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Comparing Inconsistency Resolutions in Multi-Context Systems

机译:比较多上下文系统中的不一致解决方案

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Inconsistency in heterogeneous knowledge-integration systems with non-monotonic information exchange is a major concern as it renders systems useless at its occurrence. For the knowledge-integration framework of Multi-Context Systems, the problem of finding all possible resolutions to inconsistency has been addressed previously and some basic steps have been proposed to find most preferred resolutions. Here, we refine the techniques of finding preferred resolutions of inconsistency in two directions. First, we extend available qualitative methods using domain knowledge on the intention and category of information exchange to minimize the number of categories that are affected by a resolution. Second, we present a quantitative inconsistency measure for inconsistency resolutions, being suitable for scenarios where no further domain knowledge is available.
机译:带有非单调信息交换的异构知识集成系统中的不一致是一个主要问题,因为它使系统在出现时变得无用。对于多上下文系统的知识集成框架,以前已经解决了找到所有可能的不一致解决方案的问题,并且已经提出了一些基本步骤来找到最优选的解决方案。在这里,我们改进了在两个方向上查找首选不一致解决方案的技术。首先,我们使用关于信息交换的意图和类别的领域知识扩展了可用的定性方法,以最大程度地减少受决议影响的类别数量。其次,我们提出了针对不一致解决方案的定量不一致度量,适用于没有更多领域知识可用的情况。

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