Unifying Web-Scale Search and Reasoning from the Viewpoint of Granularity

  • Authors:

  • Yi Zeng;Yan Wang;Zhisheng Huang;Ning Zhong

  • Affiliations:

  • International WIC Institute, Beijing University of Technology, Beijing, P.R. China 100124;International WIC Institute, Beijing University of Technology, Beijing, P.R. China 100124;Department of Artificial Intelligence, Vrije University Amsterdam, Amsterdam, The Netherlands 1081 and School of Computer Science and Engineering, Jiangsu University of Science and Technology, Jia ...;International WIC Institute, Beijing University of Technology, Beijing, P.R. China 100124 and Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Japan 371-0816

  • Venue:
  • AMT '09 Proceedings of the 5th International Conference on Active Media Technology
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Considering the time constraints and Web scale data, it is impossible to achieve absolutely complete reasoning results. Plus, the same results may not meet the diversity of user needs since their expectations may differ a lot. One of the major solutions for this problem is to unify search and reasoning. From the perspective of granularity, this paper provides various strategies of unifying search and reasoning for effective problem solving on the Web. We bring the strategies of multilevel, multiperspective, starting point from human problem solving to Web scale reasoning to satisfy a wide variety of user needs and to remove the scalability barriers. Concrete methods such as network statistics based data selection and ontology supervised hierarchical reasoning are applied to these strategies. The experimental results based on an RDF dataset shows that the proposed strategies are potentially effective.