Knowledge-Based Systems最新文献

{"title":"KG-prompt: Interpretable knowledge graph prompt for pre-trained language models","authors":"Liyi Chen ,&nbsp;Jie Liu ,&nbsp;Yutai Duan ,&nbsp;Runze Wang","doi":"10.1016/j.knosys.2025.113118","DOIUrl":"10.1016/j.knosys.2025.113118","url":null,"abstract":"<div><div>Knowledge graphs (KGs) can provide rich factual knowledge for language models, enhancing reasoning ability and interpretability. However, existing knowledge injection methods usually ignore the structured information in KGs. Using structured knowledge to enhance pre-trained language models (PLMs) still has a set of challenging issues, including resource consumption of knowledge retraining, heterogeneous information, and knowledge noise. To address these issues, we explore how to flexibly inject structured knowledge into frozen PLMs. Inspired by prompt learning, we propose a novel method <strong>K</strong>nowledge <strong>G</strong>raph <strong>Prompt</strong> (KG-Prompt), which for the first time encodes the KG as structured prompts to enhance the knowledge expression ability of PLMs. KG-Prompt consists of a compressed subgraph construction module and a KG prompt generation module. In the compressed subgraph construction module, we construct compressed subgraphs based on a path-weighting strategy to reduce knowledge noise. In the KG prompt generation module, we propose a multi-hop consistency optimization strategy to learn the representation of compressed subgraphs, and then generate KG prompts based on a knowledge mapper to solve the heterogeneous information problem. The KG prompts can be inserted into the input of PLMs expediently, which decouples from PLMs and the downstream model without knowledge retraining and reduces computational resources. Extensive experiments on three knowledge-driven natural language understanding tasks demonstrate that our approach effectively improves the knowledge reasoning ability of PLMs. Furthermore, we provide a detailed analysis of different KG prompts and discuss the interpretability and generalizability of the proposed method.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"311 ","pages":"Article 113118"},"PeriodicalIF":7.2,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377557","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":"MGAN-LD: A sparse label propagation-based anomaly detection approach using multi-generative adversarial networks","authors":"Shuyu Li,&nbsp;Wen Chen,&nbsp;Kaiyan Xing,&nbsp;Hongchao Wang,&nbsp;Yilin Zhang,&nbsp;Ming Kang","doi":"10.1016/j.knosys.2025.113124","DOIUrl":"10.1016/j.knosys.2025.113124","url":null,"abstract":"<div><div>Learning with synthetic data for anomaly detection has attracted a lot of attention. Recent works attempted to utilize generative adversarial networks (GANs) to generate pseudo-labeled synthetic samples for the model’s learning process. However, in real applications, the sparsity of originally labeled training samples leads to a model collapsing problem, such that most of the pseudo-labeled samples synthesized by GANs are crowded in a small area, resulting in the difficulty for GANs in learning the spatial distribution of samples. In this paper, we proposed a sparse label propagation-based anomaly detection approach using the multi-generators dual-discriminator framework (MGAN-LD). Firstly, DBSCAN clustering is utilized to assign samples to different clusters. Then, to expand the labeled training set, label propagation processes are carried out in each cluster to generate highly-credible pseudo-labels for unlabeled samples. Furthermore, a novel GAN with multiple generators is trained to simultaneously learn the local data distribution of different areas in the feature space based on the expanded training set to avoid the model collapsing. Finally, the training set is further augmented by synthetic samples from multiple generators of MGAN-LD, and the set is employed to train an overall discriminator. Benefiting from the data augmentation, MGAN-LD can build reliable classification boundaries between normal and abnormal samples. MGAN-LD is evaluated against nine classical anomaly detection methods on 11 public datasets. The results show that MGAN-LD improves the AUC metrics by an average of 10%, the AP metrics by an average of 17%, and the F1 metrics by an average of 15% compared with other classical methods.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"312 ","pages":"Article 113124"},"PeriodicalIF":7.2,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403506","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":"Learning discriminative topological structure information representation for 2D shape and social network classification via persistent homology","authors":"Changshuo Wang ,&nbsp;Rongsheng Cao ,&nbsp;Ruiping Wang","doi":"10.1016/j.knosys.2025.113125","DOIUrl":"10.1016/j.knosys.2025.113125","url":null,"abstract":"<div><div>Extracting topological structure information from data sources such as images and social networks remains a significant challenge. Drawing inspiration from the theory of topological structure in visual perception (topological perception theory), this study employs topological data analysis (TDA) to extract topological information, which is typically represented using persistence diagrams. To facilitate end-to-end learning, we introduce a topological set network (TSNet) that transforms topological information into vector representations through mixed entropy and self-attention mechanisms. Our approach first applies persistent homology to extract topological structure information from the data, followed by a 45-degree clockwise rotation of this information. We then design a topological set layer (TS-Layer) that creates vectorized representations by encoding persistence diagrams into topological set blocks (TS-Blocks) with diverse distributions. We provide theoretical proof that the TS-Layer maintains stability under input perturbations. To further enhance the discriminative power of the encoded topological features, we incorporate a residual attention layer (RA-Layer). Experimental results demonstrate that our proposed approach achieves superior performance compared to recent state-of-the-art methods. Specifically, our method achieves accuracy improvements of 1.2% (75.8% vs. 74.6%) and 1.7% (94.4% vs. 92.7%) on the Animal and MPEG-7 datasets respectively compared to the best existing methods. For social network classification tasks, our approach demonstrates improvements of 1.0% (55.9% vs. 54.9%) and 2.1% (48.5% vs. 46.4%) on the reddit-5k and reddit-12k datasets respectively, validating the effectiveness of our topological feature extraction and vectorization approach.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"311 ","pages":"Article 113125"},"PeriodicalIF":7.2,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395302","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":"From multi-scale grids to dynamic regions: Dual-relation enhanced transformer for image captioning","authors":"Wei Zhou ,&nbsp;Chuanle Song ,&nbsp;Dihu Chen ,&nbsp;Tao Su ,&nbsp;Haifeng Hu ,&nbsp;Chun Shan","doi":"10.1016/j.knosys.2025.113127","DOIUrl":"10.1016/j.knosys.2025.113127","url":null,"abstract":"<div><div>The purpose of image captioning is to describe the visual content of an image in an accurate and natural sentence. Some previous methods adopt convolutional networks to encode grid-level features, whereas others use an object detector to extract region-level features. However, the spatial resolution of high-level grid features is typically low, thus capturing small-scale objects is challenging for such models. In addition, most region-based methods directly set the same number of regions to represent all images, failing to account for varying scene complexities across different images. They introduce noise in region relationship modeling and disrupt sentence reasoning. To address these issues, we propose a novel <strong>D</strong>ual-<strong>R</strong>elation <strong>E</strong>nhanced <strong>T</strong>ransformer (<strong>DRET</strong>) model that complements the advantages of multi-scale grid and dynamic region features. In the encoding phase, we first apply multiple sampling strategies to generate multi-scale grid features, then design a novel multi-scale grid attention (MGA) encoder that learns the relationships between features at different scales. Meanwhile, a new dynamic region selection (DRS) encoder is devised to dynamically select an appropriate number of regions based on the scene complexity of each input image, effectively pruning redundant regions and enhancing correlations between selected regions. In the decoding stage, we combine the advantages of grid and region features, using a cross-modal adaptive gating (CAG) decoder that automatically determines the gate weights of the two visual features at each time step. Extensive experiments on MS-COCO and Flickr30K show that our model achieves better performance compared to current methods.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"311 ","pages":"Article 113127"},"PeriodicalIF":7.2,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395299","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":"AutoPolCNN: A neural architecture search method of convolutional neural network for PolSAR image classification","authors":"Guangyuan Liu ,&nbsp;Yangyang Li ,&nbsp;Yanqiao Chen ,&nbsp;Ronghua Shang ,&nbsp;Licheng Jiao","doi":"10.1016/j.knosys.2025.113122","DOIUrl":"10.1016/j.knosys.2025.113122","url":null,"abstract":"<div><div>Convolutional neural networks (CNNs), as a kind of typical classification model known for good performance, have been utilized to cope with polarimetric synthetic aperture radar (PolSAR) image classification. Nevertheless, the performances of CNNs highly rely on well-designed network architectures and there is no theoretical guarantee on how to design them. As a result, the architectures of CNNs can be only designed by human experts or by trial and error, which makes the architecture design is annoying and time-consuming. So, a neural architecture search (NAS) method of CNN called AutoPolCNN, which can determine the architecture automatically, is proposed in this paper. Specifically, we firstly design the search space which covers the main components of CNNs like convolution and pooling operators. Secondly, considering the fact that the number of layers can also influence the performance of CNN, we propose a super normal module (SNM), which can dynamically adjust the number of network layers according to different datasets in the search stage. Finally, we develop the loss function and the search method for the designed search space. Via AutoPolCNN, preparing the data and waiting for the classification results are enough. Experiments carried out on three PolSAR datasets prove that the architecture can be automatically determined by AutoPolCNN within an hour (<em>at least 10 times faster than existing NAS methods</em>) and has higher overall accuracy (OA) than state-of-the-art (SOTA) PolSAR image classification CNN models.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"312 ","pages":"Article 113122"},"PeriodicalIF":7.2,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403505","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":"Escape velocity-based adaptive outlier detection algorithm","authors":"Juntao Yang ,&nbsp;Lijun Yang ,&nbsp;Dongming Tang ,&nbsp;Tao Liu","doi":"10.1016/j.knosys.2025.113116","DOIUrl":"10.1016/j.knosys.2025.113116","url":null,"abstract":"<div><div>Outlier detection is a pivotal technique within the realm of data mining, serving to pinpoint aberrant values nestled within datasets. It has been widely employed across diverse domains, including detection of credit card frauds, identification of seismic activities, and identification of anomalies within image datasets. However, existing approaches still face three shortcomings: (1) they often struggle with the intricacies of parameter selection and the vexing top-n dilemma, (2) they lack in their capacity to discern local outliers, and (3) their algorithmic efficacies markedly wane as datasets burgeon in sample point size and outlier prevalence. In addressing these formidable hurdles, we propose a novel, <strong>E</strong>scape <strong>V</strong>elocity-based adaptive <strong>O</strong>utlier <strong>D</strong>etection algorithm, noted as EVOD. The EVOD algorithm calculates the escape velocity of each data sample point and automatically detects the number of outliers by monitoring peak fluctuations in the growth rate of escape velocities of sample points, thereby solving the top-n problem suffered by existing outlier detection algorithms. Experimental results demonstrate that our algorithm, without requiring manual adjustment of parameters, can simultaneously detect global outliers, local outliers, and outlier clusters. In addition, it maintains a good performance even as the number of sample points and outliers in the dataset increases, particularly for complex manifold datasets.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"311 ","pages":"Article 113116"},"PeriodicalIF":7.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377686","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 cluster chaotic optimization for solving power loss and voltage profiles problems on electrical distribution networks","authors":"Primitivo Diaz,&nbsp;Eduardo H. Haro,&nbsp;Omar Avalos,&nbsp;Nayeli Perez","doi":"10.1016/j.knosys.2025.113145","DOIUrl":"10.1016/j.knosys.2025.113145","url":null,"abstract":"<div><div>The growing demand for electricity poses significant challenges in maintaining a reliable and efficient power supply. Optimal Capacitor Placement (OCP) in electrical engineering addresses this issue by strategically positioning capacitor banks within constrained Radial Distribution Networks (RDNs). Traditional optimization methods often struggle with this problem; alternative approaches, such as metaheuristic algorithms, present promising solutions. Despite advances in optimization techniques, challenges in achieving optimal solutions continue. To address these challenges, recent hybrid computational methods, such as the cluster chaotic optimization (CCO) algorithm, have emerged to enhance stability and robustness in finding optimal solutions. The effectiveness of the CCO algorithm lies in its combination of Evolutionary Computation (EC) and Machine Learning (ML) approaches. These approaches improve the search strategy by leveraging information extracted from the solution landscape, resulting in high performance in discovering optimal solutions. In this context, this work aims to utilize the strengths of the CCO algorithm to solve real-world challenges and evaluate its potential in addressing the OCP. The CCO algorithm was tested on three benchmark RDNs to assess its efficacy. Results were compared with those obtained from classical and recently developed methods and analyzed using non-parametric tests. The findings indicate that the CCO algorithm is competitive and robust in solving the OCP, outperforming similar strategies, and demonstrates its effectiveness in optimizing complex real-world problems in electrical engineering.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"311 ","pages":"Article 113145"},"PeriodicalIF":7.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395162","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":"Multi-task dual-level adversarial transfer learning boosted RUL estimation of CNC milling tools","authors":"Pei Wang ,&nbsp;Jinrui Liu ,&nbsp;Jingshuai Qi ,&nbsp;Kesong Zhou ,&nbsp;Hongbo Zhai","doi":"10.1016/j.knosys.2025.113152","DOIUrl":"10.1016/j.knosys.2025.113152","url":null,"abstract":"<div><div>Effectively estimating the remaining useful life (RUL) of milling tools is crucial for intelligent preventive maintenance of CNC milling systems. In this paper, a novel generalized RUL estimation model based on multi-task dual-level adversarial transfer learning with multi-level attention (MTDTL-MA) is proposed for tool RUL prediction with variable working conditions. A multi-task learning structure with multi-level attention is used to predict the wear of each tool face in parallel and capture the max wear of entire tools as a health index for more accurate RUL estimation. Multi-channel encoder-decoder self-attention, multi-gate attention and global-local adversarial transferable attention are integrated to emphasize useful wear-related features, tool face-specific features and transferable features between source and target domains, respectively. A new auxiliary subdomain adversarial domain adaptation and global-local adversarial transferable attention is proposed to form a dual-level adversarial domain adaptation to synergistically improve transfer learning. Both the PHM2010 and Ideahouse dataset (2021) are employed to verify the effectiveness of MTDTL-MA, and the results indicate that the proposed method provides higher RUL prediction accuracy compared to several state-of-the-art methods.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"312 ","pages":"Article 113152"},"PeriodicalIF":7.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403507","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":"ANOGAT-Sparse-TL: A hybrid framework combining sparsification and graph attention for anomaly detection in attributed networks using the optimized loss function incorporating the Twersky loss for improved robustness","authors":"Wasim Khan ,&nbsp;Nadhem Ebrahim","doi":"10.1016/j.knosys.2025.113144","DOIUrl":"10.1016/j.knosys.2025.113144","url":null,"abstract":"<div><div>In recent years, the identification of abnormalities in attributed networks has become essential for applications including social media analysis, cybersecurity, and financial fraud detection. Unsupervised graph anomaly detection techniques seek to recognize infrequent and anomalous patterns in graph-structured data without the necessity of labelled instances. Conventional methods employing Graph Neural Networks (GNNs) frequently encounter difficulties, especially due to the transmission of noisy edges and the intrinsic intricacy of node interrelations. To overcome these restrictions, we introduce ANOGAT-Sparse-TL, an innovative hybrid framework that integrates graph sparsification and Graph Attention Networks (GAT) with autoencoder-based reconstruction for anomaly detection in attributed networks. The sparsification procedure removes extraneous edges and highlights significant node connections, thereby enhancing computational efficiency and improving anomaly detection efficacy. By including GAT, our model carefully allocates significance to pertinent neighboring nodes, yielding enhanced node embeddings. The autoencoder subsequently reconstructs these embeddings to detect abnormalities via reconstruction errors. Incorporating Tversky Loss in the reconstruction process further improves the robustness of the model by effectively addressing the imbalance between normal and anomalous data, prioritizing the detection of rare anomalies. This optimized loss function allows ANOGAT-Sparse-TL to focus on hard-to-reconstruct instances, which are typically indicative of anomalies, and reduces the impact of noisy data on the model's performance. ANOGAT-Sparse-TL effectively integrates attribute-based and structural anomalies, yielding comprehensive anomaly ratings. Comprehensive studies on the four real-world datasets indicate that our strategy surpasses current state-of-the-art methodologies, with enhanced performance. Moreover, the scalability of our methodology guarantees its relevance to extensive real-world networks, rendering it an adaptable option for diverse graph anomaly detection activities. ANOGAT-Sparse-TL, despite its complexity, maintains computational efficiency and provides substantial improvements in anomaly detection inside attributed networks. Future research may concentrate on enhancing interpretability and broadening generalizability to various network architectures.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"311 ","pages":"Article 113144"},"PeriodicalIF":7.2,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clustering matrix regularization guided hierarchical graph pooling","authors":"Zidong Wang ,&nbsp;Liu Yang ,&nbsp;Tingxuan Chen ,&nbsp;Jun Long","doi":"10.1016/j.knosys.2025.113108","DOIUrl":"10.1016/j.knosys.2025.113108","url":null,"abstract":"<div><div>Hierarchical graph pooling effectively captures hierarchical structural information by iteratively simplifying the input graph into smaller graphs using a pooling function, which has demonstrated superior performance in graph-level tasks. However, existing methods often lack a detailed analysis of the pooling function, leading to issues such as noise, loss of essential information, and difficulties in balancing the retention and removal of graph details. In this paper, we address these challenges from an information theory perspective by analyzing information transmission through the clustering matrix within the pooling function. We introduce a novel approach, CMRGP, which is guided by clustering matrix regularization. This method enhances graph representations by selectively filtering task-relevant information from the input graph to create a compressed yet predictive clustering matrix. Specifically, we incorporate high-frequency information via the graph Laplacian matrix and introduce a dynamic gating mechanism to combine both high- and low-frequency information from graph nodes, improving the predictability of the clustering matrix. Additionally, we employ a noise injection technique, adding multivariate independent Gaussian noise to the clustering matrix to compress information and accurately define node category affiliations. Theoretical validation confirms the effectiveness of our approach. We conduct extensive experiments on datasets spanning social networks, biological proteins, and molecular chemistry, totaling 17,372 sample graphs. CMRGP achieves superior performance in graph-level classification, with an average accuracy improvement of 4.36–8.16% across six public datasets, including increases of 4.36% on DD and 8.16% on NCI1.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"311 ","pages":"Article 113108"},"PeriodicalIF":7.2,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143340037","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}