This paper explores a novel approach to artificial intelligence by integrating the strengths of neural networks and symbolic reasoning. The core idea is to leverage the pattern recognition capabilities of neural networks for knowledge extraction and representation, coupled with the logical deduction and inference capabilities of symbolic reasoning systems. This hybrid architecture aims to construct AI systems that not only possess high accuracy but also offer enhanced interpretability and reliability. The system utilizes a neural network to process raw input data and generate a structured knowledge graph, which is then subjected to symbolic reasoning algorithms to derive conclusions and make decisions. The proposed method addresses the limitations of existing neural AI approaches that often lack explicit knowledge representation and reasoning mechanisms, leading to a lack of transparency and difficulty in debugging. This research contributes to the development of more robust and trustworthy AI systems by grounding AI decisions in a formally represented knowledge base. The evaluation framework will focus on assessing the system's accuracy, interpretability, and robustness across various reasoning tasks.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces Dynamic Topology Neural Networks (DTNNs), a novel approach to neural network design that leverages continuous topological evolution and feedback learning to autonomously construct and optimize network topologies. Unlike traditional static neural networks, DTNNs adapt their structure in real-time, responding to evolving input data and task requirements. The core mechanism involves a graph-based neural network framework where neurons and connections form a dynamic topology. Each neuron possesses a state vector, influencing its activation level, connection weights, and local environmental information. Topological evolution is governed by mechanisms, such as genetic algorithms or reinforcement learning, dynamically adjusting the network's structure by adding, removing, or modifying connections based on neuron states and network performance metrics. Simultaneously, feedback learning updates neuron activation levels through gradient descent or pulse feedback, driven by input signals and performance objectives. This architecture offers enhanced adaptability, robustness, and scalability, surpassing the limitations of static networks in handling unstructured and dynamic data. The key innovation lies in the dynamic control of topology and the deep learning of neuron states, providing a more flexible and powerful framework for complex data processing.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to Graph Neural Networks (GNNs) designed to effectively capture temporal dependencies within dynamic graph structures. Traditional GNNs often fail to adequately represent the evolving nature of relationships between nodes, hindering their performance in systems where the graph topology changes over time. Our proposed Temporal Graph Neural Networks (T-GNNs) address this limitation by incorporating a 'temporal strength' factor into node embeddings. This factor is dynamically adjusted based on the observed evolution of relationships within the graph, learned through a recurrent attention mechanism that specifically focuses on relational changes. The core innovation lies in the adaptive adjustment of relational strength, allowing the network to prioritize relevant temporal information. We demonstrate through a theoretical analysis and structural design that this approach significantly improves the ability of GNNs to model time-evolving graph data. The resulting T-GNN architecture offers a robust framework for analyzing and predicting behavior in dynamic systems represented as graphs.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of non-local graph neural networks to enhance semantic representation learning. Traditional graph neural networks primarily focus on node features, often struggling to capture the nuanced relationships within a graph. We propose a novel architecture that explicitly models the *meaning* of the graph, achieved through a mechanism that learns representations sensitive to the graph's structure and content. This approach aims to improve generalization and robustness compared to existing methods, particularly in scenarios with complex, high-dimensional graphs. The core of this work involves a modified attention mechanism integrated within the graph neural network to facilitate the propagation of information across the graph, thereby strengthening the representation of semantic connections. We present a series of experiments evaluating the performance of this architecture on various graph datasets, demonstrating significant improvements in semantic understanding and overall accuracy.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to Graph Neural Networks (GNNs) designed to effectively capture temporal dependencies within dynamic graph structures. Traditional GNNs often fail to adequately represent the evolving nature of relationships between nodes, hindering their performance in systems where the graph topology changes over time. Our proposed Temporal Graph Neural Networks (T-GNNs) address this limitation by incorporating a 'temporal strength' factor into node embeddings. This factor is dynamically adjusted based on the observed evolution of relationships within the graph, learned through a recurrent attention mechanism that specifically focuses on relational changes. The core innovation lies in the adaptive adjustment of relational strength, allowing the network to prioritize relevant temporal information. We demonstrate through a theoretical analysis and structural design that this approach significantly improves the ability of GNNs to model time-evolving graph data. The resulting T-GNN architecture offers a robust framework for analyzing and predicting behavior in dynamic systems represented as graphs.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations