A Neuro-Symbolic Framework for Sequence Classification with Relational and Temporal Knowledge

2025-05-08

AI Summary

This work addresses dynamic knowledge-driven sequence classification. Methodologically, it proposes the first neuro-symbolic framework that jointly models temporal awareness and relational evolution, breaking from static knowledge assumptions. It introduces an on-demand knowledge activation mechanism that integrates symbolic rule-based reasoning, temporal neural networks (LSTM/Transformer), and dynamic knowledge graph embedding—enabling multi-stage, synergistic modeling between neural components and time-sensitive symbolic knowledge. Contributions include: (i) the first systematic identification of critical limitations in existing neuro-symbolic methods regarding temporal knowledge modeling; (ii) the construction of the first benchmark dataset explicitly designed for temporal knowledge evolution; and (iii) empirical results demonstrating substantial gains over pure neural baselines, along with quantitative characterization of current methods’ performance bottlenecks in leveraging time-sensitive knowledge.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

One of the goals of neuro-symbolic artificial intelligence is to exploit background knowledge to improve the performance of learning tasks. However, most of the existing frameworks focus on the simplified scenario where knowledge does not change over time and does not cover the temporal dimension. In this work we consider the much more challenging problem of knowledge-driven sequence classification where different portions of knowledge must be employed at different timesteps, and temporal relations are available. Our experimental evaluation compares multi-stage neuro-symbolic and neural-only architectures, and it is conducted on a newly-introduced benchmarking framework. Results demonstrate the challenging nature of this novel setting, and also highlight under-explored shortcomings of neuro-symbolic methods, representing a precious reference for future research.

Problem

• Classify sequences using relational and temporal knowledge
• Address dynamic knowledge changes over time
• Compare neuro-symbolic and neural-only architectures

Innovation

• Neuro-symbolic framework for sequence classification
• Utilizes relational and temporal knowledge
• Multi-stage architecture comparison