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Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching

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Abstract

Background/Introduction

Common-sense reasoning is concerned with simulating cognitive human ability to make presumptions about the type and essence of ordinary situations encountered every day. The most popular way to represent common-sense knowledge is in the form of a semantic graph. Such type of knowledge, however, is known to be rather extensive: the more concepts added in the graph, the harder and slower it becomes to apply standard graph mining techniques.

Methods

In this work, we propose a new fast subgraph matching approach to overcome these issues. Subgraph matching is the task of finding all matches of a query graph in a large data graph, which is known to be a non-deterministic polynomial time-complete problem. Many algorithms have been previously proposed to solve this problem using central processing units. Here, we present a new graphics processing unit-friendly method for common-sense subgraph matching, termed GpSense, which is designed for scalable massively parallel architectures, to enable next-generation Big Data sentiment analysis and natural language processing applications.

Results and Conclusions

We show that GpSense outperforms state-of-the-art algorithms and efficiently answers subgraph queries on large common-sense graphs.

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Notes

  1. https://developer.nvidia.com/what-cuda.

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Acknowledgments

This work was conducted within the Rolls-Royce@NTU Corp Lab with support from the National Research Foundation Singapore under the Corp Lab@University Scheme. The study was also supported by the National Natural Science Foundation of China (NNSFC) (Grant Numbers 61402386, 61305061 and 61402389). A. Hussain was supported by the Royal Society of Edinburgh (RSE) and NNSFC Joint Project Grant No. 61411130162, and the UK Engineering and Physical Science Research Council (EPSRC) Grant No. EP/M026981/1. We also wish to thank the anonymous reviewers who helped improve the quality of the paper.

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Authors and Affiliations

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

    Ha-Nguyen Tran & Erik Cambria

  2. Division of Computing Science and Maths, Faculty of Natural Sciences, University of Stirling, Stirling, Scotland, UK

    Amir Hussain

  3. Anhui University, Hefei, Anhui, China

    Amir Hussain

Authors
  1. Ha-Nguyen Tran
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  2. Erik Cambria
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  3. Amir Hussain
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Correspondence to Erik Cambria.

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Conflict of Interest

Ha-Nguyen Tran, Erik Cambria, and Amir Hussainy declare that they have no conflict of interest.

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Informed consent was not required as no human or animals were involved.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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Tran, HN., Cambria, E. & Hussain, A. Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching. Cogn Comput 8, 1074–1086 (2016). https://doi.org/10.1007/s12559-016-9418-4

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  • DOI: https://doi.org/10.1007/s12559-016-9418-4

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