
Discard concept &
βL ď ˜Qr(C(i,j)) ă βU
continue
Discard concept & ˜Qr(C(i,j)) ă βL
move to next layer
Action SelectContinueSelectContinueDiscardContinueSelectDiscard
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

29. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Selection
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
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)
Stage II: Sequential Concept Selection
Decision Criterion
Select concept & ˜Qr(C(i,j)) ě βU
continue
Discard concept &
βL ď ˜Qr(C(i,j)) ă βU
continue
Discard concept & ˜Qr(C(i,j)) ă βL
move to next layer
Action SelectContinueSelectContinueDiscardContinueSelectDiscardDiscard
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

30. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Selection
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
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)
Stage II: Sequential Concept Selection
Decision Criterion
Select concept & ˜Qr(C(i,j)) ě βU
continue
Discard concept &
βL ď ˜Qr(C(i,j)) ă βU
continue
Discard concept & ˜Qr(C(i,j)) ă βL
move to next layer
Action SelectContinueSelectContinueDiscardContinueSelectDiscardDiscardSTOP
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

31. 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)
Stage I: Initial Concept Sorting
Sort Concepts at Each Layer
according to ˜Qs(C(i,j))
Selection
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
Selection
Region
Uncertainty
Region
Rejection
Region
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)
Stage II: Sequential Concept Selection
Decision Criterion
Select concept & ˜Qr(C(i,j)) ě βU
continue
Discard concept &
βL ď ˜Qr(C(i,j)) ă βU
continue
Discard concept & ˜Qr(C(i,j)) ă βL
move to next layer
Action SelectContinueSelectContinueDiscardContinueSelectDiscardDiscardSTOP
Number of Evaluated
Concepts: 11 (26% less)
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

32. 9/20
Introduction Method Experiments Conclusions
Summary of the proposed method and the baselines
Method
Optimization Problem Criteria in the Approximate Solution
Objective Constraint Selecting Rejecting Stopping
Method A mint
řk
i=0 Liu E( ˜Rk
Λ; T) ą θ Qb(c) ą βQ Qb(c) ă βQ i ą k
Method B maxtE( ˜Rk
Λ; T)u
řk
i=0 Li ă θ Ii(c) ă βI Ii(c) ą βI i ą k
Method C mint
řk
i=0 Liu E( ˜Rk
Λ; T) ą θ Qb(c) ą βQ Qb(c) ă βQ i ą k
Method D maxtE( ˜Rk
Λ; T)u
řk
i=0 Li ă θ I(c) ă βI I(c) ą βI i ą k
Proposed mint
řk
i=0 Niu E( ˜Rk
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 the
feature 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 proposed
method.
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

33. 10/20
Introduction Method Experiments Conclusions
Baselines (With Fixed Number of Layers)
Selection
Region Selection
Region
Rejection
Region
Selection
Region
Rejection
Region
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
Selection
Region
Rejection
Region
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
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)
SingleThreshold
onEachLayer
SingleThreshold
onAllLayers
A B
C D
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

34. 11/20
Introduction Method Experiments Conclusions
Introduction
Method
Experiments
Conclusions
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

35. 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

36. 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

37. 14/20
Introduction Method Experiments Conclusions
Feature Ablation on ROBUST04 collection
hgstDocScore
maxTCooccur
maxCooccurL*
nodeDegree
maxTCooccurL*
maxNumLinks
maxTCooccur*
maxCooccur*
varDocScore
avgTCooccurL*
avgCooccurL*
maxTMiL*
avgCooccur*
avgTMiL*
avgNumLinks
maxTCooccurP*
avgTCooccur*
maxTMiP*
avgDocScore
avgTDocScore
avgTCooccurP*
avgTCooccur
avgTMiP*
docFreqTpDoc
termFreqTpDoc
none
0.15
0.20
0.25
0.30
MAP
Feature
The feature that results in the highest
reduction of MAP after being removed
from the feature set:
§ The features that are utilized in
both stages of the proposed
method
§ The features that are dependent on
the collection
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

38. 15/20
Introduction Method Experiments Conclusions
The impact of the upper and lower thresholds on
MAP (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 to
the candidate list of expansion concepts.
§ Upper threshold > the optimum value: some useful concepts may not be
selected as expansion concepts.
§ Lower threshold < the optimum value: the selection process may terminate
earlier and a number of useful concepts may not be examined at all.
§ Lower threshold > the optimum value: the proposed method will evaluate
more concepts in total, which is against its main objective.
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

39. 15/20
Introduction Method Experiments Conclusions
The impact of the upper and lower thresholds on
MAP (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 examined
concepts.
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

40. 16/20
Introduction Method Experiments Conclusions
Retrieval Performance
Col.
Method
Concept Layer
1st 2nd 3rd 4th
TREC7-8 Method D-HAL 0.2220 0.2239 0.2155 0.2120
Method D-CNet 0.2205 0.2245 0.2214 0.2183
Method C-HAL 0.2152 0.2227 0.2185 0.2133
Method C-CNet 0.2182 0.2265 0.2225 0.2218
Method B-HAL 0.2207 0.2171 0.2266 0.2236
Method B-CNet 0.2188 0.2294 0.2255 0.2294
Method A-HAL 0.2172 0.2251 0.2290 0.2282
Method A-CNet 0.2183 0.2290 0.2329 0.2335
Proposed-HAL 0.2249 0.2348 0.2418 0.2457
Proposed-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

41. 16/20
Introduction Method Experiments Conclusions
Retrieval Performance
Col.
Method
Concept Layer
1st 2nd 3rd 4th
ROBUST04 Method D-HAL 0.2660 0.2644 0.2569 0.2554
Method D-CNet 0.2640 0.2651 0.2568 0.2555
Method C-HAL 0.2675 0.2655 0.2608 0.2516
Method C-CNet 0.2637 0.2628 0.2683 0.2695
Method B-HAL 0.2684 0.2718 0.2598 0.2535
Method B-CNet 0.2616 0.2710 0.2665 0.2675
Method A-HAL 0.2614 0.2758 0.2757 0.2764
Method A-CNet 0.2689 0.2732 0.2851 0.2793
Proposed-HAL 0.2721 0.2786 0.2865 0.2898
Proposed-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

42. 16/20
Introduction Method Experiments Conclusions
Retrieval Performance
Col.
Method
Concept Layer
1st 2nd 3rd 4th
GOV Method D-HAL 0.2337 0.2428 0.2355 0.2319
Method D-CNet 0.2348 0.2396 0.2355 0.2382
Method C-HAL 0.2404 0.2406 0.2459 0.2322
Method C-CNet 0.2416 0.2451 0.2378 0.2379
Method B-HAL 0.2359 0.2466 0.2418 0.2397
Method B-CNet 0.2420 0.2452 0.2484 0.2421
Method A-HAL 0.2434 0.2442 0.2491 0.2420
Method A-CNet 0.2365 0.2455 0.2524 0.2422
Proposed-HAL 0.2455 0.2429 0.2570 0.2578
Proposed-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

43. 17/20
Introduction Method Experiments Conclusions
Retrieval Performance
Without PRF Concepts
Collection
Evaluation
QL SDM
Method A Method A Proposed Proposed
Metric HAL CNet HAL CNet
TREC7-8
MAP 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 0.3765
0.3762 0.3783 0.3785˚ 0.3796˚
(-0.08%) (0.48%) (0.53%/0.61%) (0.82%/0.34%)
ROBUST04
MAP 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 0.3667
0.3679 0.3773˚: 0.3802˚: 0.3795˚:
(0.33%) (2.89%) (3.68%/3.34%) 3.49%/0.58%
GOV
MAP 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 0.0451
0.0476 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 (QE
and SDM) also in terms of P@20.
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

44. 17/20
Introduction Method Experiments Conclusions
Retrieval Performance
With PRF Concepts
Collection
Evaluation
RM LCE
Method A* Method A* Proposed* Proposed*
Metric HAL CNet HAL CNet
TREC7-8
MAP 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 0.3836
0.3883 0.3927˚ 0.3934˚: 0.4035˚:
(1.23%) (2.37%) (2.55%/1.31%) 5.19%/2.75%
ROBUST04
MAP 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 0.3785
0.3826˚ 0.3834˚ 0.3893˚: 0.3965˚:
(1.08%) (1.29%) (2.85%/1.75%) 4.76%/3.42%
GOV
MAP 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 0.0566
0.0583 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 and
SDM whether the concept graph is generated by HAL or ConceptNet.
§ Method A provides a significant improvement over LCE only in one of the cases
when it uses PRF concepts.
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

45. 18/20
Introduction Method Experiments Conclusions
Introduction
Method
Experiments
Conclusions
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

46. 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

47. 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 minimize
the number of selected concepts or maximize a concept quality measure.
§ The proposed method and the baselines produce more accurate results
using 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 for
extending this work.
Balaneshin, Kotov Wayne State University
Sequential Query Expansion using Concept Graph

48. Many Thanks to the ACM SIGIR Student Travel Grant!
20/20