The Probabilistic Resource Space Model Theory and Experiment

نویسندگان

  • Hai Zhuge
  • Yunpeng Xing
چکیده

The development of World Wide Web requires a semantic data model to effectively manage the contents of its heterogeneous resources. This challenges traditional data models, which are mainly for managing structured resources or relatively simple data objects. Classification is one of the most basic methods to organize and manage resources in the real world. The Resource Space Model RSM is a semantic data model for managing the contents of various resources by normalizing the classification semantics. However, uncertainty in our daily life makes it difficult to correctly classify and use resources. Previous probabilistic data models are mainly on the existence of resources or the values of resources’ attributes. This paper firstly introduces the basic concepts of RSM by comparing with the relational data model, and then proposes the Probabilistic Resource Space Model P-RSM to manage uncertainty in classification semantics. The uncertain classification semantics of different granularities is proposed to specify and manage resources. Relevant normal forms, operations and integrity constraints are also proposed for dealing with uncertain classification semantics. Experiments show the effectiveness of the proposed P-RSM. Index Term—Data Model, Classification, Web resource management, Probability.

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تاریخ انتشار 2008