Many disciplines in computer science combine similarity-based and logic-based reasoning. The problem is that the disciplines combine these independently of each other. For example in Case-Based Reasoning (CBR) (Aamodt and Plaza, AI Commun. 7(1):39–59, 1994; Bergmann et al., Künstl. Intell. 23(1):5–11, 2009; Bergmann, Experience Management: Foundation, Development, Methodology and Internet-based Applications, LNAI, vol. 2432, Springer, Berlin, 2002), the combination is applied in a sequential manner and not systematically as follows: a set of solutions is retrieved from a case-base using a similarity measure and then deductive reasoning is applied to adapt the retrieved solutions to a query. The aim of this dissertation (Mougouie, Ph.D. thesis, Trier University, Germany, 2009) is to integrate similarity-based and deductive reasoning in a unified manner within the context of Knowledge Management (KM).
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机译：计算机科学的许多学科都将基于相似性和基于逻辑的推理相结合。问题在于这些学科彼此独立地结合在一起。例如，基于案例的推理（CBR）（Aamodt and Plaza，AI Commun。7（1）：39–59，1994; Bergmann等，Künstl。Intell。23（1）：5-11，2009; Bergmann） ，《经验管理：基础，开发，方法论和基于互联网的应用》，LNAI，第2432卷，施普林格，柏林，2002年），这种组合是以顺序的方式应用的，而不是系统地按如下方式应用：使用相似性度量的案例库，然后应用演绎推理，使检索到的解决方案适应查询。本论文的目的（Mougouie博士论文，德国特里尔大学，2009年）是在知识管理（KM）的背景下以统一的方式整合基于相似度和演绎推理的方法。
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