Knowledge Fusion in Semantic Grid

Xiaoqing Zheng, Zhaohui Wu, Huajun Chen
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引用次数: 1

Abstract

Just as the Web is shifting its focus from information and communication and emphasizes the need to reuse of knowledge as a huge distributed knowledge base, the semantic grid in which information and services are given well-defined meaning extends the current grid to enable software agents, users, and programs to work in cooperation. How to process the different sources of information, in particular, to combine a variety of sources of knowledge to assist users with effective reasoning or collaborative problem-solving, is one of great barrier in realizing the above vision. Against this background, we propose a knowledge fusion and integration approach based on statistical decision theory and Bayesian analysis for the semantic grid
语义网格中的知识融合
正如Web正在将其焦点从信息和通信转移到强调需要将知识重用为一个巨大的分布式知识库一样,语义网格(其中信息和服务被赋予定义良好的含义)扩展了当前的网格,使软件代理、用户和程序能够协同工作。如何处理不同来源的信息,特别是如何将多种知识来源结合起来,帮助用户进行有效的推理或协同解决问题,是实现上述愿景的巨大障碍之一。在此背景下,我们提出了一种基于统计决策理论和贝叶斯分析的语义网格知识融合与集成方法
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