1. Self-tuning in Graph-Based Reference Disambiguation
Rabia Nuray-Turan
Dmitri V. Kalashnikov
Sharad Mehrotra
Computer Science Department
University of California, Irvine

2. DASFAA 2007, Bangkok, Thailand
Overview
Intro to Data Cleaning
Entity resolution
RelDC Framework
Past work
Adapting to data
The new part
Reduction to an Optimization problem
Linear programming
Experiments
11 January 2019
DASFAA 2007, Bangkok, Thailand

3. DASFAA 2007, Bangkok, Thailand
Data Cleaning
Analysis on bad data leads to wrong conclusions
11 January 2019
DASFAA 2007, Bangkok, Thailand

4. Example of the problem: CiteSeer top-K
Suspicious entries
Lets go to DBLP website
which stores bibliographic entries of many CS authors
Lets check two people
“A. Gupta”
“L. Zhang”
they are in top-20 because there are many of them
CiteSeer: the top-k most cited authors
DBLP
DBLP
11 January 2019
DASFAA 2007, Bangkok, Thailand

5. Two Most Common Entity-Resolution Challenges
Fuzzy lookup
reference disambiguation
match references to objects
list of all objects is given
Fuzzy grouping
group together object repre-sentations, that correspond to the same object
11 January 2019
DASFAA 2007, Bangkok, Thailand

6. Standard Approach to Entity Resolution
11 January 2019
DASFAA 2007, Bangkok, Thailand

7. DASFAA 2007, Bangkok, Thailand
Overview
Intro to Data Cleaning
RelDC Framework
Past work
Adapting to data
The new part
Reduction to an Optimization problem
Linear programming
Experiments
11 January 2019
DASFAA 2007, Bangkok, Thailand

8. DASFAA 2007, Bangkok, Thailand
RelDC Framework
11 January 2019
DASFAA 2007, Bangkok, Thailand

9. DASFAA 2007, Bangkok, Thailand
RelDC Framework
Past work
SDM’05, TODS’06
Domain-independent framework
Viewing the dataset as an Entity Relationship Graph
Analyzes paths in this graph
Solid theoretic foundation
Optimization problem
Scales to large datasets
Robust under uncertainty
High disambiguation quality
No Self-tuning
This paper solves this challenge
11 January 2019
DASFAA 2007, Bangkok, Thailand

10. Entity-Relationship Graph
Choice node
For uncertain references
To encode options/possibilities yr1, … yrN
Among options yr1, … yrN
Pick the most strongly connected one
CAP principle
Analyze paths in G
that exist between xr and yrj, for all j
Use a model to measure connection strength
“Connection strength” model
c(u,v), for nodes u and v in G
how strongly u and v are connected in G
RandomWalk-based
Fixed
Based on Intuition!!!
This paper, instead, learns such a model from data.
11 January 2019
DASFAA 2007, Bangkok, Thailand

11. DASFAA 2007, Bangkok, Thailand
Overview
Intro to Data Cleaning
RelDC Framework
Past work
Adapting to data
The new part
Reduction to an Optimization problem
Linear programming
Experiments
11 January 2019
DASFAA 2007, Bangkok, Thailand

12. DASFAA 2007, Bangkok, Thailand
Adaptive Solution
Classify the found paths in the graph into a finite set of path types
ST ={ T1, T2, …, TN}
If paths p1 and p2 are of the same type then they are treated as identical.
We can show the connection between nodes u and v with a path-type count vector:
Tuv = { c1, c2, …, cN}
If there is a way to associate path Ti to wi then connection strength will be:
11 January 2019
DASFAA 2007, Bangkok, Thailand

13. DASFAA 2007, Bangkok, Thailand
Problems to Answer
How will we classify the paths?
How will we associate each path type with a weight?
11 January 2019
DASFAA 2007, Bangkok, Thailand

14. DASFAA 2007, Bangkok, Thailand
Classifying Paths
Path Type Model (PTM):
Views each path as a sequence of edges
<e1,e2,e3,…,en>
Each edge ei has a type Ei associated with it
Thus, can associate each path p with a string
<E1,E2,E3,…,En>
Different strings correspond to different path types
Associate each string a weight
Different models are also possible
11 January 2019
DASFAA 2007, Bangkok, Thailand

15. Learning Path Weights : Optimization Problem
CAP Principle states that:
the right option will be better connected
Linear programming
Learn path types weight w’s.
11 January 2019
DASFAA 2007, Bangkok, Thailand

16. DASFAA 2007, Bangkok, Thailand
Final Solution
The value of c(xr,yrj)- c(xr,yrl) should be maximized for all r, l≠j
Then final solution:
11 January 2019
DASFAA 2007, Bangkok, Thailand

17. DASFAA 2007, Bangkok, Thailand
Example -Graph
P1= e1-e3-e P2= e1-e1-e3
P3= e1-e2-e2-e P4= e1-e2-e3-e2-e3
11 January 2019
DASFAA 2007, Bangkok, Thailand

18. DASFAA 2007, Bangkok, Thailand
Example- Solution
w1 =1
w3 = w4 = 0
w2 can be anything between 0 and 1.
11 January 2019
DASFAA 2007, Bangkok, Thailand

19. DASFAA 2007, Bangkok, Thailand
Overview
Intro to Data Cleaning
RelDC Framework
Past work
Adapting to data
The new part
Reduction to an Optimization problem
Linear programming
Experiments
11 January 2019
DASFAA 2007, Bangkok, Thailand

20. DASFAA 2007, Bangkok, Thailand
Experimental Setup
Parameters
When looking for L-short simple paths, L = 5
L is the path-length limit
RealMov:
movies (12K)
people (22K)
actors
directors
producers
studious (1K)
producing
distributing
ground truth is known
SynPub datasets:
many ds of five different types
emulation of RealPub
publications (5K)
authors (1K)
organizations (25K)
departments (125K)
ground truth is known
11 January 2019
DASFAA 2007, Bangkok, Thailand

21. Experimental Results on Movies
Parameters :
Fraction : fraction of uncertain references in the dataset
Each reference has 2 choices
11 January 2019
DASFAA 2007, Bangkok, Thailand

22. Experimental Results on Movies- II
Number of options based on PMF Distribution
11 January 2019
DASFAA 2007, Bangkok, Thailand

23. Experimental Results on SynPub
RandomWalk, PTM and the Hybrid Model have the same accuracy
Is RandomWalk the optimum model for Publications domain?
Hybrid Model :
11 January 2019
DASFAA 2007, Bangkok, Thailand

24. Effect of Random Relationships in the Publications Domain
11 January 2019
DASFAA 2007, Bangkok, Thailand

25. DASFAA 2007, Bangkok, Thailand
Summary
Main Contribution
An adaptive solution for connection strength
Model learns the weights of different path types
Ongoing work
Using different models to learn the importance of paths in the connection strength
Use of standard machine learning techniques for learning: such as decision trees, etc…
Different ways to classify paths
11 January 2019
DASFAA 2007, Bangkok, Thailand

26. DASFAA 2007, Bangkok, Thailand
Contact Information
RelDC project
(RESCUE)
Rabia Nuray-Turan (contact author)
Dmitri V. Kalashnikov
Sharad Mehrotra
11 January 2019
DASFAA 2007, Bangkok, Thailand

27. Thank you !