Knowledge-Based Systems最新文献

{"title":"Effective Generative Replay with Strong Memory for Continual Learning","authors":"Jing Yang ,&nbsp;Xinyu Zhou ,&nbsp;Yao He ,&nbsp;Qinglang Li ,&nbsp;Zhidong Su ,&nbsp;Xiaoli Ruan ,&nbsp;Changfu Zhang","doi":"10.1016/j.knosys.2025.113477","DOIUrl":"10.1016/j.knosys.2025.113477","url":null,"abstract":"<div><div>Continual learning enables artificial neural networks (ANNs) to recognize samples derived from unknown classes while maintaining high classification accuracy for known classes. A classic continual learning approach involves storing data acquired from previously learned tasks and replaying it alongside new tasks in subsequent training sessions. However, data storage may not be feasible due to privacy or security concerns. To address this issue, we propose a effective approach for retaining a strong memory of past tasks within the utilized model. Our method integrates visual saliency-based feature enhancement with a generative replay strategy that captures past task information using visual saliency cues. Specifically, we integrate an adaptive sparse convolutional network module into a generative model, where adaptive sparse convolutional layers select task-relevant features and reduce the number of redundant computations and storage. Experiments show that our method reduces computational overhead by approximately 8% compared to the baseline method. Additionally, since sparse convolution can lead to the loss of global contextual information, we incorporate a bottleneck attention module to improve the feature representations, resulting in an accuracy improvement of the model in the CIFAR-100 task from 26.90% to 27.50%. Finally, to classify unobserved data not included in the training set, we introduce an adaptive mask (AM) module. In the CIFAR-100 20-stage task, the model accuracy improved from 16.05% (ASC only) to 20.31%, and the number of parameter calculations is reduced by 5.1%. This method effectively addresses data retention challenges while enhancing performance and provides a promising solution for privacy-preserving continual learning.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"319 ","pages":"Article 113477"},"PeriodicalIF":7.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881444","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}

{"title":"Positional trace encoding for next activity prediction in event logs","authors":"Antonio Pellicani ,&nbsp;Michelangelo Ceci","doi":"10.1016/j.knosys.2025.113544","DOIUrl":"10.1016/j.knosys.2025.113544","url":null,"abstract":"<div><div>The analysis of log data, generated by running processes in many application domains, enables organizations to identify opportunities for operational improvements. For instance, in healthcare, analyzing patient treatment logs can optimize care pathways; in manufacturing, production line logs can reveal bottlenecks; and in customer service, ticket resolution logs can streamline response protocols. One key analytical task is predicting the next activity in a process, which supports operational decision-making through better resource allocation and proactive response to customer needs. In this paper, we solve the next activity prediction task by exploiting a novel positional encoding approach that is based on sliding windows. This approach allows us to consider both a way to adapt to changes in the data distribution, and exploit positional information of the activities in the traces. The method proposed in this paper, called OREO, takes into account these aspects through a positional encoding tightly coupled with specific types of deep neural network architectures. The results on eight real-world process logs show the superiority of the models exploiting OREO encoding over state-of-the-art approaches, confirming our initial intuition of benefits gained by combining a time-window based model with positional information.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"319 ","pages":"Article 113544"},"PeriodicalIF":7.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881445","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}

{"title":"Provably bounded prompting prior network for universal compressed sensing magnetic resonance imaging","authors":"Baoshun Shi ,&nbsp;Zheng Liu ,&nbsp;Kexun Liu ,&nbsp;Yueming Su","doi":"10.1016/j.knosys.2025.113485","DOIUrl":"10.1016/j.knosys.2025.113485","url":null,"abstract":"<div><div>Compressed sensing magnetic resonance imaging (CSMRI) aims to reconstruct MR images from undersampled <span><math><mi>k</mi></math></span>-space data. Existing deep unrolling CSMRI methods unfold iterative algorithms into deep neural networks, demonstrating superior reconstruction performance. However, they still face several limitations: (<span><math><mi>i</mi></math></span>) The prior networks used in deep unrolling methods are often empirically designed, lacking interpretability and hindering further theoretical analysis. (<span><math><mrow><mi>i</mi><mi>i</mi></mrow></math></span>) These methods require training for each sampling setting (e.g. sampling mode and sampling ratio), which incurs significant storage costs. To address these challenges, we propose PDSNet, a network inspired by a double sparsity model, which is both provable and interpretable. As a prior network, PDSNet is integrated into a deep unrolling framework to solve the universal CSMRI task. This enables our method to use a single model to address the compressed sensing MRI problem across various sampling settings. Specifically, PDSNet is built on a double sparsity model using tight frames, and the thresholds for shrinking frame coefficients are adaptively generated by a dedicated threshold-generating sub-network (TGNet). In TGNet, we introduce an information fusion module that effectively captures both global and regional features. Additionally, a prompt block is designed to learn discriminative information across different sampling settings, enabling high-quality reconstructions for each setting using a single model. Experimental results demonstrate that our method achieves superior reconstruction performance. On the theoretical side, we provide explicit proof that PDSNet satisfies bounded properties and further show that the corresponding iterative algorithm converges to a fixed point.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"319 ","pages":"Article 113485"},"PeriodicalIF":7.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892108","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}

{"title":"A graph-guided network with adaptive evaluation and improvement for disturbed sensors in fault-tolerant soft sensor modeling","authors":"Liyuan Kong ,&nbsp;Chunjie Yang ,&nbsp;Siwei Lou ,&nbsp;Yaoyao Bao ,&nbsp;Xiaoke Huang ,&nbsp;Li Chai","doi":"10.1016/j.knosys.2025.113497","DOIUrl":"10.1016/j.knosys.2025.113497","url":null,"abstract":"<div><div>Operating in harsh environments, sensors frequently encounter disturbances, causing prevalent deviations and drift in measured values from true values. The disturbed measurement brings extra difficulty for soft sensing, since the performance of most existing methods depends heavily on the assumption that the data is accurate and disturbance-free. Considering the above difficulty, this paper proposes a graph-guided network with adaptive evaluation and improvement (GAEI) to achieve fault-tolerant soft sensor modeling. First, an adaptive evaluation strategy is proposed to calculate sensor reliability, which is developed from two aspects. For instantaneous noise, a pointwise analysis considering the intra-variable temporal dependencies is performed. For continuous drift, the graph structure comparison that reflects the inter-variable dependencies is established, which can deal with additive deviation, static multiplicative deviation, and time-varying multiplicative deviation. Second, a specific message passing operator is designed within a graph neural network. It aims to effectively exploit information from trusted variables, thereby improving the quality of various deviations. Finally, the evaluation and improvement have an end-to-end structure, providing an adaptive solution to reduce the influence of disturbances. The effectiveness of GAEI is sufficiently demonstrated in a real cement production process.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"318 ","pages":"Article 113497"},"PeriodicalIF":7.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878872","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}

{"title":"An efficient uncertainty measure with dynamic update mechanisms","authors":"Yingying Sun ,&nbsp;Jusheng Mi","doi":"10.1016/j.knosys.2025.113572","DOIUrl":"10.1016/j.knosys.2025.113572","url":null,"abstract":"<div><div>With the extensive adoption of information technology, the data we encounter today is frequently dynamic and subject to change over time. To facilitate timely decision-making, it is crucial to possess a measure that can swiftly identify and continuously update the inherent uncertainty present in the data. In this paper, we present a measure of weighted uncertainty, referred to as WCE, and investigate methods for its dynamic updating within information systems. Initially, the granularity of the universe is established based on binary relations derived from each attribute, which is subsequently utilized to assign weights. Following this, we employ conditional entropy to assess the uncertainty level of the target concept concerning each attribute. Ultimately, the overall uncertainty of the information system is computed by weighting the uncertainty associated with each attribute. To enhance the intuitiveness and simplicity of dynamic updates for weighted uncertainty more intuitive and straightforward, we transform the WCE into matrix form. We then delve into the dynamic updating mechanism, examining how the core matrices are modified in response to variations in data volume or attributes. Finally, numerical experiments conducted on UCI datasets demonstrate that the proposed WCE measure is responsive to diverse data changes. Its updating approach for dynamic information systems can significantly reduce time consumption.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"318 ","pages":"Article 113572"},"PeriodicalIF":7.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874098","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}

{"title":"Action-Prompt: A unified visual prompt and fusion network for enhanced video action recognition","authors":"Linxi Li ,&nbsp;Mingwei Tang ,&nbsp;Shiqi Qing ,&nbsp;Yanxi Zheng ,&nbsp;Jie Hu ,&nbsp;Mingfeng Zhao ,&nbsp;Si Chen","doi":"10.1016/j.knosys.2025.113547","DOIUrl":"10.1016/j.knosys.2025.113547","url":null,"abstract":"<div><div>Video action recognition is a crucial task in video understanding and has garnered significant attention from researchers. However, while most existing methods exploit spatiotemporal and motion features for action recognition, these methods fail to consider that the fusion of different features cannot fully adapt to this task. To address this issue, we designed a prompt block named the Prompt Learning Layer (PLL), which is a plug-and-play module that can be inserted into a backbone to learn visual prompts for action recognition tasks. Additionally, we propose the Spatio-Temporal and Motion Fusion Module (STMF), which utilizes innovative extraction and fusion strategies to enhance the complementarity between the different features. The STMF comprises two main modules: the Bidirectional Motion Difference Module (BiMDM), which deals with bidirectional motion features, and the Spatio-Temporal Adaptive Module (STAM), which deals with spatio-temporal features in an adaptive approach. Finally, the experimental results demonstrate that our proposed method outperforms the state-of-the-art performance on the Kinetics-400, Something–Something V1 and V2 datasets.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"318 ","pages":"Article 113547"},"PeriodicalIF":7.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870742","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}

{"title":"A low-dimensional cross-attention model for link prediction with applications to drug repurposing","authors":"Geng-jing Chen ,&nbsp;Gong-de Guo ,&nbsp;S. Lorraine Martin ,&nbsp;Hui Wang","doi":"10.1016/j.knosys.2025.113562","DOIUrl":"10.1016/j.knosys.2025.113562","url":null,"abstract":"<div><div>Link prediction, a key technique for knowledge graph completion, has advanced with transformer-based encoders utilizing high-dimensional embeddings and self-attention mechanisms. However, these approaches often result in models with excessive parameters, poor scalability, and substantial computational demands, limiting their practical applicability. To address these limitations, this paper introduces a low-dimensional link prediction model that leverages cross-attention for improved efficiency and scalability. Our approach employs low-dimensional embeddings to capture essential, non-redundant information about entities and relations, significantly reducing computational and memory requirements. Unlike self-attention, which models interactions within a single set of embeddings, cross-attention in our model captures complex interactions between entities and relations in a compact, low-dimensional space. Additionally, a streamlined decoding method simplifies computations, reducing processing time without compromising accuracy. Experimental results show that our model outperforms most state-of-the-art link prediction models on two public datasets, WN18RR and FB15k-237. Compared to these top-performing methods, our model contains only 18.1 % and 25.4 % of the parameters of these comparable models, while incurring a performance loss of merely 2.4 % and 3.1 %, respectively. Furthermore, it achieves an average 72 % reduction in embedding dimensions compared to five leading models. A case study on drug repurposing further illustrates the model's potential for real-world applications in knowledge graph completion.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"319 ","pages":"Article 113562"},"PeriodicalIF":7.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892104","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}

{"title":"HiLINK: Hierarchical linking of context-aware knowledge prediction and prompt tuning for bilingual knowledge-based visual question answering","authors":"Hyeonki Jeong ,&nbsp;Taehyeong Kim ,&nbsp;Wooseok Shin,&nbsp;Sung Won Han","doi":"10.1016/j.knosys.2025.113556","DOIUrl":"10.1016/j.knosys.2025.113556","url":null,"abstract":"<div><div>Knowledge-based visual question answering (KBVQA) is a representative visual reasoning task that leverages external knowledge for question answering in situations where predicting the correct answer using only image and query data is difficult. In addition to KBVQA, various visual reasoning tasks have been actively studied for their potential to improve visual understanding by combining text and image modalities effectively. However, these tasks have primarily focused on high-resource languages, such as English. In contrast, studies on low-resource languages remain comparatively rare. To mitigate this research gap, we propose HiLINK, which utilizes multilingual data to enhance KBVQA performance in various languages. In this study, we use the BOK-VQA dataset to design the following key methodologies: We propose an end-to-end model that eliminates the need for a knowledge graph embedding-based training network by learning relationships between triplet knowledge components within prompts directly using Link-Tuning. We propose the HK-TriNet and HK-TriNet+ methodologies to perform triplet prediction based on contextualized knowledge relationships. Finally, we apply the frozen training approach as an alternative to conventional encoder joint training to improve the efficiency and performance of bilingual learning. HiLINK exhibits outstanding performance on the BOK-VQA dataset in three language configurations: bilingual, English, and Korean, outperforming the GEL-VQA method by +19.40%, +12.01%, and +11.30%, respectively. Furthermore, the effectiveness of the proposed method is validated based on a comprehensive analysis of bilingual embedding spaces, both visually and numerically. We expect this study to inspire future research on this topic and encourage practical applications of improved vision-language models.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"319 ","pages":"Article 113556"},"PeriodicalIF":7.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886861","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}

{"title":"Geo-FuB: A method for constructing an Operator-Function knowledge base for geospatial code generation with large language models","authors":"Shuyang Hou,&nbsp;Anqi Zhao,&nbsp;Jianyuan Liang,&nbsp;Zhangxiao Shen,&nbsp;Huayi Wu","doi":"10.1016/j.knosys.2025.113624","DOIUrl":"10.1016/j.knosys.2025.113624","url":null,"abstract":"<div><div>The rapid growth of spatiotemporal data and the increasing demand for geospatial modeling have driven the automation of these tasks with large language models (LLMs) to enhance research efficiency. However, general LLMs often encounter hallucinations when generating geospatial code due to a lack of domain-specific knowledge on geospatial functions and related operators. The retrieval-augmented generation (RAG) technique, integrated with an external operator-function knowledge base, provides an effective solution to this challenge. To date, no widely recognized framework exists for building such a knowledge base. This study presents a comprehensive framework for constructing the operator-function knowledge base, leveraging semantic and structural knowledge embedded in geospatial scripts. The framework consists of three core components: Function Semantic Framework Construction (Geo-FuSE), Frequent Operator Combination Statistics (Geo-FuST), and Combination and Semantic Framework Mapping (Geo-FuM). Geo-FuSE employs techniques like Chain-of-Thought (CoT), TF-IDF, t-SNE, and Gaussian Mixture Models (GMM) to extract semantic features from scripts; Geo-FuST uses Abstract Syntax Trees (AST) and the Apriori algorithm to identify frequent operator combinations; Geo-FuM combines LLMs with a fuzzy matching algorithm to align these combinations with the semantic framework, forming the Geo-FuB knowledge base. The instance of Geo-FuB, named GEE-FuB, has been developed using 154,075 Google Earth Engine scripts and is available at <span><span>https://github.com/whuhsy/GEE-FuB</span><svg><path></path></svg></span>. Based on a set of well-defined evaluation metrics introduced in this study, the GEE-FuB construction achieved an overall accuracy of 88.89 %, demonstrating a 31 % to 34 % reduction in hallucinations compared to mainstream LLMs without external knowledge integration. This research introduces a novel approach to knowledge mining and knowledge base construction specifically tailored for geospatial code generation tasks, broadening the applications of knowledge base construction and providing valuable theoretical insights, practical examples, and data resources for related research fields.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"319 ","pages":"Article 113624"},"PeriodicalIF":7.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892106","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}

{"title":"DAPEW: Towards robust collaborative filtering with graph contrastive learning","authors":"Kuiyu Zhu ,&nbsp;Tao Qin ,&nbsp;Haoxing Liu ,&nbsp;Chenxu Wang ,&nbsp;Pinghui Wang","doi":"10.1016/j.knosys.2025.113570","DOIUrl":"10.1016/j.knosys.2025.113570","url":null,"abstract":"<div><div>Graph Contrastive Learning (GCL) has shown excellent performance in Collaborative Filtering (CF), one of the most widely used techniques in efficient recommender systems. However, existing GCL-based CF methods suffer from node degree disparity, feature oversmoothing, difficulty in distinguishing hard negative samples, and semantic loss. To address these problems, this paper proposes a novel graph contrastive learning method for robust CF, named <strong>D</strong>egree-<strong>A</strong>ware <strong>P</strong>ropagation and <strong>E</strong>ntropy-<strong>W</strong>eighted contrastive loss (DAPEW). DAPEW introduces a degree-aware propagation mechanism to dynamically adjust the influence of initial embeddings, adjacency matrix products, and degree matrix products on the final embeddings, which can effectively handle node degree disparity and alleviate feature oversmoothing. DAPEW also designs an entropy-weighted contrastive loss, which introduces entropy weights to better distinguish hard negative samples and enhance the model’s discriminative ability and robustness. Experimental results show that DAPEW outperforms the existing GCL-based CF methods on several real-world datasets. Compared with existing GCL-based methods, DAPEW improves Recall@40 and NDCG@40 by 0.24%<span><math><mo>∼</mo></math></span>25.88% and 0.14%<span><math><mo>∼</mo></math></span>26.18% across four different datasets, respectively.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"318 ","pages":"Article 113570"},"PeriodicalIF":7.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878873","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}