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Querying Linked Data Based on Hierarchical Multi-Hop Ranking Model

  • Junxian Li (李俊娴)
  • Wei Wang (汪卫)
  • Jingjing Wang (王晶晶)
Article

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 

CLC number

TP 391 

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Copyright information

© Shanghai Jiaotong University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Junxian Li (李俊娴)
    • 1
    • 2
  • Wei Wang (汪卫)
    • 2
  • Jingjing Wang (王晶晶)
    • 3
  1. 1.School of ComputerJiangsu University of Science and TechnologyZhenjiang, JiangsuChina
  2. 2.School of Computer ScienceFudan UniversityShanghaiChina
  3. 3.Yangzhou Polytechnic CollegeYangzhou, JiangsuChina

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