1. Leverage Consensus Partition for Domain-Specific Entity Coreference
龚赛赛

2. Contents Introduction Overview of Approach
Improve quality of labeled data from user feedback
Schema independent learning approach
Conclusion

3. Introduction Human intelligence is valuable for entity coreference
Manually resolve coreference of given URIs
Collect training examples …
However, quality of labeled data from user feedback is not always satisfying
To improve quality, we use the approach of consensus partition.

4. Introduction
Modeling entity coreference at a high level is considerable
Two challenges
Usually, property matching not available or enough for entity resolution
In many cases, datasets used for statistics not available
To deal with the challenges, we build a classifier for entity coreference based on improved labeled data
Labeled data can be relatively small c.p. datasets
Classifier based on weak features can be enhanced

5. Related work Improving labeled data Modeling entity resolution
Detect good and bad worker
Modeling entity resolution
Rule based
Graph based
Learning based
…..

6. Overview of Approach

7. Overview of Approach Running Example
Mike browse u1and label <u1,u2>, <u1,u3> are coreferent. Again, he browse u4 and label <u4,u5> are coreferent. So, his partition is {u1u2u3|u4u5}
Similarly, Tom browses u1 and label <u1,u3> coreferent, and browses u5 labeling coreferent pair <u5,u1>. Also he browse u2 and label <u2,u4> coreferent. His partition becomes {u1 u3 u5| u2 u4}
Alice’s partition is {u1u3|u2u4}
Finally, consensus partition is {u1 u3| u2 u4| u5}

8. Improve quality of labeled data from user feedback
Compute a consensus partition that minimize disagreement between input partitions
Using symmetric difference
Maximizing
Hierarchical clustering (average link)

9. Schema independent learning approach
Learning model: random forest
Bagging with decision tree
Handle noise
Handle imbalanced training data
Enhance weak learner
Feature: property pair value similarity (schema independent)
URIs: vsim=1 iff identical or in equivalent class
Numeric literals: vsim=1 iff difference less than threshold
Boolean literals: vsim=1 iff value equal
Other literals: Jaccard similarity

10. Conclusion Possible contributions
Propose a new method of improving labeled data from user feedback based on consensus partition
Propose a novel approach for entity coreference which is schema independent with high accuracy
Evaluate the effect of consensus partition to the quality of labeled data
Evaluate the performance of our learning approach compared with other approaches
Information gain based
SVM based

11. Thank you for you attention !