Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/24116
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching
Author(s): Tran, Ha-Nguyen
Cambria, Erik
Hussain, Amir
Contact Email: ahu@cs.stir.ac.uk
Keywords: Common-sense reasoning
Subgraph matching
GPU computing
CUDA
Issue Date: Dec-2016
Citation: Tran H, Cambria E & Hussain A (2016) Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching, Cognitive Computation, 8 (6), pp. 1074-1086.
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.
DOI Link: http://dx.doi.org/10.1007/s12559-016-9418-4
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. Published by Springer in Cognitive Computation. The final publication is available at Springer via http://dx.doi.org/10.1007/s12559-016-9418-4

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