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GpSense: A GPU-Friendly Method for Commonsense Subgraph Matching in Massively Parallel Architectures

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Computational Linguistics and Intelligent Text Processing (CICLing 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9623))

Abstract

In the context of commonsense reasoning, spreading activation is used to select relevant concepts in a graph of commonsense knowledge. When such a graph starts growing, however, the number of relevant concepts selected during spreading activation tends to diminish. In the literature, such an issue has been addressed in different ways but two other important issues have been rather under-researched, namely: performance and scalability. Both issues are caused by the fact that many new nodes, i.e., natural language concepts, are continuously integrated into the graph. Both issues can be solved by means of GPU accelerated computing, which offers unprecedented performance by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU. To this end, we propose a GPU-friendly method, termed GpSense, which is designed for massively parallel architectures to accelerate the tasks of commonsense querying and reasoning via subgraph matching. We show that GpSense outperforms the state-of-the-art algorithms and efficiently answers subgraph queries on a large commonsense graph.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Ha-Nguyen Tran & Erik Cambria

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  1. Ha-Nguyen Tran
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  2. Erik Cambria
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Correspondence to Erik Cambria .

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  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Tran, HN., Cambria, E. (2018). GpSense: A GPU-Friendly Method for Commonsense Subgraph Matching in Massively Parallel Architectures. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9623. Springer, Cham. https://doi.org/10.1007/978-3-319-75477-2_39

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  • DOI: https://doi.org/10.1007/978-3-319-75477-2_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75476-5

  • Online ISBN: 978-3-319-75477-2

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