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.
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 Jiaotong University(Science), 2018
, 23(4)
: 568
.
DOI: 10.1007/s12204-018-1976-z
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