1. Entity Profiling with Varying Source Reliabilities
Furong Li, Mong Li Lee, Wynne Hsu
{furongli, leeml, comp.nus.edu.sg
National University of Singapore

2. Outline Motivation Proposed Method Performance Study Conclusion
The presentation contains four parts and we start from motivation

3. Entities in Multiple Sources
a real world entity's information is, more often than not, published by multiple data sources.
Here we see a webpage of a restaurant, and its urbanspoon entry, and the foursquare page.
Our first observation is, different data sources provide Various name representations
Various name representations

4. Entities in Multiple Sources
The source may contain erroneous attribute values
The phone numbers are different and one of them is wrong
Various name representations
Erroneous attribute values

5. Entities in Multiple Sources
No opening hours
Third, a source may publish Incomplete information.
Various name representations
Erroneous attribute values
Incomplete information

6. Entities in Multiple Sources
Furthermore, there are Ambiguous references.
We can see a page with very similar name, but actually refers to another entity
Various name representations
Erroneous attribute values
Incomplete information
Ambiguous references

7. What We Want Entity Profiling Various name representations
Erroneous attribute values
Incomplete information
Ambiguous references
Given the information published by different data sources, which has these properties
We want to have a complete and accurate picture of a real world entity by fusing the information above
And we call it entity profiling.
Entity Profiling

8. The Problem Involves Two Tasks
Record linkage
[Getoor et al, VLDB’12], [Negahban et al, CIKM’12]
Truth discovery
[Li et al, VLDB’13], [Yin et al, KDD’07]
(LocalEats)
(FourSquare)
(Urbanspoon)
Frank
First record linkage: link the real world entities and the records published by various sources
Second truth discovery: Find true values from data provided by different sources
Now you may realize that the second relies on the output of the first one.
But such a pipeline solution may not work well
The erroneous values may prevent correct linkages
Incomplete picture of the data limits the effectiveness of truth discovery

9. State-of-the-art Method
[Guo et al, VLDB’10]
Assume a set of (soft) uniqueness constraints
Transform records into attribute value pairs
Limitations
Make wrong associations when the percentage of erroneous values increases
Computationally expensive
The uniqueness constraint limits its generality
So we consider how to simultaneously solve the two tasks
The state of the art method…

10. Outline
Motivation
Proposed Method
Performance Study
Conclusion

11. A Motivating Example matching -> profiles √ √ ? X
Suppose we want to collate information on researchers in Computer Science.
We could first obtain a list of researchers from acm website.
Since the information in these reference records is limited,
we would look at other data sources, such as university home pages, object-level search engines, LinkedIn, to harvest more information. ? √ √ X

12. A Motivating Example
matching
-> profiles ? √ √ X

13. The Example Tells Us
The data sources are not equally reliable among different attributes
Introduce a reliability matrix 𝑀[𝑠,𝑎]
Lower the impact of erroneous values on matching decisions
Rectifying errors in attribute values provides additional evidence for linking records
Interleave the processes of record linkage and error correction
M[s,a] reliability of each source on each attribute

14. The Proposed Two-phase MethodX X
The first phase is a confidence based matching that links each input record to one or more reference records.
Based on this, we obtain an initial assessment of the reliability of the sources.
Then the second phase, adaptive matching, iteratively determine the correct attribute values and eliminate unlikely matches for the input records.
This iterative process is exactly how we interleave record linkage and truth discovery.

15. Confidence Based Matching
Bootstrap the framework with a small set of confident matches
Initialize reliability matrix based on confident matches X
Two proposes
For attributes that are not contained in reference records, set to the average of the row

16. Confidence Based Matching
Distinguish sources
Reliable: 𝑠𝑟𝑐 1 , 𝑠𝑟𝑐 2 , 𝑠𝑟𝑐 3
𝑟 5 , 𝑟 4
Unreliable: { 𝑠𝑟𝑐 4 , 𝑠𝑟𝑐 5 }
𝑟 7 , 𝑟 8 , 𝑟 10
Given the confident matches, we want to Distinguish sources

17. Confidence Based Matching
Distinguish sources
Reliable: 𝑠𝑟𝑐 1 , 𝑠𝑟𝑐 2 , 𝑠𝑟𝑐 3
𝑟 5 , 𝑟 4
Unreliable: { 𝑠𝑟𝑐 4 , 𝑠𝑟𝑐 5 }
𝑟 7 , 𝑟 8 , 𝑟 10

18. Confidence Based Matching
Distinguish sources
Reliable: 𝑠𝑟𝑐 1 , 𝑠𝑟𝑐 2 , 𝑠𝑟𝑐 3
𝑟 5 , 𝑟 4
Unreliable: { 𝑠𝑟𝑐 4 , 𝑠𝑟𝑐 5 }
𝑟 7 , 𝑟 8 , 𝑟 10

19. Adaptive Matching X X Cluster signatures <- current truth
Update reliability matrix
Refine clusters by pruning
the most dissimilar records
First, for each cluster $c$ obtained in the previous phase, we build its signature $H_c$ based on the accuracy of the attribute values of the records in $c$.
The reliability matrix is updated subsequently.
Then each record $r$ is compared with the signatures of the clusters it is associated with,
and pruned from the cluster that it is the most dissimilar to.
The above steps are repeated until there is no change to the clusters.
Discount records belonging to multiple clusters
Lower the impact of erroneous values on our matching decision

20. Outline
Motivation
Proposed Method
Performance Study
Conclusion

21. Comparative Methods PIPELINE MATCH [3] COMET
Record linkage [1] + Truth discovery [2]
MATCH [3]
State-of-the-art method
COMET
The proposed method
Negahban et al. Scaling multiple-source entity resolution using statistically efficient transfer learning. In CIKM, 2012
Yin et al. TruthFinder. In KDD, 2007
Guo et al. Record linkage with uniqueness constraints and erroneous values. VLDB, 2010

22. Results on Restaurant Dataset
The evaluation contains two parts: how good is the record matching and the quality of the constructed profiles.

23. %err: percentage of erroneous values
%ambi: percentage of records with abbreviated names
We vary two variables
We can significantly improve the recall

24. %err: percentage of erroneous values
%ambi: percentage of records with abbreviated names

25. Scalability Experiments

26. Outline
Motivation
Proposed Method
Performance Study
Conclusion

27. Conclusion
Address the problem of building entity profiles by collating data records from multiple sources in the presence of erroneous values
Interleave record linkage with truth discovery
Varying source reliabilities
Reduce the impact of erroneous values on matching decisions

28. Thanks! Q & A