Bridging dynamics and semantics: A unified perspective on explainable Graph Neural Networks based stream reasoning
| Publicatietype: | Artikel |
| Citatie: | TATAROGLUOZBULAK2026116662 |
| Tijdschrift: | Knowledge-Based Systems |
| Deel: | 351 |
| Jaar: | 2026 |
| Pagina's: | 116662 |
| ISSN: | 0950-7051 |
| URL: | https://www.sciencedirect.com/... |
| DOI: | https://doi.org/10.1016/j.knosys.2026.116662 |
| Samenvatting: | 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. |
| Trefwoorden: | explainable artificial intelligence, Graph Neural Networks, Knowledge Graphs, stream reasoning |
| Auteurs | |
| Toegevoegd door: | [] |
| Totaalscore: | 0 |
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