RDFKB: a semantic web knowledge base

  • Authors:

  • James P. McGlothlin;Latifur Khan;Bhavani Thuraisingham

  • Affiliations:

  • The University of Texas at Dallas, Richardson, Texas;The University of Texas at Dallas, Richardson, Texas;The University of Texas at Dallas, Richardson, Texas

  • Venue:
  • IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
  • Year:
  • 2011

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Abstract

There are many significant research projects focused on providing semantic web repositories that are scalable and efficient. However, the true value of the semantic web architecture is its ability to represent meaningful knowledge and not just data. Therefore, a semantic web knowledge base should do more than retrieve collections of triples. We propose RDFKB (Resource Description Knowledge Base), a complete semantic web knowledge case. RDFKB is a solution for managing, persisting and querying semantic web knowledge. Our experiments with real world and synthetic datasets demonstrate that RDFKB achieves superior query performance to other state-of-the-art solutions. The key features of RDFKB that differentiate it from other solutions are: 1) a simple and efficient process for data additions, deletions and updates that does not involve reprocessing the dataset; 2) materialization of inferred triples at addition time without performance degradation; 3) materialization of uncertain information and support for queries involving probabilities; 4) distributed inference across datasets; 5) ability to apply alignments to the dataset and perform queries against multiple sources using alignment. RDFKB allows more knowledge to be stored and retrieved; it is a repository not just for RDF datasets, but also for inferred triples, probability information, and lineage information. RDFKB provides a complete and efficient RDF data repository and knowledge base.