Progressive Approach to Relational Entity Resolution - PowerPoint Presentation

Slide1

Progressive Approach to Relational Entity Resolution

Yasser Altowim

, Dmitri Kalashnikov,

Sharad

Mehrotra

Progressive

ER

Progressive ER

Id

NamePapers

u

1

Very Large Data Bases

{p

1}

u

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ICDE Conference

{p2}u

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VLDB

{p3}u4IEEE Data Eng. Bull{p4}

Id

Title

Authors

Venue

p

1

Transaction Support in Read Optimized …

{a1, a2}u1p2Read Optimized File System Designs: …{a1}u2p3Transaction Support in Read Optimized …{a3, a4}u3p4Berkeley DB: A Retrospective ..{a3}u4

Author Venue

IdNamePapersa1Marge Seltzer{p1, p2}a2Michael Stonebraker{p1}a3Margo I. Seltzer{p3, p4}a4M. Stonebraker{p3}

Paper

Relational Dataset

duplicate

Resolve

Graph Representation

u

1

,

u

3

p

1

,

p3

duplicate

Problem Definition

Given a relational dataset

D

, and a cost budget

BG

,Our goal is to develop a progressive approach that produces a high-quality result using

BG units of cost.

ER Graph

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S

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ER Graph

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Partially Constructed Graph

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Resolution Windows

Window

1

Window

2

Window

n

…

Plan Generation.

Plan Execution ( ).

Resolution Plan ( ) Set of blocks (

) to be

instantiated

. Set of

nodes (

) to be

resolved.

BG

Lazy Resolution Strategy

Plan Cost and Benefit

Node Benefit

…

…

…

…

…

…

Indirect

Benefit

Direct Benefit

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1

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State

Generate a plan such that:

h .

is maximized.

Benefit-

vs

-Cost Analysis:

Each node and block has an updated cost and benefit.

Plan Generation Phase

NP-hard

Oregon-Trail Knapsack

Instantiated

Unresolved Nodes

Step#1

Step#2

Uninstantiated

Blocks

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Plan Generation Algorithm

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Step#3

If

>

else

return and

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…

Plan Generation Algorithm

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Experimental

Evaluation

Papers

(P)Authors

(A)Venues

(U)

= (Title

, Abstract, Keywords

, Authors,

Venue).

= (Name, Email, Affiliation,

Address, Paper).

= (Name, Year, Pages, Papers). Number of EntitiesBlocking Functions

Similarity Functions

Resolve Function

P

30,000

2

3

Naïve Bayes

A83,15214Naïve BayesU30,00013Naïve Bayes CiteSeerX Dataset

Algorithms:

DepGraph.X. Dong et al. Reference reconciliation in complex information spaces. SIGMOD.Static.

S. E. Whang et al. Joint entity resolution. ICDE

.

Full:

No lazy resolution strategy.

Random:

Lazy resolution strategy but with random order.

Experimental

Evaluation

R

R

1

R

4

R

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…

T

6

T1T3…S2S6S5…TS

Time vs. Recall

Our Approach

Random

Full

Execution Time (sec)

300.33

396.55

542.43

Plan Generation4.76%

3.81%

2.58%

Plan Execution95.11%96.17%

97.40

Lazy Resolution with

WorkflowOur Approach

Random

Full

Execution Time (sec)

300.33

396.55

542.43

Plan Generation

4.76%3.81%2.58%Reading Blocks4.70%3.75%2.90%Graph Creation8.40%6.25%4.72%Node Resolution82.01%86.17%89.78% Reading Blocks. Creating Nodes. Resolving Nodes.

Conclusion

Progressive Approach to Relational ER.

Cost and benefit model for generating a resolution plan.

Lazy resolution strategy to resolve nodes with the least amount of cost.

Experiments on publication and synthetic datasets to demonstrate the efficiency of our approach.

Questions

