Relational%20Clustering%20for%20Entity%20Resolution%20Queries

1
Relational Clustering for Entity Resolution
Queries

• Indrajit Bhattacharya, Louis Licamele
• and Lise Getoor
• University of Maryland, College Park

2
The Entity Resolution Problem
Abdulla Ansari
WeiWei Wang
Chih Chen
P1 A mouse immunity model, W.Wang, C.Chen,
A.Ansari P2 A better mouse immunity model,
W.Wang, A.Ansari P3 Measuring protein-bound
fluxetine, L.Li, C.Chen, W.Wang P4
Autoimmunity in biliary cirrhosis, W.W.Wang,
A.Ansari
Wenyi Wang
Liyuan Li
Chien-Te Chen

• Discover the domain entities
• Map each reference to an entity

3
Query-time ER Motivation

• Most publicly available databases do not have
  resolved entities
• PubMed, CiteSeer have many unresolved authors
• Millions of queries everyday require resolved
  entities directly or indirectly
• I am looking for all papers by Stuart Russell
• How do we address this problem?
• Leave the burden on the user to do the resolution
• Ask owners to clean their databases
• Develop techniques for query-time resolution

4
Entity Resolution Queries

• Disambiguation Query
• Among all papers with W Wang as author, find
  those written by WeiWei Wang

P1 A mouse immunity model, W.Wang, C.Chen,
A.Ansari P2 A better mouse immunity model,
W.Wang, A.Ansari P3 Measuring protein-bound
fluxetine, L.Li, C.Chen, W.Wang

• Resolution Query
• Do disambiguation
• Also retrieve papers by WeiWei Wang with a
  different author name, e.g. W W Wang etc

P1 A mouse immunity model, W.Wang, C.Chen,
A.Ansari P2 A better mouse immunity model,
W.Wang, A.Ansari P4 Autoimmunity in biliary
cirrhosis, W.W.Wang, A.Ansari
5
Query-time ER using Relations

• Simple approach for resolving queries
• Use attributes
• Quick but not accurate
• Use best techniques available
• Collective resolution using relationships
• How can localize collective resolution?
• Two-phase collective resolution for query
• Extract minimal set of relevant records
• Collective resolution on extracted records

6
Cut-based Evaluation of Relational Clustering

• Vertices embedded in attribute space
• Additional (hyper)edges represent relationships

C3
C3
C1
C1
C2
C2
C4
C4

• Good separation of attributes
• Many cluster-cluster relationships
• C1-C3, C1-C4, C2-C4

• Worse in terms of attributes
• Fewer cluster-cluster relationships
• C1-C3, C2-C4

7
A Cut-based Objective Function
weight for attributes
weight for relations
similarity of attributes
1 iff relational edge exists between ci and cj
compatibility of ci and cj

• Greedy clustering algorithm merge cluster pair
  with max reduction in objective function

• Common cluster neighborhood
• Jaccard works better than intersection

• Similarity of attributes
• Jaro, Levenstein TF-IDF

8
Extracting Relevant Records
Name expansion
Name expansion
Hyper-edge expansion
Query
Level 0
Level 1
Level 2
P4 A Ansari P2 A Ansari P1 A Ansari P1 C
Chen P3 C Chen P3 L Li
P A Ansari P A Ansari P C Chen P C
Chen P L Li P L Li
W Wang
P4 W W Wang P1 W Wang P2 W Wang P3 W Wang
Start with query name or record

• Alternate between
• Name expansion For any relevant record, include
  other records with that name
• Hyper-edge Expansion For any relevant record,
  include other related records

Terminate at some depth k
9
Adaptive Expansion for a Query

• Too many records with unconstrained expansion
• Adaptively select records based on ambiguity
• Chen is more ambiguous than Ansari
• Adaptive Name Expansion
• Expand the more ambiguous records
• They need extra evidence
• Adaptive Hyper-edge expansion
• Add fewer ambiguous records
• They lead to imprecision

10
Unsupervised Estimation of Ambiguity

• Probability of multiple entities sharing an
  attribute value
• Estimate ambiguity of one single valued attribute
  (A1a) using another (A2)
• Count number of different values of A2 observed
  for records having A1a
• e.g. different first initials for last-name
  Smith
• Estimate improves with more independent
  attributes

11
Evaluation Datasets

• arXiv High Energy Physics
• 29,555 publications, 58,515 refs to 9,200 authors
• Queries All ambiguous names (75 in total)
• True authors per name 2 to 11 (avg. is 2.4)
• Elsevier BioBase
• 156,156 publications, 831,991 author refs
• Keywords, topic classifications, language,
  country and affiliation of corresponding author,
  etc
• Queries 100 most frequent names
• True authors per name 1 to 100 (avg. is 32)

12
Growth Rate of Relevant Records and Query
Processing Time

• Number of relevant references grows rapidly with
  expansion depth

RC-ER is fast but not good enough for query-time
resolution
13
Query-time ER Results

• Unconstrained expansion
• Collective resolution more accurate
• Accuracy improves beyond depth 1

A pair-wise attributes similarity AN also
neighbors attributes transitive closure

• Adaptive expansion
• Minimal loss in accuracy
• Dramatic reduction in query processing time

AX-2 adaptive expansion at depths 2 and
beyond AX-1 adaptive expansion even at depth 1
14
Conclusions

• Query-centric entity resolution
• Cut-based evaluation of relational clustering
• Adaptive selection of relevant references for a
  query
• Resolution at query-time with minimal loss in
  accuracy

Future Directions

• Spectral algorithm for relational clustering
• Stronger coupling between extraction and
  resolution
• Localized resolution for incoming records

15
References

• "Query-Time Entity Resolution", Indrajit
  Bhattacharya, Louis Licamele and Lise Getoor, ACM
  SIGKDD, 2006
• "A Latent Dirichlet Model for Unsupervised Entity
  Resolution", Indrajit Bhattacharya and Lise
  Getoor, SIAM Data Mining, 2006
• "Entity Resolution in Graphs", Indrajit
  Bhattacharya and Lise Getoor, Chapter in Mining
  Graph Data, Lawrence B. Holder and Diane J. Cook,
  Editors, Wiley, 2006 (to appear).
• "Relational Clustering for Multi-type Entity
  Resolution", Indrajit Bhattacharya and Lise
  Getoor, SIGKDD Workshop on Multi Relational Data
  Mining (MRDM), 2005