Reasoning with Large Data Sets.
Darko Anicic · 2007
Abstract. Efficient reasoning is a critical factor for successful Semantic Web applications. In this context, applications may require vast volumes of data to be processed in a short time. We develop novel reasoning techniques which will extend current reasoning methods as well as existing database technologies in order to enable large scale reasoning. We propose advances and key design principles primarily in: making an efficient query execution plan as well as in memory, storage and recovery management. Our study is being implemented in Integrated Rule Inference System (IRIS)- a reasoner for Web Service Modeling Language. 1 Problem Statement The Web Service Modeling Language WSML 1 is a language framework for describing various aspects related to Semantic Web (SW) services. We are developing IRIS 2 to serve as a WSML reasoner which handles large workload efficiently. Current inference systems exploit reasoner methods developed rather for small knowledge bases [2]. These systems 3, although utilize mature and efficient