Neural Symbolic Time Table Reasoning (NSTI)

Abstract

This paper introduces Neural Symbolic Time Table Reasoning (NSTI), a novel framework for understanding complex time series data by integrating the strengths of neural networks and symbolic reasoning. The core claim is that combining neural networks with symbolic time table inference provides a more interpretable and robust understanding of temporal data compared to traditional time series models. NSTI employs an encoder network to extract feature vectors from the time series data, which are then fed into a symbolic reasoning engine based on time tables. This engine utilizes knowledge graphs and rules to infer events, relationships, and patterns within the time series. A trainable bridge module facilitates interaction between the neural network and the symbolic engine, learning to map neural features to symbolic representations and guiding the reasoning process. This approach represents a significant advance over standalone neural time series prediction or rule-based symbolic time table systems, offering a synergistic combination for enhanced understanding and reasoning capabilities. The proposed method aims to address the limitations of existing approaches by providing both the fine-grained feature extraction capabilities of neural networks and the logical inference and explainability of symbolic systems.