Discovery of semantic associations in an RDF graph using bi-directional BFS on massively parallel hardware. (2016) - Viswanathan, V.
Resource description framework (RDF) data model provides a framework to capture the meaning of an entity by specifying how it relates to other entities. Large RDF graph involving millions of entities are common in many semantic graph applications and are challenging to process. For example, finding complex relationships called semantic associations between two entities in an RDF graph is a tedious process. Graphics processing units (GPUs) provides high computation power at low price. Today, the GPUs expose a general data-parallel programming model in the form of CUDA. In this paper, we present the implementation of bi-directional breadth-first-search algorithm to discover the semantic association in RDF graph using CUDA programming model. In the experimental evaluation, we prove that our proposed algorithm is faster than the existing algorithms.