Materializing and Persisting Inferred and Uncertain Knowledge in RDF Datasets - McGlothlin, James; Khan, Latifur (Proceedings of the AAAI Conference on Artificial Intelligence, 2010)
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. Advanced semantic web ontologies, especially in medicine and science, are developing. As more complex ontologies are developed, there is a 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. OWL (Web Ontology Language) defines rules that govern provable inference in semantic web datasets. In this paper, we detail a database schema using bit vectors that is designed specifically for RDF datasets. We introduce a framework for materializing and storing inferred triples. Our bit vector schema enables storage of inferred knowledge without a query performance penalty. Inference queries are simplified and performance is improved. Our evaluation results demonstrate that our inference solution is more scalable and efficient than the current state-of-the-art. There are also standards being developed for representing probabilistic reasoning within OWL ontologies. We specify a framework for materializing uncertain information and probabilities using these ontologies. We define a multiple vector schema for representing probabilities and classifying uncertain knowledge using thresholds. This solution increases the breadth of information that can be efficiently retrieved.