1. Liang Zheng, Yuzhong Qu Nanjing University, China
An EMD-based Similarity Measure for Multi-Type Entities Using Type Hierarchy
Liang Zheng, Yuzhong Qu
Nanjing University, China

2. Outline Motivation Problem definition Proposed method Experiments
Conclusion

3. Motivation
Entity recommendation services …

4. Motivation
Recommending entities with similar types is an important part of entity recommendation.
. . .
Person
Patent examiner
Jewish scientist
Nobel laureate

5. Q1: How to measure similarity between multi-type entities?
Motivation
Person
Physicist
Jewish scientist
English scientist
Nobel laureate
Mathematician
Patent examiner
Christian Mystics
. . .
. . .
Q1: How to measure similarity between multi-type entities?

6. Q2: how to calculate the weight of entity type?
Motivation
. . .
Person
Patent examiner
Jewish scientist
Nobel laureate
Q2: how to calculate the weight of entity type?

7. Problem definition Given two entities a and b ,
The types of each entity
The weight of the type
Goal: Measure similarity between entities a and b.

8. Problem definition A B … … (ta1 , wa1 ) (tb1 , wb1 ) (tai , wai )
D=[dij]
(tbj , wbj ) …
(tam , wam )
(tbn , wbn )

9. Proposed method
We measure multi-type entity similarity based on the earth mover's distance (EMD) [Rubner 2000], which not only takes into account pairwise type similarity, but also the weighting of entity type.
The EMD is modeled as a solution to the transportation problem.

10. Proposed method Equation of EMD
We find a flow F = { fij }, with fij the flow between tai and tbj , that minimizes the overall cost WORK (A, B, F)

11. Proposed method Entity Type weighting ( PageRank-based Scheme )
The process of understanding entity type is regarded as a random surfing on entity type graph.

12. Proposed method The PR value of entity type
d is the damping factor and N is the total number of vertices
The PageRank-based weighting for entity type

13. Experiments Experimental setup: Dataset: DBpedia; 4 entities
24 users (university students)
2 tasks per user:
Extract the important entity type
Extract the similar entity.
Give ratings 3, 2 and 1 (“closely important/similar”, “somewhat important/similar” and “no important/similar”)

14. Experiments Experimental Results for Type Weighting Schemes
NDCG of the PageRank-based weighting scheme with different p
NDCG of different type weighting schemes

15. Experiments Experimental Results for Different Similarity Measures
NDCG of EMD-based measure with different type weighting schemes
NDCG of different similarity measures

16. Experiments Experimental Results for Entity Recommendation
Comparison of different recommendation methods in terms of (a) precision and (b) recall

17. Conclusion
We propose an EMD-based similarity measure for multi-type entities, which not only takes into account pairwise type similarity, but also the weighting of types.
We also devise a PageRank-based weighting scheme by using type hierarchy.
The experimental results show that PageRank-based weighting scheme outperforms base-line weighting schemes and that our EMD-based similarity measure outperforms traditional similarity measures.

18. THANK YOU!