Partitioning OWL Knowledge Bases for Parallel Reasoning

@article{Priya2014PartitioningOK,
  title={Partitioning OWL Knowledge Bases for Parallel Reasoning},
  author={Sambhawa Priya and Yuanbo Guo and Michael Spear and Jeff Heflin},
  journal={2014 IEEE International Conference on Semantic Computing},
  year={2014},
  pages={108-115}
}
The ability to reason over large scale data and return responsive query results is widely seen as a critical step to achieving the Semantic Web vision. We describe an approach for partitioning OWL Lite datasets and then propose a strategy for parallel reasoning about concept instances and role instances on each partition. The partitions are designed such that each can be reasoned on independently to find answers to each query sub goal, and when the results are unioned together, a complete set… CONTINUE READING