摘要翻译：
本文提出了一种独立数据源相干积分的溯因方法。这个想法是计算一个数据事实列表，这些数据事实应该插入到合并数据库中，或者从合并数据库中收回，以便恢复其一致性。这种方法是由一个名为Asystem的溯因求解器实现的，它将SLDNFA解析应用于一个元理论，该元理论将不同的、可能相互矛盾的输入数据库联系起来。我们还对从不一致数据库中“恢复”一致数据的可能方法进行了纯模型理论分析，根据那些尽可能少地显示不一致信息的数据库模型。这使得我们能够根据理论的“首选”（即，最一致的）模型来描述“恢复的数据库”。结果是一个基于溯因的应用程序，它相对于相应的基于模型的优先语义是健全和完整的，而且--据我们所知--比任何其他数据库一致集成的实现都更有表现力（因此更通用）。
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英文标题：
《Coherent Integration of Databases by Abductive Logic Programming》
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作者：
O. Arieli, M. Bruynooghe, M. Denecker, B. Van Nuffelen
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  We introduce an abductive method for a coherent integration of independent data-sources. The idea is to compute a list of data-facts that should be inserted to the amalgamated database or retracted from it in order to restore its consistency. This method is implemented by an abductive solver, called Asystem, that applies SLDNFA-resolution on a meta-theory that relates different, possibly contradicting, input databases. We also give a pure model-theoretic analysis of the possible ways to `recover' consistent data from an inconsistent database in terms of those models of the database that exhibit as minimal inconsistent information as reasonably possible. This allows us to characterize the `recovered databases' in terms of the `preferred' (i.e., most consistent) models of the theory. The outcome is an abductive-based application that is sound and complete with respect to a corresponding model-based, preferential semantics, and -- to the best of our knowledge -- is more expressive (thus more general) than any other implementation of coherent integration of databases.
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PDF链接：
https://arxiv.org/pdf/1107.0030