Towards GPU Based Common Sense Reasoning: Using Fast Subgraph Matching
Background/IntroductionCommon 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.MethodsIn 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 ConclusionsWe show that GpSense outperforms state of the art algorithms and efficiently answers subgraph queries on large common sense graphs.
Cognitive Computation