Intelligent decision support for tunnel fire incidents: integrating dynamic knowledge graph with large language models

Knowledge graph–driven laboratory intelligence for anemia diagnosis using hematological profiles

MicrobExpert: A mixture-of-experts framework for cross-domain knowledge mining of food–microbe–disease associations from biomedical texts

Transforming oncology clinical trial matching through neuro-symbolic, multi-agent AI and an oncology-specific knowledge graph: a prospective evaluation in 3804 patients.

KG-DNA: Knowledge graphs as network DNA for purpose-preserving intelligence in autonomous 6G networks

Ontology - and data-driven defect diagnosis with knowledge graphs and causal reasoning: Application to the risk management of gravity dams

OntoLLM: Enhancing LLM grounding and digression prevention with ontologies and knowledge graphs

Multi-relational knowledge graph for drug-drug interaction prediction via dual aggregation and collaborative optimization.

A cross-scenario robust verification framework for transmission systems via knowledge graph and causal intervention

Towards efficient Graph-RAG via structure-aware intermediate representation: Incremental collaborative exploration on knowledge graph

Knowledge graph integration of clustered medicinal plants, molecules, diseases, and targets.

Evaluating adaptive and generative AI-based feedback and recommendations in a knowledge-graph-integrated programming learning system

Temporal knowledge graph reasoning based on multidimensional information interaction and dynamic frequency awareness

HLCRL: Hierarchical pruning temporal knowledge graph reasoning model based on reinforcement learning

Simplicial complex neural networks for deterministic and uncertain knowledge graph embedding

• Five-star framework to assess On-Device AI in Internet of Everything scenarios • Mafalda 2.0 federated incremental learning model with semantic explainability • Evaluation of Mafalda 2.0 against state-of-the-art On-Device AI approaches • Concrete examples of global and local explainability and federated learning As the Internet of Things (IoT) evolves into an Internet of Everything (IoE), adapting Artificial Intelligence (AI) and Machine Learning (ML) approaches to pervasive computing devices is not enough. Collaborative intelligence is required, calling for on-device AI frameworks combining adequate accuracy and computational efficiency levels with incremental learning on continuous data streams, federated learning in distributed architectures and symbolic explainability formalisms to foster trustworthiness with interpretable trained models and comprehensible prediction outcomes. To fill this gap, the paper introduces a five-star rating for on-device AI based on the Semantic Web of Everything (SWoE) paradigm, and presents the five-star Mafalda 2.0 framework. It combines statistical data processing with Knowledge Graph technologies for information representation and automated reasoning to support: semi-automatic or fully data-driven ontology definition; on-device training to generate highly interpretable semantics-based models; prediction framed as a semantic matchmaking problem, exploiting non-standard reasoning services endowed with logic-based justifications to provide comprehensible results as well as counterfactual and contrastive explanations. An experimental campaign on four publicly available datasets has been carried out to validate the efficiency and accuracy of the proposal, along with federated learning and explainability examples.

Deep learning has achieved remarkable success in chest x-ray interpretation, yet most models remain black boxes, producing accurate predictions without exposing the clinical reasoning behind them. This opacity limits trust and adoption in real-world practice. We introduce Med-ViX-Ray, a knowledge-guided and interpretable framework that integrates symbolic clinical reasoning into a vision Transformer backbone. The model leverages a structured graph of radiological signs and conditions, aligning image attention maps with domain knowledge through a probabilistic soft-matching module and a nudging mechanism that refines classifier outputs. This dual integration allows predictions to be explained in terms of clinically meaningful signs and corresponding image regions, offering transparency beyond post-hoc heatmaps. We evaluated Med-ViX-Ray on MIMIC-CXR for training and internal validation, and tested its generalization on VinDR-CXR and RSNA Pneumonia benchmarks. The proposed method improves recall and F1-score compared to a strong SwinV2 baseline (Respectively, F1-micro: 0.561 - 0.456; Precision: 0.462 - 0-529; Recall: 0.715 - 0.466; ROC: 0.788 - 0.744), while maintaining competitive overall performance. Qualitative analyses confirm that the model highlights clinically relevant regions and sign-activations aligned with radiological practice. These results suggest that knowledge-guided attention and sign-based explanations can enhance interpretability and recall in chest X-ray classification models. Future work will extend the framework toward report generation and prospective clinical evaluation.

Modelling uncertainty in data fusion: a knowledge graph approach

Fine-grained 3D phenotypic analysis of rice plays a vital role in rice breeding and yield estimation. However, a comprehensive rice data acquisition and segmentation pipeline is still lacking. While Neural Radiance Fields (NeRF) have shown impressive results in crop-level 3D reconstruction, their high sensitivity to data volume and camera viewpoints often leads to reconstruction failures for rice. In addition, the large-scale rice point clouds, coupled with heavy occlusion and visual similarity among grains, pose significant challenges for fine-grained trait extraction. To address the challenge of reconstructing rice point clouds under low-quality data conditions, we propose a novel method named Multi-Scale NeRF(MSNeRF). This method incorporates a structure-detail collaborative reconstruction mechanism and a dynamic initialization density scheduling strategy. Furthermore, we introduce a multimodal and multitask rice dataset (MMR) as a benchmark resource for future research. For rice point cloud segmentation, we develop Vision Rice Knowledge Graph Network(VRKGNet), which comprises an image segmentation module, a projection module, and a point cloud segmentation module enhanced with a Transformer to enlarge the receptive field. VRKGNet performs standalone point cloud segmentation and integrates image segmentation results from multiple viewpoints as prior knowledge to enhance semantic and instance-level segmentation. Extensive experiments demonstrate that MSNeRF achieves high-fidelity point cloud reconstruction with as few as 10 viewpoints. VRKGNet achieves superior rice plant segmentation with a semantic segmentation mIoU of 88.79% and an instance segmentation AP 25 of 84.55%, outperforming mainstream algorithms.

Unsupervised temporal knowledge graph entity alignment via BERT and structure adaptation