Process models play an important role in various system-related management activities including requirements elicitation, domain analysis, software design as well as documentation of databases, business processes, and software systems. However, it has been found that the correct and meaningful usage of process models appears to be achallenge in practical settings requiring the usage of automatic model analysis techniques. Up until now, such automatic quality assurance is mainly available for checking formal properties of process models. Forinstance, there is arich set of analysis techniques to check control-flow-related properties of process models, such as soundness. There are only afew techniques available for checking guidelines on text labels with regard to terminological ambiguity.Moreover, the terminology problem is even more serious in process models since the process model givesa na bstract viewo ft he business process and provides only limited context to detect and resolvea mbiguity issues. It is thus the goal of [PLM15] to address the need for automatic techniques as well as to define detection and resolution technique for textual ambiguities that improve the terminological quality of aprocess models and repositories thereof.Fort hat purpose, our approach addresses three important issues, i.e. the manual effort, the missing focus on process model text fragments, and the focus on single models. First, the required manual effort,refers to the extensive amount of manual work that is required to detect and resolvea mbiguities in process models. The human effort can be tremendous since organizations tend to maintain several hundreds or even thousands of process models. Second, the missing focus on process model text fragments refers to the fact that manyapproaches of ambiguity detection and resolution are tailored to deal with sentences and phrases taken from ag rammatically and syntactically correct natural language text. However, the elements of process models contain only short textual fragments that do not exhibit ac omplete or ac orrect sentence structure impeding the direct application of such approaches. Third, the focus on single models relates to the observation that available techniques consider only single models or smaller units thereof. Hence, these techniques address ambiguities within as ingle document or process model. However, since we assume ar epository of several process models, the correction of ambiguities on document levelmight introduce an inconsistencyinanother document or model.Our approach introduces the notion of semantic vectors that represents all the possible meanings of at erm in the context of ap rocess model. As emantic vector interprets the
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