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arXiv · 2009.03420

A Hybrid Neuro-Symbolic Approach for Complex Event Processing

Marc Roig Vilamala·Harrison Taylor·Tianwei Xing·Luis Garcia·Mani Srivastava·Lance Kaplan·Alun Preece·Angelika Kimmig·Federico Cerutti
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Abstract

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.

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Marc Roig Vilamala, Harrison Taylor, Tianwei Xing, Luis Garcia, Mani Srivastava, Lance Kaplan, Alun Preece, Angelika Kimmig, Federico Cerutti. 2020-10-13. A Hybrid Neuro-Symbolic Approach for Complex Event Processing. https://arxiv.org/abs/2009.03420

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