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首页> 外文期刊>International Journal of Innovative Computing Information and Control >CONTEXT-BASED KNOWLEDGE SUPPORT FOR PROBLEM-SOLVING BY RULE-INFERENCE AND CASE-BASED REASONING
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CONTEXT-BASED KNOWLEDGE SUPPORT FOR PROBLEM-SOLVING BY RULE-INFERENCE AND CASE-BASED REASONING

机译：基于规则推理和基于案例的推理解决问题的基于上下文的知识支持

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摘要

Problem-solving is an important process that enables corporations to create competitive business advantages. Traditionally, case-based reasoning techniques have been widely used to help workers solve problems. However, conventional approaches focus on identifying similar problems without exploring relevant context of problem situations. Situation features are generally occurred according to the context characteristics of problems. Moreover, situation features collected are usually partial or incomplete. Workers need to use knowledge inferred from relevant context information and previous problem-solving experience to clarify the causes and take appropriate action effectively. In this paper, we propose to use rule inference to infer possible situation features based on context information. Association rule mining is used to discover context-based inference rules from historical problem-solving logs. The discovered patterns identify frequent associations between context information and situation features, and therefore, can be used to infer more situation features. By considering the inferred situation features, case-based reasoning can then be employed to identify similar situations effectively. Moreover, we employ information retrieval techniques to extract context-based situation profiles to model workers' information needs when handling problem situations in certain context. Effective knowledge support can thus be facilitated by providing workers with situation-relevant information based on the profiles. We develop a prototype system to demonstrate the effectiveness of providing context-based relevant information and decision-making knowledge to help workers solve problems.
机译：解决问题是使公司能够创造竞争性业务优势的重要过程。传统上，基于案例的推理技术已广泛用于帮助工人解决问题。然而，常规方法集中于识别相似的问题而不探索问题状况的相关背景。情境特征通常是根据问题的上下文特征而发生的。此外，收集的情况特征通常是部分或不完整的。工人需要利用从相关背景信息中得出的知识和以前的解决问题的经验来弄清原因，并采取有效的措施。在本文中，我们建议使用规则推理基于上下文信息来推理可能的情境特征。关联规则挖掘用于从历史问题解决日志中发现基于上下文的推理规则。发现的模式标识上下文信息和情境特征之间的频繁关联，因此可以用于推断更多的情境特征。通过考虑推断的情况特征，然后可以采用基于案例的推理来有效地识别类似情况。此外，我们采用信息检索技术来提取基于上下文的情境概要文件，以在某些情况下处理问题情境时为工人的信息需求建模。因此，可以通过基于配置文件为工人提供与情况相关的信息来促进有效的知识支持。我们开发了一个原型系统，以演示提供基于上下文的相关信息和决策知识来帮助工人解决问题的有效性。

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