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Cognitive Computation

, Volume 8, Issue 6, pp 1074–1086 | Cite as

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

  • Ha-Nguyen Tran
  • Erik CambriaEmail author
  • Amir Hussain
Article

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.

Keywords

Common-sense reasoning Subgraph matching GPU computing CUDA 

Notes

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.

Compliance with Ethical Standards

Conflict of Interest

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

Informed Consent

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

Human and Animal Rights

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

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Division of Computing Science and Maths, Faculty of Natural SciencesUniversity of StirlingStirlingScotland, UK
  3. 3.Anhui UniversityHefeiChina

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