A complex system integrates multiple sub‐systems and contains both knowledge in each sub‐system and in their connections. This paper aims to present the relational concept analysis as a method to extract “connection” knowledge as much as the sub‐system contained knowledge. A use case from neurology to validate the approach is introduced. A brain is a system that can be studied through different clinical examinations, therefore each clinical examination can be considered as a sub‐system.
To make knowledge-supported decisions, industrial actors often need to examine available data for suggestive patterns. As industrial data are typically unlabeled and involve multiple object types, unsupervised multi-relational (MR) data mining methods are particularly suitable for the task. Current MR association miners merely produce singleton-conclusions rules hence might miss multi-way dependencies. Our novel MR miner builds upon a relational extension of concept analysis to extract general associations. While successfully dealing with circularity in data, it avoids producing cyclic rules by limiting the description depth of relational concepts. Our rules' relevance was validated by an application to aluminum die casting.
Interoperability is a major stake for industry, and in general for all the systems, of any dimension, that need to share contents in every shape. It provides that the exchanges between different parts of different entities perform in a perfect way. Various problems could arise and let the interoperation difficult or impossible. One of those problems could be the presence of implicit knowledge in the systems models. This kind of problems can be faced through knowledge formalisation strategies.The Formal Concept Analysis (FCA) is a mathematical tool to represent the information in a structured and complete way. In this scientific work, we present an extension of the FCA, the Relational Concept Analysis, to reveal tacit knowledge hidden in multi contexts systems.
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