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Progressive Approach to Relational Entity Resolution

Progressive Approach to Relational Entity Resolution Yasser Altowim , Dmitri Kalashnikov, Sharad Mehrotra. Progressive ER. Progressive ER. Relational Dataset. Paper. Author Venue. Graph Representation. a 1 , a 3. p 2 , p 4. u 2 , u 4. a 2 , a 4. p 1 , p 3. u 1 , u 3.

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Progressive Approach to Relational Entity Resolution

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  1. Progressive Approach to Relational Entity Resolution Yasser Altowim, Dmitri Kalashnikov, SharadMehrotra

  2. Progressive ER Progressive ER

  3. Relational Dataset Paper Author Venue

  4. Graph Representation a1,a3 p2,p4 u2,u4 a2,a4 p1,p3 u1,u3 Resolve duplicate

  5. 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. • Given a relational dataset D, and a cost budget BG.

  6. ER Graph T1 R1 S1 S2 R2 T2

  7. ER Graph V1 V9 V5 V2 V10 V6 V3 V11 V7 R1 S1 T1 V4 V8 V12 S2 R2 T2

  8. Partially Constructed Graph V9 V1 V5 V10 V2 V6 V11 V3 V7 T1 R1 S1 V8 V12 V4 S2 R2 T2

  9. Resolution Windows BG … Window2 Window1 Windown Plan Generation. Plan Execution ( ). Resolution Plan ( ) Lazy Resolution Strategy • Set of blocks ( ) to be instantiated. • Set of nodes ( ) to be resolved.

  10. Plan Cost and Benefit

  11. Node Benefit … … V3 V5 V1 … … V4 V2 V6 … … Direct Benefit Indirect Benefit

  12. Plan Generation Phase • Benefit-vs-Cost Analysis: • Each node and block has an updated cost and benefit. • Generate a plan such that: • h • is maximized. NP-hard

  13. Plan Generation Algorithm Instantiated Unresolved Nodes Step#1 Uninstantiated Blocks Step#2 R1 R2 R4 R5 v2 v1 v4 v2 v7 v1 v6 v13 v6 v10 R6 R8 R9 v16 v16 v15 v10 v21

  14. Plan Generation Algorithm R1 R8 R6 R2 … If > else return and Step#3 v42 v32 v40 v30 v34 v45 v2 v2 v38 v48 v1 v1 v36 v47 v6 v30 v16 v10 v10

  15. Experimental Evaluation • Algorithms: • DepGraph. • X. Dong et al. Reference reconciliation in complex information spaces. SIGMOD. • Static. • S. E. Whang et al. Joint entity resolution. ICDE. • Quality Metric: T S R T6 S2 R1 T1 R4 S6 R5 T3 S5 … … …

  16. Real Dataset - CiteSeerX • Papers(P) • (Title, Abstract, Keywords, Authors, Venue) • |P| = 30,000 • Authors (A) • (Name, Email, Affiliation, Address, Paper) • |A| = 83,152 • Venues (U) • (Name, Year, Pages, Papers) • |U| = 30,000

  17. Time vs. Recall

  18. 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.

  19. Questions

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