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Bioinformatic Workflow Extraction from Scientific Texts based on Word Sense Disambiguation

机译:基于词义消歧的科学文本生物信息工作流提取

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This paper introduces a method for automatic workflow extraction from texts using Process-Oriented Case-Based Reasoning (POCBR). While the current workflow management systems implement mostly different complicated graphical tasks based on advanced distributed solutions (e.g., cloud computing and grid computation), workflow knowledge acquisition from texts using case-based reasoning represents more expressive and semantic case representations. We propose in this context, an ontology-based workflow extraction framework to acquire processual knowledge from texts. Our methodology extends the classic NLP techniques to extract and disambiguate complex tasks and relations in texts. Using a graph-based representation of workflows and a domain ontology, our extraction process uses a context-aware approach to recognize workflow components in texts: data and control flows. We applied our framework in a technical domain in bioinformatics: i.e., phylogenetic analyses. An evaluation based on workflow semantic similarities in a gold standard proves that our approach provides promising results in the process extraction domain. Both data and implementation of our framework are available in: http://labo.bioinfo.uqam.ca/tgowler.
机译:本文介绍了一种使用面向过程的基于案例的推理(POCBR)从文本中自动提取工作流的方法。虽然当前的工作流管理系统基于高级分布式解决方案(例如,云计算和网格计算)来执行大多数不同的复杂图形任务,但是使用基于案例的推理从文本中获取工作流知识表示的是更具表达性和语义的案例表示形式。在这种情况下,我们提出了一种基于本体的工作流提取框架,以从文本中获取过程知识。我们的方法扩展了经典的NLP技术,以提取和消除文本中复杂的任务和关系的歧义。通过使用基于图形的工作流和域本体表示,我们的提取过程使用上下文感知方法来识别文本中的工作流组件:数据和控制流。我们在生物信息学的技术领域中应用了我们的框架:即系统发育分析。在黄金标准中基于工作流语义相似性进行的评估证明,我们的方法在过程提取领域提供了可喜的结果。我们的框架的数据和实现均可在以下网址中找到: n http://labo.bioinfo.uqam.ca/tgowler

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