Knowledge-Based Systems最新文献_第8页

IRTF: A new tensor factorization for irregular multidimensional data recovery IRTF：一种新的不规则多维数据恢复张量分解方法

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-12 DOI: 10.1016/j.knosys.2025.114372

Jin-Yu Xie , Hao Zhang , Xi-Le Zhao , Yi-Si Luo

{"title":"IRTF: A new tensor factorization for irregular multidimensional data recovery","authors":"Jin-Yu Xie , Hao Zhang , Xi-Le Zhao , Yi-Si Luo","doi":"10.1016/j.knosys.2025.114372","DOIUrl":"10.1016/j.knosys.2025.114372","url":null,"abstract":"<div><div>Tensor factorizations, although serving as paramount tools for exploiting prior knowledge of multidimensional data, are unsuitable for emerging irregular multidimensional data with the arbitrary shape spatial domain (i.e., spatial-irregular tensor), such as superpixels and spatial transcriptomics. Developing new tensor factorizations suitable for spatial-irregular tensors poses a compelling challenge. To meet this challenge, we introduce a novel Irregular Tensor Factorization (IRTF), which can fully capture the intrinsic spatial and channel information behind the spatial-irregular tensor. Concretely, a spatial-irregular tensor can be decomposed into the product of an intrinsic regular tensor, learnable channel transform matrices, and a learnable spatial transform matrix. Accompanying IRTF, we suggest the Total Variation on Channel and Spatial Transforms (TV-CST) to exploit the local information of spatial-irregular tensors, which is hardly excavated by traditional total variation methods. Combining the proposed IRTF and TV-CST, we built a spatial-irregular tensor recovery model. Extensive experiments on real-world spatial-irregular tensors demonstrate the promising performance of our IRTF and its significant advantages on downstream tasks.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114372"},"PeriodicalIF":7.6,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-class intrusion detection system for in-vehicle networks using few-shot learning and convolutional anomaly transformer network 基于少采样学习和卷积异常变压器网络的车载网络多类入侵检测系统

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-12 DOI: 10.1016/j.knosys.2025.114436

Nguyen Thanh Minh Duy , Truong Hoang Bao Huy , Pham Van Phu , Tien-Dat Le , Daehee Kim

{"title":"Multi-class intrusion detection system for in-vehicle networks using few-shot learning and convolutional anomaly transformer network","authors":"Nguyen Thanh Minh Duy , Truong Hoang Bao Huy , Pham Van Phu , Tien-Dat Le , Daehee Kim","doi":"10.1016/j.knosys.2025.114436","DOIUrl":"10.1016/j.knosys.2025.114436","url":null,"abstract":"<div><div>Modern vehicles depend on the Controller Area Network (CAN) for electronic control unit (ECU) communication, but its inherent vulnerabilities necessitate robust intrusion detection systems (IDS). Current machine learning and deep learning IDS solutions struggle with limited labeled data, class imbalances, and costly data collection processes. Few-shot learning, effective with few labeled samples, remains underexplored for in-vehicle networks (IVNs) despite its potential in data-scarce automotive cybersecurity scenarios. To bridge this gap, we introduce the first few-shot learning approach for multi-class intrusion detection in IVNs, leveraging a novel, lightweight Convolutional Anomaly Transformer. By integrating a 1D convolutional layer with an Anomaly Transformer, our model effectively classifies diverse attack types with minimal training data, mitigating class imbalance. Experiments on the widely-used real-world Car Hacking dataset, the complex ROAD dataset, and the distinct CAN-ML dataset validate its efficacy. On the Car Hacking dataset, we achieve an exceptional F1 score of 0.9994 with only 2 % of training data, improving to 0.9999 with 10 %. On the challenging ROAD dataset, characterized by diverse attacks and high variability, the model achieves an F1 score of up to 0.9980 using just 10 % of training data. Demonstrating strong generalization capabilities, the model also attains an impressive F1 score of 0.9918 on the CAN-ML dataset, which features entirely different vehicles and attack distributions. Furthermore, the lightweight architecture of our proposed IDS enables practical deployment in resource-constrained automotive environments.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114436"},"PeriodicalIF":7.6,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SEER: Knowledge-driven semantic image restoration with vision-language diffusion alignment 知识驱动的语义图像恢复与视觉语言扩散对齐

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-12 DOI: 10.1016/j.knosys.2025.114464

Shengliang Wu , Jun Jiang , Xin He , Yong Xu , Yujun Zhu , Weiwei Jiang , Heju Li

{"title":"SEER: Knowledge-driven semantic image restoration with vision-language diffusion alignment","authors":"Shengliang Wu , Jun Jiang , Xin He , Yong Xu , Yujun Zhu , Weiwei Jiang , Heju Li","doi":"10.1016/j.knosys.2025.114464","DOIUrl":"10.1016/j.knosys.2025.114464","url":null,"abstract":"<div><div>Semantic communication is an emerging paradigm to enhance network efficiency and perceptual quality, particularly demonstrating strong potential in image generation tasks. However, existing deep learning (DL)-based single-modal reconstruction approaches often suffer from semantic distortion and image blurring under bandwidth-limited and highly noisy channel conditions, limiting their suitability in task-oriented perception scenarios. Although generative AI-based semantic communication can significantly reduce data transmission volume, its high sensitivity to channel noise and lack of dynamic adaptation mechanisms limit the stability of reconstruction. To address these challenges, this paper proposes a multi-modal semantic communication framework named <em>SEER</em>, designed for resource-constrained intelligent sensing terminals. Built upon a pretrained language model, SEER incorporates a channel-aware prompt control strategy, a dual-modal integrative semantic restoration mechanism (DISR), and a single-pass sequential cross-modal reconstruction pathway to achieve collaborative semantic representation and robust structural recovery between images and text. Experimental results demonstrate that SEER achieves approximately <span><math><mrow><mn>2.08</mn><mspace></mspace><mo>%</mo></mrow></math></span> bandwidth compression, while outperforming existing methods under extreme channel conditions by <span><math><mrow><mn>33.92</mn><mspace></mspace><mo>%</mo></mrow></math></span> in structural fidelity and <span><math><mrow><mn>12.64</mn><mspace></mspace><mo>%</mo></mrow></math></span> in perceptual consistency, highlighting its strong engineering deployability.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"329 ","pages":"Article 114464"},"PeriodicalIF":7.6,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temporal knowledge graph forecasting query based on global-local historical information 基于全局-局部历史信息的时态知识图预测查询

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-11 DOI: 10.1016/j.knosys.2025.114476

Luyi Bai, Tongyue Zhang, Lin Zhu

{"title":"Temporal knowledge graph forecasting query based on global-local historical information","authors":"Luyi Bai, Tongyue Zhang, Lin Zhu","doi":"10.1016/j.knosys.2025.114476","DOIUrl":"10.1016/j.knosys.2025.114476","url":null,"abstract":"<div><div>Temporal knowledge graph (TKG) queries aim to retrieve relevant facts that conform to time constraints to answer a given query by reasoning known TKG facts. The continuous development of TKG query research has extended TKG queries to the TKG forecasting domain, enabling the forecasting of answers to unknown queries by leveraging historical information from query questions. However, TKG forecasting query research is currently facing two considerable challenges. Firstly, existing TKG forecasting query methods cannot adequately capture the global historical information of query questions, which makes it difficult to effectively mine periodic features, repetitive patterns, and dynamic evolution characteristics of new events. Secondly, when modeling local historical information, existing methods fail to focus on the historical correlation of facts between adjacent timestamps, ignoring the crucial role of local information in the temporal evolution process. In this paper, a TKG forecasting query framework based on global-local historical information is proposed to solve the above challenges. Specifically, for the global historical information of the query question, the periodic and repetitive patterns of historical facts and the potential changing laws of non-historical facts are learned by modeling global historical facts and non-historical facts. Concerning the local historical information, entities and relations are aggregated in knowledge graph (KG) snapshots and their changes and evolution are simulated at adjacent timestamps to enhance the ability of the model to capture temporal dependencies. At the same time, the impact of local snapshots on query questions is quantified to capture the evolution process of local information more accurately. Finally, we design dedicated scoring functions for different types of query tasks to achieve effective query forecasting. Extensive experiments on four datasets demonstrate that the proposed model has better performances in forecasting unknown queries than other baseline models.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"329 ","pages":"Article 114476"},"PeriodicalIF":7.6,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Open-ViTabQA: A novel benchmark for Vietnamese question answering on open domain wikipedia table open - vitabqa：一个在开放域维基百科表上的越南语问答的新基准

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-11 DOI: 10.1016/j.knosys.2025.114391

Dung Hoang Dao , Ngan Thi-Kim Huynh , Khanh Quoc Tran , Kiet Van Nguyen

{"title":"Open-ViTabQA: A novel benchmark for Vietnamese question answering on open domain wikipedia table","authors":"Dung Hoang Dao , Ngan Thi-Kim Huynh , Khanh Quoc Tran , Kiet Van Nguyen","doi":"10.1016/j.knosys.2025.114391","DOIUrl":"10.1016/j.knosys.2025.114391","url":null,"abstract":"<div><div>This paper presents Open-ViTabQA, the first Vietnamese dataset for Table Question Answering (Table QA), addressing the lack of resources for Vietnamese natural language processing. The dataset was meticulously constructed and rigorously validated to ensure high quality. A comprehensive analysis of the structural characteristics of the dataset, including table structure, question types, and answer patterns, is presented. We also introduce BIF, a novel metric combining PhoBERT embeddings within BERTScore for semantic similarity and ViNLI for logical consistency, effectively capturing Vietnamese-specific linguistic nuances and logical coherence. The rigorously validated dataset, accompanied by an analysis of its structural characteristics, provides a robust framework for evaluating Table QA systems. Experiments with pre-trained models and large language models (LLMs) show that ViT5 achieves an F1-score of 45.22 %, an Exact Match (EM) score of 45.13 %, and a BIF score of 0.562. Among large language models, Gemini 2.0 Flash Experimental achieves 60.50 % F1 and 60.20 % EM, while Gemini 1.5 Pro-leads with a BIF score of 0.649, slightly outperforming Gemini 2.0 Flash Experimental (0.644 BIF), indicating more stable reasoning capabilities. However, a significant gap persists compared to human performance (86.49 % F1, 83.43 % EM, 0.781 BIF), highlighting challenges in capturing Vietnamese linguistic subtleties and logical intricacies. These findings underscore opportunities for advancing model performance and addressing data scarcity in Vietnamese Table QA. To facilitate reproducibility and further research, the Open-ViTabQA dataset is publicly accessible for research purposes.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114391"},"PeriodicalIF":7.6,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FCAT: Federated causal adversarial training FCAT：联邦因果对抗训练

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-11 DOI: 10.1016/j.knosys.2025.114440

Yunhao Feng , Yanming Guo , Mingrui Lao, Yulun Wu, Yishan Li, Yuxiang Xie

{"title":"FCAT: Federated causal adversarial training","authors":"Yunhao Feng , Yanming Guo , Mingrui Lao, Yulun Wu, Yishan Li, Yuxiang Xie","doi":"10.1016/j.knosys.2025.114440","DOIUrl":"10.1016/j.knosys.2025.114440","url":null,"abstract":"<div><div>Causal inference has been proven to be a crucial technique for improving the efficacy and explainability of adversarial training (AT). However, its applicability in the decentralized adversarial training paradigm has not been fully explored. Where one potential challenge is to apply the causal inference in the settings of non-independent and identically distributed (Non-IID) federated learning. In particular, the imbalanced data distributions among various clients will unavoidably hinder the efficacy and adaptability of causal inference. To address this issue, this paper proposes a novel yet practical method dubbed Federated Causal Adversarial Training (FCAT), which seeks to improve causal models via calibrated correction information. Additionally, we introduce a lightweight slack aggregation method aimed at addressing client model disparities and minimizing the communication overhead in each iteration. Extensive experimental results demonstrate that FCAT significantly improves the efficacy of causal models in federated adversarial training, and remarkably outperforms the current state-of-the-art (SOTA) competitors on multiple widely-adopted benchmarks.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114440"},"PeriodicalIF":7.6,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing dual network based semi-supervised medical image segmentation with uncertainty-guided pseudo-labeling 基于不确定性引导的伪标记增强双网络半监督医学图像分割

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-11 DOI: 10.1016/j.knosys.2025.114454

Yunyao Lu , Yihang Wu , Ahmad Chaddad , Tareef Daqqaq , Reem Kateb

{"title":"Enhancing dual network based semi-supervised medical image segmentation with uncertainty-guided pseudo-labeling","authors":"Yunyao Lu , Yihang Wu , Ahmad Chaddad , Tareef Daqqaq , Reem Kateb","doi":"10.1016/j.knosys.2025.114454","DOIUrl":"10.1016/j.knosys.2025.114454","url":null,"abstract":"<div><div>Despite the remarkable performance of supervised medical image segmentation models, relying on a large amount of labeled data is impractical in real-world situations. Semi-supervised learning approaches aim to alleviate this challenge using unlabeled data through pseudo-label generation. Yet, existing semi-supervised segmentation methods still suffer from noisy pseudo-labels and insufficient supervision within the feature space. To solve these challenges, this paper proposes a novel semi-supervised 3D medical image segmentation framework based on a dual-network architecture. Specifically, we investigate a Cross Consistency Enhancement module using both cross pseudo and entropy-filtered supervision to reduce the noisy pseudo-labels, while we design a dynamic weighting strategy to adjust the contributions of pseudo-labels using an uncertainty-aware mechanism (i.e., Kullback–Leibler divergence). In addition, we use a self-supervised contrastive learning mechanism to align uncertain voxel features with reliable class prototypes by effectively differentiating between trustworthy and uncertain predictions, thus reducing prediction uncertainty. Extensive experiments are conducted on three 3D segmentation datasets, Left Atrial, NIH Pancreas and BraTS-2019. The proposed approach consistently exhibits superior performance across various settings (e.g., 89.95 % Dice score on left Atrial with 10 % labeled data) compared to the state-of-the-art methods. Furthermore, the usefulness of the proposed modules is further validated via ablation experiments. The code repository is available at <span><span>https://github.com/AIPMLab/Semi-supervised-Segmentation</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114454"},"PeriodicalIF":7.6,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A transformer-based approach for traffic prediction with fusion spatiotemporal attention 基于变换的融合时空注意力交通预测方法

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-11 DOI: 10.1016/j.knosys.2025.114466

Wenfeng Zhou , Guojiang Shen , Zhenzhen Zhao , Zhaolin Deng , Tao Tang , Xiangjie Kong , Amr Tolba , Osama Alfarraj

{"title":"A transformer-based approach for traffic prediction with fusion spatiotemporal attention","authors":"Wenfeng Zhou , Guojiang Shen , Zhenzhen Zhao , Zhaolin Deng , Tao Tang , Xiangjie Kong , Amr Tolba , Osama Alfarraj","doi":"10.1016/j.knosys.2025.114466","DOIUrl":"10.1016/j.knosys.2025.114466","url":null,"abstract":"<div><div>Accurate traffic data prediction is a crucial technology for data-driven intelligent transportation systems. This has an important impact on optimizing urban traffic management, travel efficiency, traffic experience, etc. Traffic flow prediction tasks primarily focus on mining dynamic spatiotemporal dependencies. Most existing Transformer-based methods and GNN-based methods have limitations in mining local-global spatiotemporal dependencies. To address this issue, we propose a novel traffic data prediction model called LGSTformer that can perceive local-global spatiotemporal dependencies. First, we construct an embedding layer that provides multiple types of embedding representations for the model by projecting spatiotemporal data and temporal and spatial information into different embeddings. Next, we design two modules to capture local-global temporal and spatial dependencies based on the naive spatiotemporal self-attention mechanism: the local-global temporal module and the local-global spatial module. The former incorporates multi-scale temporal convolutions to capture short-term temporal dependencies, and the latter incorporates dynamic-static graph convolutions to capture local spatial dependencies. Finally, to achieve effective fusion of local-global dependency information, a dual-path adaptive gated fusion layer based on a gating mechanism is introduced to attain adaptive fusion of information at different levels. Experimental results on four public real-world traffic datasets show that LGSTformer outperforms existing methods and has potential as an advanced solution for traffic flow prediction.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"329 ","pages":"Article 114466"},"PeriodicalIF":7.6,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Taxonomy-guided routing in capsule network for hierarchical image classification 用于分层图像分类的胶囊网络分类引导路由

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-11 DOI: 10.1016/j.knosys.2025.114444

Khondaker Tasrif Noor , Wei Luo , Antonio Robles-Kelly , Leo Yu Zhang , Mohamed Reda Bouadjenek

{"title":"Taxonomy-guided routing in capsule network for hierarchical image classification","authors":"Khondaker Tasrif Noor , Wei Luo , Antonio Robles-Kelly , Leo Yu Zhang , Mohamed Reda Bouadjenek","doi":"10.1016/j.knosys.2025.114444","DOIUrl":"10.1016/j.knosys.2025.114444","url":null,"abstract":"<div><div>Hierarchical multi-label classification in computer vision presents significant challenges in maintaining consistency across different levels of class granularity while capturing fine-grained visual details. This paper presents Taxonomy-aware Capsule Network (HT-CapsNet), a novel capsule network architecture that explicitly incorporates taxonomic relationships into its routing mechanism to address these challenges. Our key innovation lies in a taxonomy-aware routing algorithm that dynamically adjusts capsule connections based on known hierarchical relationships, enabling more effective learning of hierarchical features while enforcing taxonomic consistency. Extensive experiments on six benchmark datasets, including Fashion-MNIST, Marine-Tree, CIFAR-10, CIFAR-100, CUB-200-2011, and Stanford Cars, demonstrate that HT-CapsNet significantly outperforms existing methods across various hierarchical classification metrics. Notably, on CUB-200-2011, HT-CapsNet achieves absolute improvements of <span><math><mrow><mn>10.32</mn><mspace></mspace><mo>%</mo></mrow></math></span>, <span><math><mrow><mn>10.2</mn><mspace></mspace><mo>%</mo></mrow></math></span>, <span><math><mrow><mn>10.3</mn><mspace></mspace><mo>%</mo></mrow></math></span>, and <span><math><mrow><mn>8.55</mn><mspace></mspace><mo>%</mo></mrow></math></span> in hierarchical accuracy, F1-score, consistency, and exact match, respectively, compared to the best-performing baseline. On the Stanford Cars dataset, the model improves upon the best baseline by <span><math><mrow><mn>21.69</mn><mspace></mspace><mo>%</mo></mrow></math></span>, <span><math><mrow><mn>18.29</mn><mspace></mspace><mo>%</mo></mrow></math></span>, <span><math><mrow><mn>37.34</mn><mspace></mspace><mo>%</mo></mrow></math></span>, and <span><math><mrow><mn>19.95</mn><mspace></mspace><mo>%</mo></mrow></math></span> in the same metrics, demonstrating the robustness and effectiveness of our approach for complex hierarchical classification tasks.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"329 ","pages":"Article 114444"},"PeriodicalIF":7.6,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Precision through progression: Empowering temporal knowledge graph reasoning with knowledge-guided chain of thought 精确通过进展：授权时间知识图推理与知识引导的思维链

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-11 DOI: 10.1016/j.knosys.2025.114448

Zhangtao Cheng , Shichong Li , Yichen Xin , Bin Chen , Ting Zhong , Fan Zhou

{"title":"Precision through progression: Empowering temporal knowledge graph reasoning with knowledge-guided chain of thought","authors":"Zhangtao Cheng , Shichong Li , Yichen Xin , Bin Chen , Ting Zhong , Fan Zhou","doi":"10.1016/j.knosys.2025.114448","DOIUrl":"10.1016/j.knosys.2025.114448","url":null,"abstract":"<div><div>Temporal Knowledge Graphs (TKGs) have emerged as a powerful paradigm for event forecasting, owing to their ability to dynamically represent the evolving relationships between entities over time. By effectively reasoning along the temporal dimension, TKGs help address real-world data incompleteness through inference of missing facts. Recent advances in large language models (LLMs) have led to their integration with TKG reasoning tasks. However, current LLM-based approaches face three critical challenges: (1) insufficient utilization of background knowledge, (2) inadequate modeling of the evolving temporal dynamics intrinsic to TKGs, and (3) difficulty in bridging the structural mismatch between the graph structure and the sequential operation mode of LLMs. To address these challenges, we propose EV-COT, a novel EVent-aware Chain-Of-Thought reasoning framework designed to explicitly model event evolution through structured, interpretable reasoning chains. EV-COT comprises three modular, plug-and-play components – knowledge module, perception module, and thinking module – that work collaboratively to extract essential event-related cues for enhanced reasoning. Specifically, the knowledge module generates high-quality contextual knowledge to enrich entity representation, and the perception module captures intricate structural and temporal patterns inherent in TKGs. Moreover, the thinking module extracts temporal logical rules, facilitating interpretable step-by-step reasoning. By effectively integrating these diverse contextual knowledge, EV-COT delivers more accurate predictions. Extensive evaluations on three datasets demonstrate that EV-COT consistently outperforms state-of-the-art methods, highlighting its effectiveness for precise event forecasting in TKGs.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"329 ","pages":"Article 114448"},"PeriodicalIF":7.6,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0