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Use of a fuzzy machine leaming technique in the knowledge acquisition process

机译：在知识获取过程中使用模糊机器学习技术

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摘要

Acquiring the knowledge to support an expert system is one of the key activities in knowledge engineering. Knowledge acquisition (KA) is closely related to research in the machine learning field. Any machine learning acquires some knowledge, but not enough knowledge for building expert systems. The aim of this article is to present a new approach to machine learning which helps to acquire knowledge When building expert systems. This technique will acquire the more general knowledge that should be used for extending, updating and improving an incomplete and partially incorrect knowledge base (KB). The main claim of our approach is that the system will start with poor knowledge, provided by the expert or the organization to which he belongs. A machine learning technique will evolve it to an incomplete KB, which may be used for further interactions with the expert, that will incrementally extend and improve it until obtaining a complete KB (i.e., with complete inferential capabilities).
机译：获取知识以支持专家系统是知识工程中的关键活动之一。知识获取（KA）与机器学习领域的研究紧密相关。任何机器学习都获得一些知识，但不足以构建专家系统。本文的目的是提出一种新的机器学习方法，该方法有助于在构建专家系统时获取知识。该技术将获得应用于扩展，更新和改进不完整且部分不正确的知识库（KB）的更一般的知识。我们方法的主要主张是，系统将以专家或他所属组织提供的知识不足为起点。机器学习技术会将其演变为不完整的知识库，可用于与专家进行进一步的交互，这将逐步扩展和改进它，直到获得完整的知识库（即具有完整的推理功能）。

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