This paper presents a structured review of explainable stream reasoning with Graph Neural Networks (GNNs) in settings where graph structures and background knowledge evolve over time. Although prior work has advanced GNN modeling, temporal reasoning, and Knowledge Graph (KG) integration, the literature remains fragmented regarding how explanations should be generated, semantically grounded, and evaluated in knowledge-enriched graph streams. Existing studies provide limited guidance on how explanations should align with ontologies, preserve relational coherence, and remain stable under temporal evolution. As a review article, this paper provides a taxonomy-driven synthesis of GNN explanation methods, semantic integration patterns, and stream-oriented constraints rather than a new benchmark or deployment system. It analyzes how established explanation families apply to evolving, knowledge-enriched graphs and systematizes KG integration strategies for reasoning and explanation, including embedding-based, message-passing, neuro-symbolic, and KG-assisted approaches. The review also presents an implementation-oriented architectural roadmap illustrating how semantic constraints can be incorporated into temporal message passing. Building on this synthesis, the paper proposes knowledge-aware evaluation dimensions, including semantic fidelity, relational coherence, temporal semantic stability, and human/domain-centered alignment. Where possible, these dimensions are accompanied by illustrative formal metric definitions, while their standardization remains open. A bounded empirical feasibility illustration on temporally ordered healthcare graph data demonstrates the computability of selected dimensions. Representative use cases in healthcare, security, social media, and autonomous systems further illustrate how the proposed perspective can guide future research on trustworthy and semantically grounded GNN-based stream reasoning.

The emergence of dynamic, high-volume data streams demands advanced reasoning frameworks to capture complex spatio-temporal relationships that are essential for enabling contextual understanding. However, current approaches often lack scalable and adaptable semantic representations in dynamic and spatio-temporal scenarios. To answer this need, we introduce a novel Spatio-Temporal Knowledge approach based on Graph Neural Networks (STKGNN) for activity recognition. This framework performs graph-based reasoning over semantically enriched Spatio-Temporal Knowledge Graphs (STKGs) constructed from open-source video datasets. By leveraging these custom STKGs, we propose three advanced Graph Neural Network (GNN) based architectures to recognize various activities. Accordingly, we establish a comprehensive approach for spatio-temporal reasoning that adapts to diverse Knowledge Graph structures by addressing adaptability, scalability, and temporal complexities. This framework enhances activity recognition and provides a foundation for wider dynamic or real-time applications in different domains including healthcare, autonomous systems, video surveillance, and various other fields.

Real-time video streams present unique challenges for continual learning systems, demanding models that can incrementally update representations, preserve past knowledge, and reason over complex semantic relationships without sacrificing efficiency. In this paper, we introduce CAST-GNN, the first unified Graph Neural Network architecture expressly designed for continual adaptation on streaming Spatio-Temporal Knowledge Graphs (STKGs) derived from open-source video benchmarks. CAST-GNN integrates dynamic temporal embedding layers, adaptive self-attention, episodic graph pattern memory, and a novel hybrid selective replay buffer with Fisher-based regularization and knowledge distillation to mitigate catastrophic forgetting. Through comprehensive experiments on four diverse STKG benchmarks (UCF-101, HMDB-51, Kinetics-400, and SomethingSomething), our model achieves 96-97% accuracy, between 0.13-0.31 % forgetting, by consistently outperforming re-implemented continual-learning baselines under identical conditions. Ablation studies confirm the critical synergy between temporal embeddings and adaptive attention. We further demonstrate XAI-driven interpretability by aligning global distributional shifts with local node-level attributions. CAST-GNN not only advances robust semantic reasoning and knowledge retention but also provides a scalable, explainable framework applicable to a wide array of real-world streaming scenarios.

Abstract The rapid expansion of decentralized, complex streaming data across diverse domains such as the Internet of Things, healthcare, and smart cities presents significant technical challenges. These challenges–data heterogeneity (integration of diverse formats and sources), dynamicity (handling real-time data evolution), and high-volume throughput (efficient processing of large, rapidly arriving data)–are the central focus of this study and are examined in depth. To address these critical issues necessitates advanced methods capable of seamless integration, effective real-time reasoning, and continuous learning from heterogeneous streaming data, thus enhancing real-time decision-making capabilities. This study provides an extensive review of existing research at the intersection of streaming data, machine learning, and reasoning. The literature review categorizes Stream Reasoning approaches into three key groups: Streaming Machine Learning, Streaming Linked Data, and Streaming Knowledge Graphs. Each category is critically examined in terms of strengths, limitations, ongoing challenges, and future opportunities identified in recent studies. Additionally, potential integrative solutions that leverage Knowledge Graph structures and advanced Stream Reasoning techniques are highlighted, illustrating how state-of-the-art modeling methods can effectively address Stream Reasoning related challenges. The analysis concludes that combining Knowledge Graph and Machine Learning approaches significantly enhances the capability to manage and overcome complex Stream Reasoning challenges.