Automatic Detection and Resolution of Lexical Ambiguity in Process Models
Automatic Detection and Resolution of Lexical Ambiguity in Process Models
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DOI：
10.1109/tse.2015.2396895
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发表时间：
2015-06
影响因子：
7.4
通讯作者：
Fabian Pittke;H. Leopold;J. Mendling
中科院分区：
文献类型：
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作者：
Fabian Pittke;H. Leopold;J. Mendling
系统相关的工程任务通常使用过程模型来执行。在这种情况下，这些模型不包含结构或术语上的不一致是至关重要的。为此，提出了几种自动分析技术来支持质量保证。虽然可以以自动化的方式检查控制流的正式属性，但缺乏处理文本质量的技术。更具体地说，目前还没有可用于处理同音异义词和同义词引起的词汇歧义问题的技术。在本文中，我们解决了这一研究缺口，并提出了一种检测和解决过程模型中词汇歧义的技术。我们使用来自不同规模、领域和标准化程度的实践的三个过程模型集合来评估该技术。评价结果表明，该技术显著降低了词汇歧义的水平，并提出了有意义的候选词来解决歧义。
System-related engineering tasks are often conducted using process models. In this context, it is essential that these models do not contain structural or terminological inconsistencies. To this end, several automatic analysis techniques have been proposed to support quality assurance. While formal properties of control flow can be checked in an automated fashion, there is a lack of techniques addressing textual quality. More specifically, there is currently no technique available for handling the issue of lexical ambiguity caused by homonyms and synonyms. In this paper, we address this research gap and propose a technique that detects and resolves lexical ambiguities in process models. We evaluate the technique using three process model collections from practice varying in size, domain, and degree of standardization. The evaluation demonstrates that the technique significantly reduces the level of lexical ambiguity and that meaningful candidates are proposed for resolving ambiguity.