Search Results (1,961)

Temporal knowledge graphs (TKGs) incorporate temporal information into traditional triplets, enhancing the dynamic representation of real-world events. Temporal knowledge graph reasoning aims to infer unknown quadruples at future timestamps through dynamic modeling and learning of nodes and edges in the knowledge graph. Existing TKG reasoning approaches often suffer from two main limitations: neglecting the influence of temporal information during entity embedding and insufficient or unreasonable processing of relational structures. To address these issues, we propose DERP, a relation-aware reasoning model with dynamic evolution mechanisms. The model enhances entity embeddings by jointly encoding time-varying and static features. It processes graph-structured data through relational graph convolutional layers, which effectively capture complex relational patterns between entities. Notably, it introduces an innovative relational-aware attention mechanism (RAGAT) that dynamically adapts the importance weights of relations between entities. This facilitates enhanced information aggregation from neighboring nodes and strengthens the model’s ability to capture local structural features. Subsequently, prediction scores are generated utilizing a convolutional decoder. The proposed model significantly enhances the accuracy of temporal knowledge graph reasoning and effectively handles dynamically evolving entity relationships. Experimental results on four public datasets demonstrate the model’s superior performance, as evidenced by strong results on standard evaluation metrics, including Mean Reciprocal Rank (MRR), Hits@1, Hits@3, and Hits@10. Full article

Conventional machine-learning-based defenses are unable to generalize well to novel chains of ATT&CK actions. Being inefficient with low telemetry budgets, they are also unable to provide causal explainability and auditing. We propose a knowledge-based cyber-defense framework that integrates ATT&CK constrained model generation, budget-constrained reinforcement learning, and graph-based causal explanation into a single auditable pipeline. The framework formalizes the synthesis of zero-day chains of attacks using a grammar-formalized ATT&CK database and compiles them into the Zeek-aligned witness telemetry. This allows for efficient training of detection using the generated data within limited sensor budgets. The Cyber-Threat Knowledge Graph (CTKG) stores dynamically updated inter-relational semantics between tactics, techniques, hosts, and vulnerabilities. This enhances the decision state using causal relations. The sensor budget policy selects the sensoring and containment decisions within explicit bounds of costs and latency. The inherent defense-provenance features enable a traceable explanation of each generated alarm. Extensive evaluations of the framework using the TTP holdouts of the zero-day instances show remarkable improvements over conventional techniques in terms of low-FPR accuracy, TTD, and calibration. Full article

Botnets represent a persistent and significant threat to internet security. Many detection methods fail because they analyze isolated node data, neglecting the coordinated interactions of centrally managed bots. Graph-based methods, particularly Graph Neural Networks (GNNs), offer a promising solution. This study developed and compared four novel GNN models (HeteroGCN, HeteroGAT, HeteroSAGE, and HeteroGAE) for botnet detection. We constructed a heterogeneous graph from the TI-16 DNS-labeled dataset, capturing interactions between users and domains. Experimental results show our models achieve up to 95% accuracy. Specifically, HeteroSAGE and HeteroGAE significantly outperform other models, demonstrating superior F1-Scores and exceptionally high Recall. This high recall, indicating a low false-negative rate, is critical for effective anomaly detection. Conversely, the computationally expensive HeteroGAT model yielded poorer results and slower inference times, demonstrating that increased model complexity does not guarantee better performance. To our knowledge, this is the first study to successfully apply and compare heterogeneous GNNs for bot detection using DNS query data. Full article

High-resolution range profile (HRRP) target recognition has garnered significant attention in radar automatic target recognition (RATR) research for its rich structural information and low computational costs. With the rapid advancements in deep learning, methods for HRRP target recognition that leverage deep neural networks have emerged as the dominant approaches. Nevertheless, these traditional closed-set recognition methods require labeled data for every class in training, while in reality, seen classes and unseen classes coexist. Therefore, it is necessary to explore methods that can identify both seen and unseen classes simultaneously. To this end, a semantic- and physical-guided generative network (SPGGN) was innovatively proposed for HRRP generalized zero-shot recognition; it combines a constructed knowledge graph with attribute vectors to comprehensively represent semantics and reconstructs strong scattering points to introduce physical constraints. Specifically, to boost the robustness, we reconstructed the strong scattering points from deep features of HRRPs, where class-aware contrastive learning in the middle layer effectively mitigates the influence of target-aspect variations. In the classification stage, discriminative features are produced through attention-based feature fusion to capture multi-faceted information, while the design of balancing loss abates the bias towards seen classes. Experiments on two measured aircraft HRRP datasets validated the superior recognition performance of our method. Full article

A knowledge graph is constructed for flight test safety, which is conducive to enhancing the data deduction ability in flight test monitoring and offers efficient and highly complex decision-making support for safety monitoring. Based on this graph, an aircraft attitude predictive model is established by employing neural network technology. This model can accurately predict the changes in aircraft attitude under pilot manipulation, with a mean absolute error of 0.18 degrees in the predicted angle of attack values. By integrating the knowledge graph and the predictive model, an auxiliary decision-making method for abnormal aircraft attitude situations is proposed. This method calculates the safety manipulation space of the aircraft under different flight states through risk quantification technology, providing a theoretical basis for the pilots’ manipulation decisions in abnormal attitude situations. The research is verified based on simulation data, which not only enhances the scientific rigor and practicability of flight test safety monitoring in simulated scenarios but also provides new theoretical support and technical approaches for the field of flight safety. Full article

Background: Steel box girders are widely employed in bridge engineering due to their excellent mechanical properties and construction convenience, yet their modular design still encounters bottlenecks such as knowledge reuse difficulties and information silos. This study proposes a BIM-driven framework based on knowledge graphs and data fusion. By constructing a professional knowledge graph comprising 85 core entity types and 150 semantic relationships (integrated with over 15,000 knowledge units), systematic management of design knowledge is achieved. The developed BIM reverse modeling technology improves parametric modeling efficiency by 30–40%, while the data fusion mechanism supports over 90% accuracy in design conflict detection. The intelligent decision-making system built upon this framework meets 75% of business scenario requirements while effectively assisting critical decisions such as module selection. Results demonstrate that this framework significantly enhances design collaboration efficiency and intelligence through knowledge structuring and deep data integration. Although some achievements were validated via simulation due to limited field measurement data, the approach demonstrates strong engineering applicability and provides novel technical pathways and methodological support for advancing digital transformation in bridge engineering. Full article

To address the limitations of Retrieval-Augmented Generation (RAG) systems in handling long policy documents, mitigating information dilution, and reducing hallucinations in engineering-oriented applications, this paper proposes SPR-RAG, a retrieval-augmented framework designed for knowledge-intensive vertical domains such as electric power policy analysis. With practicality and interpretability as core design goals, SPR-RAG introduces a Semantic Parsing Retriever (SPR), which integrates community detection–based entity disambiguation and transforms natural language queries into logical forms for structured querying over a domain knowledge graph, thereby retrieving verifiable triple-based evidence. To further resolve retrieval bias arising from diverse policy writing styles and inconsistencies between user queries and policy text expressions, a question-repository–based indirect retrieval mechanism is developed. By generating and matching latent questions, this module enables more robust retrieval of non-structured contextual evidence. The system then fuses structured and unstructured evidence into a unified dual-source context, providing the generator with an interpretable and reliable grounding signal. Experiments conducted on real electric power policy corpora demonstrate that SPR-RAG achieves 90.01% faithfulness—representing a 5.26% improvement over traditional RAG—and 76.77% context relevance, with a 5.96% gain. These results show that SPR-RAG effectively mitigates hallucinations caused by ambiguous entity names, textual redundancy, and irrelevant retrieved content, thereby improving the verifiability and factual grounding of generated answers. Overall, SPR-RAG demonstrates strong deployability and cross-domain transfer potential through its “Text → Knowledge Graph → RAG” engineering paradigm. The framework provides a practical and generalizable technical blueprint for building high-trust, industry-grade question–answering systems, offering substantial engineering value and real-world applicability. Full article

Recent advancements in artificial intelligence have driven significant progress in intelligent anti-jamming communications. However, existing methods still face two major limitations: reinforcement learning-based models often suffer from slow convergence, while knowledge graph-based approaches lack dynamic interaction capabilities in complex, time-varying electromagnetic environments. To address these challenges, this paper proposes a novel two-stage intelligent decision-making framework. In the first stage, an anti-jamming knowledge graph repository is constructed to enable rapid decision-making through efficient reasoning, thereby ensuring real-time responsiveness. The second stage introduces a hierarchical reinforcement learning architecture that facilitates environmental interaction for continuous model evolution and self-adaptation. By simplifying multidimensional parameter spaces into two-dimensional decision scenarios, the proposed method effectively reduces computational complexity and accelerates convergence. Experimental results demonstrate that the proposed method achieves a 4.2% increase in the anti-jamming decision success rate and a 104.8% improvement in the transmission rate compared to state-of-the-art methods. Simulation results demonstrate the superiority of the framework in both anti-jamming performance and learning efficiency, validating its practical effectiveness in dynamic electromagnetic environments. Full article

In high-mountain canyon areas, complex geological environments lead to frequent cascading disasters with unclear triggering mechanisms, posing severe threats to human life and property. Existing knowledge graph research in geology predominantly focuses on single-hazard types or general geological entities, lacking structured modeling and specialized datasets for cascading disaster processes, particularly the evolutionary chains in high-mountain canyon settings. To address this gap, this study proposes a method for constructing a knowledge graph tailored to cascading disasters in high-mountain canyon regions. First, a three-layer schema framework—comprising concept, relation, and instance layers—was designed to systematically characterize the knowledge elements and evolutionary relationships of disaster chains. To address the lack of a knowledge dataset for cascade disasters, this paper integrates multi-source heterogeneous data to construct a high-mountain canyon cascading disasters entity–relation dataset (DCER-MC), providing a reliable benchmark for related tasks. Based on this dataset, we implemented the knowledge graph and conducted disaster chain analysis. Experiments and applications demonstrate that the constructed knowledge graph effectively supports structured storage, centralized management, and scenario-based application of regional cascading disaster information. The main contributions of this work are (1) proposing a targeted schema framework for cascading-disaster knowledge graphs; (2) releasing a specialized dataset for cascading disasters in high-mountain canyon regions; and (3) establishing a complete pipeline from data to knowledge to scenario-based services, offering a novel knowledge-driven paradigm for disaster chain risk identification, inference prediction, and emergency decision-making in these areas. Full article

The rapid expansion and interdisciplinary nature of Physical Internet (PI) research have resulted in fragmented knowledge, limiting the ability of stakeholders to identify emerging trends, actionable insights and genuine research gaps. This study introduces a novel knowledge management approach that uses Graph Retrieval-Augmented Generation (GraphRAG) to systematically organize and integrate PI-related literature. A comprehensive knowledge graph was constructed by extracting and semantically modeling entities and relationships from 2835 academic papers, conference proceedings and international roadmaps. The developed system incorporates fuzzy semantic search and multiple retrieval strategies, including local, global and hybrid approaches, enabling nuanced, context-aware access to information. Stakeholder-specific prompts, tailored to the needs of industry, government and academia, demonstrate how GraphRAG can support the discovery of business model innovations, policy design and underexplored research areas. A comparative evaluation using cosine similarity and BERTScore confirms that graph-based strategies outperform standard LLM retrieval in providing relevant and comprehensive answers while also revealing connections that would be missed in manual reviews. The results demonstrate that the proposed GraphRAG model is a scalable and extensible framework for addressing knowledge gaps and promoting collaboration in PI research synthesis for sustainable logistics. The model also shows promise for application in other complex domains. Full article

Emergency scenarios in the Internet of Vehicles (IoV) face significant challenges due to the stringent requirements for ultra-reliable and low-latency communication under high-mobility conditions. This paper proposes a cooperative transmission framework for semantic communication to address these challenges. We introduce a knowledge graph-based approach to represent information as semantic triples (structured entity-relation-attribute representations), whose importance is quantified using a Zipf distribution, enabling prioritized transmission. At the physical layer, a semantic-aware cooperative communication scheme is proposed to combat fading and enhance transmission reliability. The joint optimization of the number of transmitted triples and node power allocation is formulated as a cross-layer problem. To tackle this Mixed-Integer Nonlinear Programming (MINLP) problem with a hybrid action space, we employ the Multi-Pass Deep Q-Network (MP-DQN) algorithm, which is specifically designed for problems with hybrid discrete-continuous action spaces. Simulation results demonstrate that our framework dynamically adapts to channel states and semantic value, achieving up to 85% end-to-end success rate and improving convergence speed by approximately 40% compared to conventional methods. Full article

Sequence prediction is widely applied and has significant theoretical and practical application value in fields such as meteorology and medicine. Traditional models, such as LSTM(Long Short-Term Memory) and GRU(Gated Recurrent Unit), may perform better than this model when dealing with short-term dependencies, but their performance may decline on long sequences and complex data, especially in cases where sequence fluctuations are significant. However, the Transformer requires a large amount of computing resources (parallel computing) when dealing with long sequences. Aiming to solve the problems existing in sequence prediction models, such as insufficient modeling ability of long sequence dependencies, insufficient interpretability, and low efficiency of multi-element heterogeneous information fusion, this study embeds sequential data into the knowledge graph, enabling the model to associate context information when processing complex data and providing more reasonable decision support for the prediction results. Given the historical sequence and the predicted future sequence, three groups of sequence lengths were set in the experiment. And MAE (Mean Absolute Error)and MSE (Mean Square Error) are used as indicators for sequence prediction. In sequence prediction, dynamic evolution is conducive to enhancing the ability of the prediction model to capture the changing patterns of the current time series data and significantly improving the reliability of the prediction results. Experiments were conducted using five datasets from different application fields to verify the effectiveness of the prediction model. The experimental results show that based on the randomization of the prediction time step, the prediction model proposed in this study significantly improves the expression performance of stationary sequences. It has addressed the shortcomings of these traditional methods, such as maintaining good performance in the case of short sequences with large fluctuations. Full article

Accurate state estimation is essential for the real-time operation and control of modern distribution systems characterized by high renewable energy penetration, bidirectional power flows, and volatile loads. Conventional model-driven approaches such as the Weighted Least Squares (WLS) exhibit limited robustness under noisy and sparse measurements, while existing data-driven methods often neglect critical physical constraints inherent to power systems. To address these limitations, this paper proposes a physics-constrained Graph Attention Network (GAT) framework for distribution system state estimation (DSSE) that synergistically integrates data-driven learning with physical domain knowledge. The proposed method comprises three key components: (1) a Gaussian Mixture Model (GMM)-based data augmentation strategy that captures the stochastic characteristics of loads and distributed generation to generate synthetic samples consistent with actual operating distributions; (2) a GAT-based feature extractor with topology-aware admittance matrix embedding that effectively learns spatial dependencies and structural relationships among network nodes; and (3) a physics-constrained loss function that incorporates nodal power and voltage limit penalties to enforce operational feasibility. Comprehensive evaluations on the real-world 141-bus test system demonstrate that the proposed method achieves mean absolute error (MAE) reductions of 52.4% and 45.5% for voltage magnitude and angle estimation, respectively, compared to conventional Graph Convolutional Network (GCN)-based approaches. These results validate the superior accuracy, robustness, and adaptability of the proposed framework under challenging measurement conditions. Full article

Green buildings increasingly couple electrical, thermal, and hydrogen subsystems, yet these assets are typically monitored and controlled through separate standards and protocols. The resulting heterogeneous information models and communication stacks hinder millisecond-level coordination, plug-and-play integration, and resilient operation. To address this gap, we develop a unified information model and a cross-protocol real-time interaction mechanism based on extensions of IEC 61850. At the modeling level, we introduce new logical nodes and standardized data objects that describe electrical, thermal, and hydrogen devices in a single semantic space, supported by a global unit system and knowledge-graph-based semantic checking. At the communication level, we introduce a semantic gateway with adaptive mapping bridges IEC 61850 and legacy building protocols, while fast event messaging and 5G-enabled edge computing support deterministic low-latency control. The approach is validated on a digital-twin platform that couples an RTDS-based multi-energy system with a 5G test network. Experiments show device plug-and-play within 0.8 s, cross-protocol response-time differences below 50 ms, GOOSE latency under 5 ms, and critical-data success rates above 90% at a bit-error rate of 10⁻³. Under grid-fault scenarios, the proposed framework reduces voltage recovery time by about 60% and frequency deviation by about 70%, leading to more than 80% improvement in a composite resilience index compared with a conventional non-unified architecture. These results indicate that the framework provides a practical basis for interoperable, low-carbon, and resilient energy management in green buildings. Full article

With the exponential growth of digital music, efficiently identifying key music relationship nodes in large-scale music knowledge graphs is crucial for enhancing music recommendation, emotion analysis, and genre classification. To address this challenge, we propose MUSIGAIN, a GATv2-based adaptive framework that combines graph robustness metrics with advanced graph neural network mechanisms for multi-relationship mining in heterogeneous music knowledge graphs. MUSIGAIN tackles three fundamental challenges: the prohibitive computational complexity of exact graph-robustness calculations, the limitations of traditional centrality measures in capturing semantic heterogeneity, and the over-smoothing problem in deep graph neural networks. The framework introduces three key innovations: (1) a layer-wise dynamic skipping mechanism that adaptively controls propagation depth based on third-order embedding stability, reducing computation by 30–40% while preventing over-smoothing; (2) the DiGRAF adaptive activation function that enables node-specific nonlinear transformations to capture semantic heterogeneity across different entity types; and (3) ranking-based optimization supervised by graph robustness metrics, focusing on relative importance ordering rather than absolute value prediction. Experimental results on four real-world music knowledge graphs (POP-MKG, ROCK-MKG, JAZZ-MKG, CLASSICAL-MKG) demonstrate that MUSIGAIN consistently outperforms existing methods in Top-5% node identification accuracy, achieving up to 96.78% while maintaining linear scalability to graphs with hundreds of thousands of nodes. MUSIGAIN provides an efficient, accurate, and interpretable solution for key node identification in complex heterogeneous graphs. Full article