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Knowledge discovery from post-project reviews

机译：项目后审查中的知识发现

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摘要

Many construction companies conduct reviews on project completion to enhance learning and to fulfil quality management procedures. Often these reports are filed away never to be seen again. This means that potentially important knowledge that may assist other project teams is not exploited. In order to ascertain whether useful knowledge can be gleaned from such reports. Knowledge Discovery from Text (KDT) and text mining (TM) are applied. Text mining avoids the need for a manual search through a vast number of reports, potentially of different formats and foci, to seek trends that may be useful for current and future projects. Pilot tests were used to analyse 48 post-project review reports. The reports were first reviewed manually to identify key themes. They were then analysed using text mining software to investigate whether text mining could identify trends and uncover useful knowledge from the reports. Pilot tests succeeded in finding common occurrences across different projects that were previously unknown. Text mining could provide a potential solution and would aid project teams to learn from previous projects. However, a lot of work is currently required before the text mining tests are conducted and the results need to be examined carefully by those with domain knowledge to validate the results obtained.
机译：许多建筑公司对项目完成情况进行审查，以加强学习并完成质量管理程序。这些报告经常被归档，再也不会被看到。这意味着不会利用可能有助于其他项目团队的重要知识。为了确定是否可以从此类报告中收集有用的知识。应用了来自文本的知识发现（KDT）和文本挖掘（TM）。文本挖掘避免了通过大量可能具有不同格式和焦点的报告手动搜索的需要，以寻找可能对当前和未来项目有用的趋势。试点测试用于分析48个项目后审查报告。首先对报告进行手动审核，以识别关键主题。然后使用文本挖掘软件对其进行分析，以调查文本挖掘是否可以识别趋势并从报告中发现有用的知识。试点测试成功地发现了以前未知的不同项目之间的共同点。文本挖掘可以提供潜在的解决方案，并有助于项目团队从以前的项目中学习。但是，在进行文本挖掘测试之前，当前需要进行大量工作，并且具有相关领域知识的人员必须仔细检查结果以验证所获得的结果。

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