Process models are a central element of modern business process management technology. When adopting such technology, organizations inevitably establish process model collections which, depending on the degree of adoption, can reach sizes of thousands of models. Process model matching techniques are intended to assist experts in the management of such large collections, e.g., in querying the collections and in comparing process models. Yet, as demonstrated in comparative evaluations, existing techniques struggle to achieve a high effectiveness on real-world datasets, limiting their practical applicability. This is partly due to these techniques being fully automated and relying on universal knowledge bases that insufficiently represent the domain semantics of model collections.

To increase effectiveness and to progress on the path to practical applicability, we pursue the idea of integrating expert feedback into the matching process, so as to continuously update the knowledge base and achieve a better domain adaptation. In particular, we present ADBOT, a matching technique that relies on expert feedback in terms of corrected matching results. Our contributions are twofold. First, we introduce different strategies to utilize expert feedback in the matching process and to improve its effectiveness. Second, we provide heuristics for guiding experts through a model collection intended to reduce the amount of collected feedback while still maximizing the gains of learning from it. Based on five separate real-world datasets we provide empirical evidence towards the feasibility of our matcher. In the experiments, ADBOT (i) achieves high f-measures of up to $.90$, (ii) improves the effectiveness of baseline matchers by up to 88%, (iii) yields high recall values due to the detection of correspondences that automated matchers fail to achieve, and (iv) still increases effectiveness when the feedback contains errors. We also discuss evidence that substantiates ADBOT’s individual components, amongst others demonstrating that the guidance heuristics can maximize effectiveness, while minimizing human effort.

流程模型是现代业务流程管理技术的核心元素。当采用这种技术时，组织不可避免地要建立过程模型集合，这取决于采用程度，可以达到数千个模型的大小。过程模型匹配技术旨在协助专家管理此类大型集合，例如，查询集合和比较过程模型。然而，如比较评估所示，现有技术难以在现实世界的数据集上实现高效，从而限制了它们的实际适用性。部分原因是这些技术是完全自动化的，并且依赖于不能充分表示模型集合的域语义的通用知识库。

为了提高有效性并在实际应用的道路上取得进展，我们追求将专家反馈整合到匹配过程中的想法，以便不断更新知识库并实现更好的领域适应性。特别是，我们提出了ADBOT，这是一种匹配技术，它依赖于专家的反馈来纠正匹配结果。我们的贡献是双重的。首先，我们介绍不同的策略，以在匹配过程中利用专家反馈并提高其有效性。其次，我们提供启发式方法，以指导专家进行模型收集，以减少收集的反馈数量，同时仍能最大程度地从中学习。基于五个独立的现实世界数据集，我们为匹配器的可行性提供了经验证据。在实验中$。90$，（ii）将基线匹配器的有效性提高多达88％，（iii）由于检测到自动匹配器未能实现的对应关系而产生较高的召回值，并且（iv）当反馈包含错误时仍会提高有效性。我们还将讨论可证明ADBOT各个组成部分的证据，其中包括证明指导启发法可以在最大程度地减少人力的同时，最大程度地提高有效性。