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

Outline Motivation Problem definition Proposed method Experiments Conclusion

Motivation Entity recommendation services …

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

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?

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?

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.

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

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.

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)

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

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

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”)

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

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

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

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.

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