Abstract Data tables provide the standard means of representation of qualitative or quantitative information about objects of interest. They also form a starting point for the task of information processing: an operation of passing from raw data or information to semantically processed knowledge. The fundamental issue here is the question about the meaning of data: What do entries in the data table actually tell us about objects? It entails another question: How should the meaning be further processed? The primary aim of the article is an attempt to answer these two questions. To this end we are going to employ conceptual scales from formal concept analysis, an important theory of data processing introduced by Rudolf Wille, and apply them to data tables so as to obtain multivalued information systems, which were introduced and developed within the conceptual framework of rough set theory by Zdzislaw Pawlak and Ewa Orlowska. Our main idea is to regard multivalued information systems as the semantics or meaning of the original tables. This idea allows us to describe and combine classical rough set theory, dominance-based rough set approach, and formal concept analysis within the single framework of multivalued information systems, which is rich enough to cope with a number of semantical nuances that may occur during the process of data analysis.

中文翻译：

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数据意义与知识发现：信息系统的语义方面

摘要 数据表提供了表示感兴趣对象的定性或定量信息的标准方法。它们还构成了信息处理任务的起点：从原始数据或信息传递到语义处理知识的操作。这里的基本问题是关于数据含义的问题：数据表中的条目实际上告诉我们关于对象的什么？这就引出了另一个问题：意义应该如何进一步处理？本文的主要目的是试图回答这两个问题。为此，我们将采用形式概念分析中的概念量表，这是 Rudolf Wille 引入的数据处理的重要理论，并将它们应用于数据表以获得多值信息系统，Zdzislaw Pawlak 和 Ewa Orlowska 在粗糙集理论的概念框架内引入和发展。我们的主要思想是将多值信息系统视为原始表的语义或含义。这个想法使我们能够在多值信息系统的单一框架内描述和结合经典的粗糙集理论、基于支配性的粗糙集方法和形式概念分析，该框架足够丰富，可以应对在数据传输过程中可能出现的许多语义细微差别。数据分析的过程。