Process anomaly detection is an important application of process mining for identifying deviations from the normal behavior of a process. Neural network-based methods have recently been applied to this task, learning directly from event logs without requiring a predefined process model. However, since anomaly detection is a purely statistical task, these models fail to incorporate human domain knowledge. As a result, rare but conformant traces are often misclassified as anomalies due to their low frequency, which limits the effectiveness of the detection process. Recent developments in the field of neuro-symbolic AI have introduced Logic Tensor Networks (LTN) as a means to integrate symbolic knowledge into neural networks using real-valued logic. In this work, we propose a neuro-symbolic approach that integrates domain knowledge into neural anomaly detection using LTN and Declare constraints. Using autoencoder models as a foundation, we encode Declare constraints as soft logical guiderails within the learning process to distinguish between anomalous and rare but conformant behavior. Evaluations on synthetic and real-world datasets demonstrate that our approach improves F1 scores even when as few as 10 conformant traces exist, and that the choice of Declare constraint and by extension human domain knowledge significantly influences performance gains.

翻译：过程异常检测是流程挖掘中的重要应用，旨在识别与正常流程行为的偏差。基于神经网络的方法近期已被应用于该任务，可直接从事件日志中学习而无需预定义过程模型。然而，由于异常检测本质为统计性任务，这些模型未能融入人类领域知识。因此，低频但合规的轨迹常因其出现频率低而被误判为异常，限制了检测过程的有效性。神经符号人工智能领域的最新进展引入了逻辑张量网络（LTN），可利用实值逻辑将符号知识整合至神经网络中。本研究提出一种神经符号方法，通过LTN与Declare约束将领域知识融入神经异常检测。以自编码器模型为基础，我们将Declare约束编码为学习过程中的软逻辑引导规则，用于区分异常行为与罕见但合规的行为。在合成数据集与真实数据集上的评估表明，即使仅存在10条合规轨迹，该方法仍能提升F1分数；且Declare约束的选择（进而延伸至人类领域知识）对性能提升具有显著影响。