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A Practical Large Scale Demonstration



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Hawkeye A Practical Large Scale Demonstration of Semantic Web Integration Zhengxiang Pan Abir Qasem Sudhan Kanitkar Fabiana Prabhakar Jeff Heflin Department of Computer Science and Engineering Lehigh University 19 Memorial Dr West Bethlehem PA 18015 U S A zhp2 abq2 sgk205 ffp206 heflin cse lehigh edu Abstract We discuss our DLDB knowledge base system and evaluate its capability in processing a very large set of real world Semantic Web data Using DLDB we have constructed the Hawkeye knowledge base in which we have loaded more than 166 million facts from a diverse set of real world data sources We use this knowledge base to demonstrate realistic integration queries in egovernment and academic scenarios In order to support Hawkeye we extended DLDB with additional reasoning capabilities At present the Semantic Web consists of numerous independent ontologies We demonstrate that OWL can be used to integrate these ontologies and thereby integrate the data sources that commit to them In terms of performance we show that the load time of our system is linear on the number of triples loaded Furthermore we show that many complex queries have response times under one minute and that simple queries can be answered in seconds 1 Introduction The 2005 index of Swoogle 2 contained 850 000 of Semantic Web documents in 2006 this index had 1 5 million SW documents and at the time of writing this paper it boasts a staggering 2 1 million SW Documents The Semantic Web is growing and clearly scalability is an important requirement for Semantic Web systems Furthermore the Semantic Web is an open and decentralized system where different parties can and will in general adopt different ontologies Thus merely using ontologies does not reduce heterogeneity it just raises heterogeneity problems to a different level Without some form of alignment the data that is described in terms of one ontology will be inaccessible to users that ask questions in terms of another ontology In this paper we



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