Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/24116
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dc.contributor.authorTran, Ha-Nguyen-
dc.contributor.authorCambria, Erik-
dc.contributor.authorHussain, Amir-
dc.date.accessioned2017-01-05T23:42:24Z-
dc.date.issued2016-12-
dc.identifier.urihttp://hdl.handle.net/1893/24116-
dc.description.abstractBackground/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.en_UK
dc.language.isoen-
dc.publisherSpringer Verlag-
dc.relationTran H, Cambria E & Hussain A (2016) Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching, Cognitive Computation, 8 (6), pp. 1074-1086.-
dc.rightsThis 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-
dc.subjectCommon-sense reasoningen_UK
dc.subjectSubgraph matchingen_UK
dc.subjectGPU computingen_UK
dc.subjectCUDAen_UK
dc.titleTowards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matchingen_UK
dc.typeJournal Articleen_UK
dc.rights.embargodate2017-11-30T00:00:00Z-
dc.rights.embargoreasonPublisher requires embargo of 12 months-
dc.identifier.doihttp://dx.doi.org/10.1007/s12559-016-9418-4-
dc.citation.jtitleCognitive Computation-
dc.citation.issn1866-9956-
dc.citation.volume8-
dc.citation.issue6-
dc.citation.spage1074-
dc.citation.epage1086-
dc.citation.publicationstatusPublished-
dc.citation.peerreviewedRefereed-
dc.type.statusPost-print (author final draft post-refereeing)-
dc.author.emailahu@cs.stir.ac.uk-
dc.citation.date08/08/2016-
dc.contributor.affiliationNanyang Technological University-
dc.contributor.affiliationNanyang Technological University-
dc.contributor.affiliationComputing Science - CSM Dept-
dc.rights.embargoterms2017-12-01-
dc.rights.embargoliftdate2017-12-01-
dc.identifier.isi000389304300006-
Appears in Collections:Computing Science and Mathematics Journal Articles

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