Large Scale Knowledge Base Systems: An Empirical Evaluation Perspective

Yuanbo Guo, Abir Qasem, Jeff Heflin

In this paper, we discuss how our work on evaluating Semantic Web knowledge base systems (KBSs) contributes to address some broader AI problems. First, we show how our approach provides a benchmarking solution to the Semantic Web, a new application area of AI. Second, we discuss how the approach is also beneficial in a more traditional AI context. We focus on issues such as scalability, performance tradeoffs, and the comparison of different classes of systems.

Subjects: 11. Knowledge Representation

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.