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Abstract

In this paper, we propose the design and implementation of a large-scale qualitative temporal reasoner, MRQUTER, which can perform reasoning over large Web-scale knowledge bases. This temporal reasoner is built on a Hadoop cluster system using the MapReduce parallel programming framework. It decomposes the entire qualitative temporal reasoning process into several MapReduce jobs and incorporates some optimization techniques into each reasoning job component, implemented using a pair of Map and Reduce functions. Through experiments using large benchmarking temporal knowledge bases, MRQUTER shows high reasoning performance and scalability.

Keywords

Qualitative temporal reasoning MapReduce Temporal relations 

Notes

Acknowledgements

This work was supported by the Technology Innovation Program (No. 10060086, A robot intelligence software framework as an open and self-growing integration foundation of intelligence and knowledge for personal service robots) funded By the Ministry of Trade, industry & Energy (MI, Korea).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Kyonggi UniversitySuwonKorea

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