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

  • Yi Zeng
  • Yan Wang
  • Zhisheng Huang
  • Ning Zhong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5820)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yi Zeng
    • 1
  • Yan Wang
    • 1
  • Zhisheng Huang
    • 2
    • 3
  • Ning Zhong
    • 1
    • 4
  1. 1.International WIC InstituteBeijing University of TechnologyBeijingP.R. China
  2. 2.Department of Artificial IntelligenceVrije University AmsterdamAmsterdamThe Netherlands
  3. 3.School of Computer Science and EngineeringJiangsu University of Science and TechnologyJiangsuP.R. China
  4. 4.Department of Life Science and InformaticsMaebashi Institute of TechnologyMaebashiJapan

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