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RDFVector : An Efficient and Scalable Schema for Semantic Web Knowledge Bases

As the semantic web grows in popularity and enters the mainstream of computer technology, RDF(Resource Description Framework) datasets are becoming larger and more complex. As datasets grow larger and more datasets are linked together, scalability becomes more important. As more complex ontologies are developed, there is growing need for efficient queries that handle inference. In areas such as research, it is vital to be able to perform queries that retrieve not just facts but also inferred knowledge and uncertain information. Currently, scalability and performance issues limit the semantic web from reaching its full potential. The primary goal of RDFVector is to improve the performance of queries against large RDF datasets, including queries that involve inferred knowledge and probabilistic reasoning. RDFVector is a new persistent data model using bit vectors that is highly efficient. This design allows us to store inferred knowledge with no penalty. Queries become simpler and more efficient.