Knowledge-Based Systems最新文献_第3页

MESN: A multimodal knowledge graph embedding framework with expert fusion and relational attention

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-04-11 DOI: 10.1016/j.knosys.2025.113541

Ban Tran , Thanh Le

{"title":"MESN: A multimodal knowledge graph embedding framework with expert fusion and relational attention","authors":"Ban Tran , Thanh Le","doi":"10.1016/j.knosys.2025.113541","DOIUrl":"10.1016/j.knosys.2025.113541","url":null,"abstract":"<div><div>Knowledge graph embedding is essential for knowledge graph completion and downstream applications. However, in multimodal knowledge graphs, this task is particularly challenging due to incomplete and noisy multimodal data, which often fails to capture semantic relationships between entities. While existing methods attempt to integrate multimodal features, they frequently overlook relational semantics and cross-modal dependencies, leading to suboptimal entity representations. To address these limitations, we propose MESN, a novel multimodal embedding framework that integrates relational and multimodal signals through semantic aggregation and neighbor-aware attention mechanisms. MESN selectively extracts informative multimodal features via adaptive attention and expert-driven learning, ensuring more expressive entity embeddings. Additionally, we introduce an enhanced ComplEx-based scoring function, which effectively combines structured graph interactions with multimodal information, capturing both relational and feature diversity. Extensive experiments on standard multimodal datasets confirm that MESN significantly outperforms baselines across multiple evaluation metrics. Our findings highlight the importance of relational guidance in multimodal embedding tasks, paving the way for more robust and semantically-aware knowledge representations.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"318 ","pages":"Article 113541"},"PeriodicalIF":7.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839245","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

Semi-supervised high-uncertainty deep canonical variate analysis for fault diagnosis in blast furnace ironmaking

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-04-11 DOI: 10.1016/j.knosys.2025.113454

Yuelin Yang , Chunjie Yang , Xiongzhuo Zhu , Hanwen Zhang , Haifeng Zhang , Zhiqi Su , Siwei Lou

{"title":"Semi-supervised high-uncertainty deep canonical variate analysis for fault diagnosis in blast furnace ironmaking","authors":"Yuelin Yang , Chunjie Yang , Xiongzhuo Zhu , Hanwen Zhang , Haifeng Zhang , Zhiqi Su , Siwei Lou","doi":"10.1016/j.knosys.2025.113454","DOIUrl":"10.1016/j.knosys.2025.113454","url":null,"abstract":"<div><div>Blast furnace ironmaking process (BFIP) is of paramount importance in the steel industry. Reliable fault diagnosis for BFIP is crucial to ensure production safety, improve efficiency and quality, reduce costs, and maximize resource utilization. However, establishing effective fault diagnosis models is hindered by challenges including non-linearity, dynamics, widespread noise, and the scarcity of labeled data alongside an abundance of unlabeled data. To address these issues, this paper proposes a new fault diagnosis method for BFIP called semi-supervised high-uncertainty deep canonical variate analysis (SHDCVA). The proposed algorithm consists of three main parts, including (1) high-uncertainty nonlinear dynamic feature capture, (2) robust semi-supervised framework construction, and (3) model solving and parameter optimization. Firstly, a high-uncertainty deep canonical variate representation method is proposed from a probabilistic perspective, which can capture high-uncertainty nonlinear dynamic characteristics. The high-uncertainty property can effectively deal with data noise and enhance the reliability of downstream fault diagnosis model. Moreover, this paper proposes a robust semi-supervised classification framework that can efficiently utilize limited labeled samples and a large amount of unlabeled samples. The supervised part controls the release of labeled samples by training signal annealing method (TSA) to prevent overfitting, while the unsupervised part enforces model smoothing by applying adversarial perturbations to enhance robustness. Subsequently, an efficient computational method is devised to generate adversarial perturbations and the overall objective is constructed. Finally, the effectiveness of SHDCVA is confirmed through a practical case study utilizing genuine BFIP data.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"317 ","pages":"Article 113454"},"PeriodicalIF":7.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830130","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

Parameterized data-free knowledge distillation for heterogeneous federated learning

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-04-11 DOI: 10.1016/j.knosys.2025.113502

Cheng Guo , Qianqian He , Xinyu Tang , Yining Liu , Yingmo Jie

{"title":"Parameterized data-free knowledge distillation for heterogeneous federated learning","authors":"Cheng Guo , Qianqian He , Xinyu Tang , Yining Liu , Yingmo Jie","doi":"10.1016/j.knosys.2025.113502","DOIUrl":"10.1016/j.knosys.2025.113502","url":null,"abstract":"<div><div>Knowledge distillation has emerged as a widely adopted and effective method for addressing two challenges of heterogeneous federated learning: Data heterogeneity causes client drift, which makes model convergence slow and model accuracy decrease, and personalized requirements of heterogeneous clients are ignored, which cannot be satisfied by a single global model. However, most existing knowledge distillation-based federated learning schemes are constrained by two fundamental limitations: They rely on public datasets for knowledge distillation, forming an impractical assumption for real-world scenarios, and the model personalization process employs a unified redundant teacher model, which conflicts with the diverse data distribution characteristics among heterogeneous clients. Therefore, we propose a parameterized data-free knowledge distillation, addressing the impractical dependency on public datasets and the static single knowledge transfer bottleneck through global view knowledge extraction without public datasets and an adaptive personalized teacher model. Specifically, the server learns a conditional distribution to extract knowledge about the global view of ground-truth data distributions and then uses the acquired knowledge as an inductive bias to enhance the generalization performance of local models. Additionally, the server calculates the knowledge contribution of each local model based on the similarity of the average data representation and aggregates a personalized teacher model that contains more positive transfer knowledge for each client. Experimental validation shows that the proposed scheme improves local test accuracy by up to 69.55%, 47.56%, and 18.76% on the Mnist, EMnist, and CelebA datasets, respectively, while reducing communication rounds across varying degrees of data heterogeneity compared to state-of-the-art schemes.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"317 ","pages":"Article 113502"},"PeriodicalIF":7.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834177","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

Cross-domain UAV pose estimation: A novel attempt in UAV visual localization

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-04-11 DOI: 10.1016/j.knosys.2025.113449

Wenhao Lin , Tao Liu , Kan Ren, Qian Chen

{"title":"Cross-domain UAV pose estimation: A novel attempt in UAV visual localization","authors":"Wenhao Lin , Tao Liu , Kan Ren, Qian Chen","doi":"10.1016/j.knosys.2025.113449","DOIUrl":"10.1016/j.knosys.2025.113449","url":null,"abstract":"<div><div>With the rapid advancement of depth estimation algorithms and continuous improvements in devices such as LiDAR and depth cameras, the acquisition of high-quality 3D models has become increasingly accessible. This progress opens up new opportunities for leveraging cross-domain matching between images and point clouds to estimate the pose of Unmanned Aerial Vehicles (UAVs) for visual localization. In this context, we propose a novel cross-domain descriptor that facilitates the fusion and matching of features across modalities. Building upon this approach, we designed a dual-branch UAV localization pipeline that incorporates an object detection strategy to extract more reliable feature points from the scene. Additionally, we constructed two new datasets specifically tailored for UAV-based aerial applications. The first dataset is manually annotated and focuses on training and evaluating object detection models from an aerial perspective, while the second dataset contains approximately 1.7 million 2D-3D correspondences from diverse scenarios, offering a rich collection of training and evaluation samples. Extensive experiments on public UAV datasets demonstrate that, compared to existing descriptors, our method not only achieves superior pose estimation accuracy through a coarse-to-fine image matching strategy but also enables robust pose estimation by directly matching images and point clouds to obtain accurate 2D-3D correspondences. Moreover, the incorporation of object detection strategies significantly enhances pose estimation accuracy and demonstrates increased resilience to interference in complex environments. Our datasets and code will be publicly available at <span><span>https://github.com/lwhhhh13/Cross-Domain-UAV-Pose-Estimation</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"317 ","pages":"Article 113449"},"PeriodicalIF":7.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830199","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

Federated feature reconstruction with collaborative star networks

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-04-10 DOI: 10.1016/j.knosys.2025.113463

Yihong Zhang , Yuan Gao , Maoguo Gong, Hao Li, Yuanqiao Zhang, Sijia Zhang

引用次数: 0

EAEFA-R: Multiple learning-based ensemble artificial electric field algorithm for global optimization

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-04-10 DOI: 10.1016/j.knosys.2025.113453

Dikshit Chauhan , Anupam Yadav , Rammohan Mallipeddi

{"title":"EAEFA-R: Multiple learning-based ensemble artificial electric field algorithm for global optimization","authors":"Dikshit Chauhan , Anupam Yadav , Rammohan Mallipeddi","doi":"10.1016/j.knosys.2025.113453","DOIUrl":"10.1016/j.knosys.2025.113453","url":null,"abstract":"<div><div>Adjusting the search behaviors of swarm-based algorithms is crucial for solving real-world optimization challenges. Researchers have developed ensemble strategies and self-adaptive mechanisms to enhance the optimization ability of individual algorithms by balancing global and local search capabilities. Inspired by these advancements, this paper proposes a physics-based artificial electric field algorithm with three improvement strategies and an attraction–repulsion operator (EAEFA-R) to enhance diversity and escape local optima. These strategies are probabilistically selected using a dynamic adaptation mechanism. The effectiveness of EAEFA-R is assessed through extensive analysis of exploration-exploitation dynamics and diversity, and it is evaluated on two real-parameter test suites, CEC 2017 and CEC 2022, across 10, 20, 30, 50, and 100-dimensional search spaces. Compared to fifteen state-of-the-art algorithms, including AEFA variants and other optimization algorithms, EAEFA-R demonstrates superior solution accuracy, convergence rate, search capability, and stability performance. The overall ranking highlights its exceptional potential for solving challenging optimization problems, outperforming other state-of-the-art algorithms across various dimensions. The MATLAB source code of EAEFA-R is available at <span><span>https://github.com/ChauhanDikshit</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"318 ","pages":"Article 113453"},"PeriodicalIF":7.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839244","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

Diachronic semantic encoding based on pre-trained language model for temporal knowledge graph reasoning

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-04-10 DOI: 10.1016/j.knosys.2025.113479

Yunteng Deng , Jia Song , Zhongliang Yang , Yilin Long , Li Zeng , Linna Zhou

{"title":"Diachronic semantic encoding based on pre-trained language model for temporal knowledge graph reasoning","authors":"Yunteng Deng , Jia Song , Zhongliang Yang , Yilin Long , Li Zeng , Linna Zhou","doi":"10.1016/j.knosys.2025.113479","DOIUrl":"10.1016/j.knosys.2025.113479","url":null,"abstract":"<div><div>Temporal Knowledge Graph Reasoning (TKGR) aims to infer missing facts at specific timestamps. However, most existing methods primarily focus on the local and global evolutionary characteristics of temporal knowledge graphs (TKG), often neglecting the inherent semantic information of historical facts. The oversight limits the understanding of the diachronic evolution of facts, thereby limiting the ability to predict future missing facts. To address these issues, we propose a TKGR model with <strong>D</strong>iachronic <strong>S</strong>emantic <strong>E</strong>ncoding based on a <strong>P</strong>re-trained language model, called <strong>DSEP</strong>. It uses a pre-trained language model (PLM) to learn the evolutionary characteristics of historical related facts of the entity or relation to be predicted, so as to enhance the understanding of historical facts by the graph encoder used to capture the local evolutionary characteristics of the temporal knowledge graph. Additionally, to further narrow the prediction scope, DSEP incorporates historical fact correlation matrix in its prediction results. Experimental results on four benchmark datasets demonstrate that DSEP significantly improves the performance of relation prediction in temporal knowledge graphs, with an average improvement of 20.9% in MRR<span><span><sup>1</sup></span></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"318 ","pages":"Article 113479"},"PeriodicalIF":7.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839242","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

Decentralized Contrastive Learning for generalized zero-shot image classification

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-04-10 DOI: 10.1016/j.knosys.2025.113466

Ya Chen , Zhihao Zhang , Pei Wang , Feng Tian

{"title":"Decentralized Contrastive Learning for generalized zero-shot image classification","authors":"Ya Chen , Zhihao Zhang , Pei Wang , Feng Tian","doi":"10.1016/j.knosys.2025.113466","DOIUrl":"10.1016/j.knosys.2025.113466","url":null,"abstract":"<div><div>Generalized zero-shot learning (GZSL) aims to learn a model on known classes that can adapt to a test set comprising both known and unknown classes. Recent GZSL research in image classification has made significant progress by utilizing representation learning techniques. However, the challenge of generating discriminative representations for fine-grained classes with close relevance remains unresolved. To address this problem, we introduce a Decentralized Contrastive Learning (DCL) framework that seamlessly integrates a nested Wasserstein GAN (WGAN) with decentralized contrastive representation learning. Our nested WGAN incorporates the representation learning module within the discriminator, enabling the model to simultaneously train the representations and differentiate them in a synergistic manner. Moreover, our decentralized contrastive learning module enhances the discriminative nature of representations by preserving calibration based on class information without additional parameters during training. We further provide theoretical analysis for DCL, uncovering its superiority in learning discriminative representations and its robustness in handling mixed features. Experiments on show that DCL outperforms the state-of-the-art models by margins of about 3%, 4% and 3% on CUB, SUN and aPY datasets.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"317 ","pages":"Article 113466"},"PeriodicalIF":7.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820548","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

CharFormer: Character-oriented attention network for string edit distance

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-04-10 DOI: 10.1016/j.knosys.2025.113482

Xijuan Liu , Haobo Wei , Peilun Yang , Haiyang Hu

{"title":"CharFormer: Character-oriented attention network for string edit distance","authors":"Xijuan Liu , Haobo Wei , Peilun Yang , Haiyang Hu","doi":"10.1016/j.knosys.2025.113482","DOIUrl":"10.1016/j.knosys.2025.113482","url":null,"abstract":"<div><div>String similarity computation plays a crucial role in numerous real-world applications, such as similarity search and sequence alignment. String Edit Distance (SED) is a representative similarity metric that effectively measures the similarity between strings. However, its quadratic complexity makes the computation of SED challenging, especially for large datasets. Consequently, in recent years, an increasing number of algorithms have adopted deep learning techniques to accelerate SED computation. However, we observe that existing methods often employ a bi-encoder framework to learn the features of individual strings, which leads to neglect of the matching information across strings. Moreover, these methods fail to fully leverage subsequence information and the sampling space. To this end, we propose a character-oriented attention network named CharFormer to learn the computation of SED. Specifically, CharFormer operates at the character granularity, leveraging both the intra-sequence information of individual input strings and the inter-sequence information between them to learn the representations of characters and strings. Subsequently, CharFormer uses two prediction heads to simultaneously utilize these two types of information to predict the similarity between strings. Additionally, we incorporate the similarity between substrings to provide extra supervision and design a novel sampling method to fully exploit the sampling space. Extensive experiments demonstrate the superiority of CharFormer over state-of-the-art algorithms and the efficacy of the proposed methods.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"318 ","pages":"Article 113482"},"PeriodicalIF":7.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843953","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

FactorVQVAE: Discrete latent factor model via Vector Quantized Variational Autoencoder

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-04-10 DOI: 10.1016/j.knosys.2025.113460

Namhyoung Kim, Seung Eun Ock, Jae Wook Song

{"title":"FactorVQVAE: Discrete latent factor model via Vector Quantized Variational Autoencoder","authors":"Namhyoung Kim, Seung Eun Ock, Jae Wook Song","doi":"10.1016/j.knosys.2025.113460","DOIUrl":"10.1016/j.knosys.2025.113460","url":null,"abstract":"<div><div>This study introduces FactorVQVAE, the first integration of the Vector Quantized Variational Autoencoder (VQVAE) into factor modeling, providing a novel framework for predicting cross-sectional stock returns and constructing systematic investment portfolios. The model employs a two-stage architecture to improve the extraction and utilization of latent financial factors. In the first stage, an encoder–decoder-quantizer compresses high-dimensional input data into discrete latent factors through vector quantization, addressing posterior collapse and ensuring distinct representations. In the second stage, an autoregressive Transformer captures sequential dependencies among these latent factors, enabling precise return predictions. Empirical results in the CSI300 and S&P500 markets demonstrate FactorVQVAE’s superior performance. The model achieves the best Rank IC and Rank ICIR scores, surpassing the state-of-the-art latent factor models in varying market conditions. In portfolio evaluations, FactorVQVAE consistently excels in both Top-<span><math><mi>k</mi></math></span> Drop-<span><math><mi>n</mi></math></span> and Long–Short strategies, translating predictive accuracy into robust investment performance. In particular, it delivers the highest risk-adjusted returns, highlighting its ability to balance returns and risks effectively. These findings position FactorVQVAE as a significant advancement in integrating modern deep learning methodologies with financial factor modeling. Its adaptability, robustness, and exceptional performance in portfolio investment establish it as a promising tool for systematic investing and financial analytics.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"318 ","pages":"Article 113460"},"PeriodicalIF":7.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835326","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