ICS 624 Spring 2011 Entity Resolution with Evolving Rules Preface to Steven Whang’s slides Asst. Prof. Lipyeow Lim Information & Computer Science Department.

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ICS 624 Spring 2011 Entity Resolution with Evolving Rules Preface to Steven Whang’s slides Asst. Prof. Lipyeow Lim Information & Computer Science Department University of Hawaii at Manoa 3/30/20111Lipyeow Lim -- University of Hawaii at Manoa

3/30/2011Lipyeow Lim -- University of Hawaii at Manoa2 Outline Facebook LinkedIn Are these two pages referring to the same person ?

Entity Resolution (ER) comparison shopping mailing lists classified ads customer files counter-terrorism 3/30/2011Lipyeow Lim -- University of Hawaii at Manoa3 NameAddressCredit CardPhone SharonRI NameAffiliationPhone SharonIBM r1 r2 Are these two records referring to the same entity ?

Entity Resolution Problem Statement Given a table of records (about some entities), partition the records according to the “entities” that they refer to. 3/30/2011Lipyeow Lim -- University of Hawaii at Manoa4 NameAddressCredit CardPhone SharonRI SharonNY JohnNY JohnNJ Entity: Sharon Entity: John

ER Rules ER Rules: ER algorithm that computes the mapping of records to entities (“partition”) Match-based: boolean rules like – “if the name in the two records are the same, then they belong to the same partition” Distance-based: uses a distance function 3/30/2011Lipyeow Lim -- University of Hawaii at Manoa5 NameAddressCredit CardPhone SharonRI SharonNY JohnNY JohnNJ Entity: Sharon Entity: John ER Rule

Sorted Neighborhood (SN) Avoids comparing all O(n 2 ) pairs of records by: Sorting records based on some column(s) Comparing all pairs of records in a sliding window Merging connected components into entities 3/30/2011Lipyeow Lim -- University of Hawaii at Manoa6 NameAddressCredit CardPhone SharonRI SharonNY JohnNY JohnNJ SortFind pairs

HC B : Hierarchical Clustering Boolean Similar to bottom-up hierarchical agglomerative clustering Merge two clusters if a boolean comparison rule B returns true. Apply rule B on one chosen tuple in each of the two clusters 3/30/2011Lipyeow Lim -- University of Hawaii at Manoa7 NameAddressCredit CardPhone SharonRI SharonNY JohnNY JohnNJ B(r1,r2) = true if r1.name=r2.name

HC BR : Hierarchical Clustering Boolean Same as HC B except in how comparison is evaluated. Apply rule B on all pairs of tuples in each of the two clusters Merge clusters if B is true on at least one pair 3/30/2011Lipyeow Lim -- University of Hawaii at Manoa8 NameAddressCredit CardPhone SharonRI SharonNY JohnNY JohnNJ B(r1,r2) = true if r1.name=r2.name

ME: Monge-Elkan Clustering Sort records according to some column(s) Initialize an empty fixed length queue of clusters Scan through sorted records and match each record to clusters in queue If record matches existing cluster, move cluster to front Else make record into a new cluster at front of queue If queue is full, last cluster is dropped 3/30/2011Lipyeow Lim -- University of Hawaii at Manoa9 NameAddressCredit CardPhone SharonRI SharonNY JohnNY JohnNJ Sort Sharon Queue

Distance-based ER Algorithms Similar to bottom-up hierarchical agglomerative clustering with different variations on how distance is computed from two clusters HC DS Single-link : smallest possible distance between two records from the two clusters HC DC Complete-link : largest possible distance between two records from the two clusters 3/30/2011Lipyeow Lim -- University of Hawaii at Manoa10 R1 R2 R3 R4

Evolving Rules 3/30/2011Lipyeow Lim -- University of Hawaii at Manoa11 Input Records Resolved Records R ER Rule 1 ER Rule 2 Resolved Records S ER Rule 3 Given Rule 1 and Rule 2, can we compute S from R? Under what conditions would that be possible?