AI systems often need to deal with inconsistent information. For this reason, since the early 2000s, some AI researchers have developed ways to measure the amount of inconsistency in a knowledge base. By now there is a substantial amount of research about various aspects of inconsistency measuring. The problem is that most of this work applies only to knowledge bases formulated as sets of formulas in propositional logic. Hence this work is not really applicable to the way that information is actually stored. The purpose of this paper is to extend inconsistency measuring to real world information. We first define the concept of general information space which encompasses various types of databases and scenarios in AI systems. Then, we show how to transform any general information space to an inconsistency equivalent propositional knowledge base, and finally apply propositional inconsistency measures to find the inconsistency of the general information space. Our method allows for the direct comparison of the inconsistency of different information spaces, even though the data is presented in different ways. We demonstrate the transformation on four general information spaces: a relational database, a graph database, a spatio-temporal database, and a Blocks world scenario, where we apply several inconsistency measures after performing the transformation. Then we review so-called rationality postulates that have been developed for propositional knowledge bases as a way to judge the intuitive properties of these measures. We show that although general information spaces may be nonmonotonic, there is a way to transform the postulates so they can be applied to general information spaces and we show which of the measures satisfy which of the postulates. Finally, we discuss the complexity of inconsistency measures for general information spaces.

人工智能系统通常需要处理不一致的信息。因此，自2000年代初以来，一些AI研究人员已经开发出测量知识库中不一致程度的方法。到目前为止，关于不一致测量的各个方面已有大量研究。问题在于，大部分工作仅适用于命题逻辑中公式化为公式集的知识库。因此，这项工作实际上不适用于实际存储信息的方式。本文的目的是将不一致性测量扩展到现实世界中的信息。我们首先定义通用信息空间的概念，其中包含AI系统中各种类型的数据库和方案。然后，我们展示如何将任何一般信息空间转换为不一致等价命题知识库，最后运用命题不一致措施发现一般信息空间的矛盾。即使数据以不同的方式表示，我们的方法也可以直接比较不同信息空间的不一致性。我们演示了在四个常规信息空间上的转换：关系数据库，图形数据库，时空数据库和Blocks世界场景，在执行转换之后，我们在其中应用了几种不一致的度量。然后，我们回顾为命题知识库开发的所谓合理性假设，作为判断这些度量的直观属性的一种方式。我们证明，尽管一般信息空间可能是非单调的，有一种方法可以转换这些假定，以便可以将它们应用于一般信息空间，并且我们将说明哪些度量满足那些假定。最后，我们讨论了通用信息空间不一致度量的复杂性。