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Ontology-based Adaptive Reasoning Service for ScienceWeb

Authors:
Hui Shi
Kurt J Maly
Steven J Zeil

Keywords: knowledge bases; ontologies; reasoning

Abstract:
ScienceWeb is a system that provides answers to qualitative and quantitative queries of a large knowledge base covering science research. The system will support the community joining together, sharing the insights of its members, to evolve the large knowledge base. ScienceWeb will need to scale to accommodate the substantial corpus of information about researchers, their projects and their publications. It will need to accommodate the inherent heterogeneity of both its information sources and of its user community. A reasoning system supports the queries and scalability becomes a serious challenge. In this paper we describe experiments and the resulting reasoning architecture and services whose scalability and efficiency are able to meet the requirements of query and answering in ScienceWeb. One key element of the services is an adaptive combination of both Query-invoked Inference and Materialization Inference together with incremental inferencing for more efficient query and answering. Second, we introduce new ways of storing, grouping and indexing objects in the knowledge base for faster searching and reasoning as the size of the triple set scales to the millions and the complexity of the Abox increases. The adaptive reasoning architecture and resulting adaptive reasoning service should provide efficient reasoning based on a scalable knowledge base.

Pages: 131 to 138

Copyright: Copyright (c) IARIA, 2011

Publication date: September 25, 2011

Published in: conference

ISSN: 2308-3549

ISBN: 978-1-61208-152-6

Location: Rome, Italy

Dates: from September 25, 2011 to September 30, 2011