论文标题
复杂事件处理的混合神经符号方法
A Hybrid Neuro-Symbolic Approach for Complex Event Processing
论文作者
论文摘要
训练模型来检测形成感兴趣情况的相互关联事件的模式可能是一个复杂的问题：这种情况往往很少见，并且只有稀疏的数据可用。我们提出了基于事件计算的混合神经符号结构，该结构可以执行复杂的事件处理（CEP）。它利用神经网络来解释表达复杂事件模式的输入和逻辑规则。与纯神经网络方法相比，我们的方法能够使用标记数据少得多的培训，并且即使以端到端的方式进行培训，也可以学会对单个事件进行分类。我们证明了这种方法与基于Urban Sounds 8K的数据集上的纯神经网络方法进行了比较。
Training a model to detect patterns of interrelated events that form situations of interest can be a complex problem: such situations tend to be uncommon, and only sparse data is available. We propose a hybrid neuro-symbolic architecture based on Event Calculus that can perform Complex Event Processing (CEP). It leverages both a neural network to interpret inputs and logical rules that express the pattern of the complex event. Our approach is capable of training with much fewer labelled data than a pure neural network approach, and to learn to classify individual events even when training in an end-to-end manner. We demonstrate this comparing our approach against a pure neural network approach on a dataset based on Urban Sounds 8K.
