Directing Web Search Engines using a Knowledge Amplification by Structured Expert Randomization Architecture.
Stuart H. Rubin, Isaí Michel Lombera, Michael Armella, Jeremy Conn, Shu‐Ching Chen, Gordon K. Lee · 2009
Abstract. The capability to dynamically retrieve detailed multimedia which may come from knowledge bases as well as sensor information in response to specific user queries offers the potential to create decision support systems of unprecedented utility. Such systems can learn from user feedback; by minimizing the system training required of the knowledge engineer, we can more effectively process vast free-text databases of knowledge for minimal development cost. Furthermore, these bases may be concurrently created and maintained and search algorithms can run on parallel processors connected in heterogeneous distributed networks. This paper presents an approach to web searching using knowledge amplification by structured expert randomization platform. Creativity, training, and decision support via