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An Incremental Reasoning Algorithm for Large Scale Knowledge Graph

  • Yifei Wang
  • Jie Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)

Abstract

Knowledge graphs usually contain much implicit semantic information, which needs to be further mined through semantic inference. Current algorithms can effectively accomplish such task, however they often require a full re-reasoning even when only a few new triples is added to expand the knowledge graph. In this paper, we propose an incremental reasoning algorithm which can effectively avoid re-reasoning over the entire knowledge graph while keeping the relative completeness of the final deduction results. Key to our approach is the filter algorithms which reduce the scale of data that need to be considered and a delay strategy which limit the number of time-consuming iterations while still preserve relative completeness. Extensive experiments and comprehensive evaluations are conducted and experimental results prove that our methods significantly outperform re-reasoning methods.

Keywords

Knowledge reasoning Incremental reasoning Knowledge graph OWL2 RL 

Notes

Acknowledgments

This work was supported by National Natural Science Foundation of China (Grand No. 61502022) and State Key Laboratory of Software Development Environment (Grand No. SKLSDE-2017ZX-17).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Software Development Environment, School of Computer Science and EngineeringBeihang UniversityBeijingChina

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