Querying Linked Data Based on Hierarchical Multi-Hop Ranking Model

doi:10.1007/s12204-018-1976-z

Journal of Shanghai Jiao Tong University (Science) ›› 2018, Vol. 23 ›› Issue (4): 568-.doi: 10.1007/s12204-018-1976-z

Querying Linked Data Based on Hierarchical Multi-Hop Ranking Model

LI Junxian (李俊娴), WANG Wei (汪卫), WANG Jingjing (王晶晶)

(1. School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, Jiangsu, China; 2. School of Computer Science, Fudan University, Shanghai 200433, China; 3. Yangzhou Polytechnic College, Yangzhou 225009, Jiangsu, China)

出版日期:2018-08-01 发布日期:2018-08-02
通讯作者: LI Junxian (李俊娴) E-mail: lijunxian just@163.com

Querying Linked Data Based on Hierarchical Multi-Hop Ranking Model

LI Junxian (李俊娴), WANG Wei (汪卫), WANG Jingjing (王晶晶)

(1. School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, Jiangsu, China; 2. School of Computer Science, Fudan University, Shanghai 200433, China; 3. Yangzhou Polytechnic College, Yangzhou 225009, Jiangsu, China)

Online:2018-08-01 Published:2018-08-02
Contact: LI Junxian (李俊娴) E-mail: lijunxian just@163.com

摘要/Abstract

摘要： How to query Linked Data effectively is a challenge due to its heterogeneous datasets. There are three types of heterogeneities, i.e., different structures representing entities, different predicates with the same meaning and different literal formats used in objects. Approaches based on ontology mapping or Information Retrieval (IR) cannot deal with all types of heterogeneities. Facing these limitations, we propose a hierarchical multi-hop language model (HMPM). It discriminates among three types of predicates, descriptive predicates, out-associated predicates and in-associated predicates, and generates multi-hop models for them respectively. All predicates’ similarities between the query and entity are organized into a hierarchy, with predicate types on the first level and predicates of this type on the second level. All candidates are ranked in ascending order. We evaluated HMPM in three datasets, DBpedia, LinkedMDB and Yago. The results of experiments show that the effectiveness and generality of HMPM outperform the existing approaches.

关键词: hierarchical multi-hop ranking model (HMPM), Linked Data, language model

Abstract: How to query Linked Data effectively is a challenge due to its heterogeneous datasets. There are three types of heterogeneities, i.e., different structures representing entities, different predicates with the same meaning and different literal formats used in objects. Approaches based on ontology mapping or Information Retrieval (IR) cannot deal with all types of heterogeneities. Facing these limitations, we propose a hierarchical multi-hop language model (HMPM). It discriminates among three types of predicates, descriptive predicates, out-associated predicates and in-associated predicates, and generates multi-hop models for them respectively. All predicates’ similarities between the query and entity are organized into a hierarchy, with predicate types on the first level and predicates of this type on the second level. All candidates are ranked in ascending order. We evaluated HMPM in three datasets, DBpedia, LinkedMDB and Yago. The results of experiments show that the effectiveness and generality of HMPM outperform the existing approaches.

Key words: hierarchical multi-hop ranking model (HMPM), Linked Data, language model

中图分类号:

TP 391

LI Junxian (李俊娴), WANG Wei (汪卫), WANG Jingjing (王晶晶). Querying Linked Data Based on Hierarchical Multi-Hop Ranking Model[J]. Journal of Shanghai Jiao Tong University (Science), 2018, 23(4): 568-.

参考文献 16

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Querying Linked Data Based on Hierarchical Multi-Hop Ranking Model

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