Scalable Distributed Temporal Reasoning
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.
KeywordsQualitative temporal reasoning MapReduce Temporal relations
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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