Entity Resolution with Evolving Rules Youzhong Ma 2010-9-25 Lab of WAMDM

Outline Motivations ER Related concepts ER properties Conclusions

Entity Resolution background

Entity Resolution background

Naïve ER Approach Vs. New Approach

Outline Motivations ER Related concepts ER properties Conclusions

ER Related concepts Suppose market A will merge market B They have to combine their customers The same person may occur in two markets’ customer DB, but some attributes are different How to deal with it?

ER Rule Boolean functions determines if two records represent the same entity: true or false. Distance functions How different(similar) the records are.

ER Example

ER procedure The Evolving rule approach only works if the ER algorithm satisfies Certain properties and B2 is Stricter than B1. So one contribution of this paper is to exploit Under what conditions and for what ER algorithms Are incremental approaches feasible? original records set S = {r1,r2,r3,r4} ER input Pi = {{r1},{r2},{r3},{r4}} B1:Pname  E1 = {{r1,r2,r3},{r4}} (6 comps) 6 comps Naïve approach 3 comps Evolving rule B2: Pname ∧ Pzip  E2 = {{r1,r2},{r3},{r4}}

Materialization! original records set S = {r1,r2,r3,r4} ER input Pi = {{r1},{r2},{r3},{r4}} B1:Pname ∧ Pzip  E1 = {{r1,r2},{r3},{r4}} (6 comps) Pname  Ename = {{r1,r2,r3},{r4}} Pzip  Ezip = {{r1,r2},{r3},{r4}} 3comps B2: Pname ∧ Phone  E2 ={{r1},{r2,r3},{r4}}

Outline Motivations ER Related concepts ER properties Conclusions

Two important properties for ER algorithms that enable efficient rule evolution for match-based clustering Rule Monotonicity(RM) Context Free(CF)

Pname ∧ Pzip ≤ Pname

Rule Monotonicity(RM) B1: Pname ∧ Pzip  E1 = {{r1,r2},{r3},{r4}} B2:Pname  E2 = {{r1,r2,r3},{r4}}

Context Free (CF)

Existing properties in literature General Incremental VS. Context Free Order independent VS. Rule Monotonicity An ER algorithm is order independent if the ER result is same regardless of the order of the records processed.

experiments

Outline Motivations ER Related concepts ER properties Conclusions

conclusions Propose a new ER approach with evolving rules Exploiting the properties (RM、CF) of the ER algorithms that enable efficient rule evolution Providing guidance to the ER algorithms designer

Some problems How are the comparision rules generated? How to design the ER Algorithms that hold the RM and CF　properties? How to Implement the ER algorithms in MapReduce framework?

Thanks to everyone of Web Group sincerely

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