6.1. Summary
In this work, a novel knowledge support system has developed for problem-solving on a production-line. The description of situation/action and collected attributes assist case-based reasoning to identify similar situations/actions. Information Retrieval (Automatic Indexing) techniques are then applied to discover the key terms of a situation/action. The terms form situation/action profiles that model the information needed of workers to handle a problem. Association rule mining and sequential pattern mining are used to discover de-cision-making and dependency knowledge patterns, respectively. The situation/action pro-files and discovered knowledge patterns are used to construct a knowledge support network, which forms the basis of support for solving problems on a production line. The proposed system provides integrated browsing and suggestions about problem-solving knowledge.
Relevant documents are recommended to help users identify the root cause of a problem situation and the appropriate action to take. Workers can also use the knowledge support network to navigate the knowledge patterns and obtain decision-making and dependency knowledge. The proposed knowledge support network, enhanced with suggestions about problem-solving knowledge, provides workers with the necessary knowledge to effectively solve problems. A prototype system is implemented using a data set from a company’s intranet portal, in which the log file contains a log of information for handling problems on the company’s production line.
Moreover, based on context modeling, context-based inference rules are discovered to infer more relevant situation features. The description of situation, collected attributes, and inferred situation features assist case-based reasoning in situation identification. Information Retrieval (Automatic Indexing) techniques are then applied to discover the key terms of a situation. The terms form context-based situation profiles that model the information needs of workers to handle a problem. The system uses the context-based situation profile to gather existing and new relevant knowledge documents for situation in certain context.
Furthermore, the system continually infers situation features to form context-based knowledge patterns which provide workers with relevant inferred knowledge, as well as decision-making and dependency knowledge.
6.2. Future works
In our future work, we will apply our proposed method to different data resources or other application domains. This work has focused on solving problems in stages in different situations with different actions. The stages need to be predefined by experts, which is the case with the company’s production line. For other application domains, the stages may not be easy to define. Moreover, the stages investigated in this work are limited to a sequential order, rather than a combination of AND/OR parallelisms and sequences, as in a workflow system. Accordingly, a more flexible approach to address these issues would be worthy of further study.
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