Computer Science ›› 2022, Vol. 49 ›› Issue (11): 90-97.doi: 10.11896/jsjkx.211100065

• Database & Big Data & Data Science • Previous Articles     Next Articles

Temporal RDF Modeling Based on Relational Database

HAN Xiao, ZHANG Zhe-qing, YAN Li   

  1. College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
  • Received:2021-11-05 Revised:2022-02-22 Online:2022-11-15 Published:2022-11-03
  • About author:HAN Xiao,born in 1996,postgraduate.His main research interests include semantic web and temporal RDF data.
    YAN Li,born in 1964,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include big data,know-ledge graph,spatiotemporal information processing and NoSQL database.
  • Supported by:
    Basic Research Program of Jiangsu Province,China(BK20191274).

Abstract: With the increase of temporal data,the concept of temporal knowledge graph is popularized,and how to represent temporal knowledge graph efficiently has become an important research direction.Although resource description framework(RDF) is widely used in traditional knowledge graph modeling,it can only represent static semantics and lacks the ability to represent temporal knowledge graph.Therefore,several temporal RDF models have been proposed for temporal knowledge graph,but all these models simply attach temporal information to the predicate of RDF or the whole triple,and lack the accurate positioning of the object to which the temporal information belongs.In order to better represent temporal knowledge graph,firstly,this paper proposes a new temporal RDF representation model called tRDF,which attaches temporal information to the object or predicate according to the type of object.Secondly,by combining the concept of temporal database,this paper presents a tRDF data storage method based on the relational database,PostgreSQL.Finally,the proposed tRDF data storage method is verified from two aspects,the time of storing and the size of space.Experimental results show that the proposed scheme can effectively represent temporal knowledge graph.

Key words: RDF, Temporal expansion, Temporal RDF, Temporal knowledge graph, Temporal database

CLC Number: 

  • TP399
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