Knowledge-Based Systems最新文献

{"title":"Advanced people analytics: integrating tournament theory, human capital theory, and social network theory for enhanced HRIS and performance evaluation","authors":"Tianzi Zheng,&nbsp;Riyaz Sikora","doi":"10.1016/j.knosys.2025.114477","DOIUrl":"10.1016/j.knosys.2025.114477","url":null,"abstract":"<div><div>This study explores the integration of advanced data analytics techniques within People Analytics and Human Resource Information Systems (HRIS), emphasizing their application in both organizational and sports performance contexts. By synthesizing Tournament Theory, Human Capital Theory, and Social Network Theory, this research provides a comprehensive framework for understanding skill dissemination, performance evaluation, and wage determination. Utilizing the NBA 2 K dataset, this study quantifies both tangible and intangible player attributes, incorporating digital engagement and social media metrics to enhance traditional performance metrics. Employing community detection algorithms and the Independent Cascade Model, the research uncovers hidden competencies and their influence on team dynamics and organizational effectiveness. The results contest established HRIS approaches, suggesting a holistic talent management strategy that takes into account the multifacetedness of skills propagation through networks. This work offers significant implications for HR professionals, providing novel insights into strategic HR planning, talent acquisition, and performance management in the digital age.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114477"},"PeriodicalIF":7.6,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159957","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":"MRBalance: A framework for enhancing event causality identification in multi-agent debates via role assignment","authors":"Xiang Zou ,&nbsp;Xuanhong Li ,&nbsp;Po Hu ,&nbsp;Ming Dong","doi":"10.1016/j.knosys.2025.114470","DOIUrl":"10.1016/j.knosys.2025.114470","url":null,"abstract":"<div><div>The rapid development of large language models (LLMs) has advanced natural language processing by improving contextual understanding and generalization abilities. However, despite these advances, determining event causality remains a challenging task. When LLMs are applied to this task, they frequently exhibit significant inconsistencies in recognizing causal representations, resulting in the phenomenon known as causal hallucinations. Specifically, LLMs perform well in predicting events with causal relationships but struggle with events without such relationships, frequently failing to achieve balanced performance across different causal scenarios. In this study, we propose MRBalance, a novel framework that uses role-based multi-agent debates to improve event causality identification. Our method transforms the task into a single-choice question-answering task, prompting LLM-based agents to engage in structured debates and justify their answers using their unique role-based perspectives. In addition, we introduce a mechanism for optimizing team members that selects the best agents to participate in the next debate when the debate rounds are lengthy. Extensive experiments on two benchmark datasets demonstrate significant performance improvements, highlighting the effectiveness of MRBalance in reducing causal hallucinations and increasing robustness.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114470"},"PeriodicalIF":7.6,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159953","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":"MIAFEx: An attention-based feature extraction method for medical image classification","authors":"Oscar Ramos-Soto ,&nbsp;Jorge Ramos-Frutos ,&nbsp;Ezequiel Pérez-Zarate ,&nbsp;Diego Oliva ,&nbsp;Sandra E. Balderas-Mata","doi":"10.1016/j.knosys.2025.114468","DOIUrl":"10.1016/j.knosys.2025.114468","url":null,"abstract":"<div><div>Feature extraction techniques are crucial in medical image classification; however, classical feature extractors, in addition to traditional machine learning classifiers, often exhibit significant limitations in providing sufficient discriminative information for complex image sets. While Convolutional Neural Networks (CNNs) and Vision Transformer (ViT) have shown promise in feature extraction, they are prone to overfitting due to the inherent characteristics of medical imaging data, including small sample sizes or high intra-class variance. In this work, the Medical Image Attention-based Feature Extractor (MIAFEx) is proposed, a novel method that employs a learnable refinement mechanism to enhance the classification token within the Transformer encoder architecture. This mechanism adjusts the token based on learned weights, improving the extraction of salient features and enhancing the model’s adaptability to the challenges presented by medical imaging data. The MIAFEx output feature quality is compared against classical feature extractors using traditional and hybrid classifiers. Also, the performance of these features is compared against modern CNN and ViT models in classification tasks, demonstrating their superiority in accuracy and robustness across multiple complex medical imaging datasets. This advantage is particularly pronounced in scenarios with limited training data, where traditional and modern models often struggle to generalize effectively. The source code of this proposal can be found at <span><span>github.com/Oscar-RamosS/Medical-Image-Attention-based-Feature-Extractor-MIAFEx</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114468"},"PeriodicalIF":7.6,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159955","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":"Potential subgraph rule and reasoning context enhancement for sparse multi-hop knowledge graph reasoning","authors":"Congcong Sun ,&nbsp;Jianrui Chen ,&nbsp;Deguang Chen ,&nbsp;Junjie Huang","doi":"10.1016/j.knosys.2025.114483","DOIUrl":"10.1016/j.knosys.2025.114483","url":null,"abstract":"<div><div>Multi-hop knowledge graph reasoning aims to leverage the relations between multiple nodes in a knowledge graph to reason information about an event or entity. This reasoning process requires traversing multiple interconnected facts or knowledge points, which aids in understanding the model’s decision-making process. Multi-hop knowledge graph reasoning has driven the development of knowledge-based technologies, such as question-answering systems and recommendation systems. However, multi-hop reasoning relies on the connectivity between different entities in the knowledge graph. This characteristic makes multi-hop reasoning lack robustness when dealing with sparse data. To address the challenges of sparsity, recent studies pre-train knowledge graph embedding models to complete potential triples. The completion methods introduce noisy triples, which increases the risk of model selection errors and spurious paths. In this work, we propose a framework based on potential subgraph rule and reasoning context enhancement to mitigate the challenges of sparsity. On one hand, we leverage reasoning context to enhance state information and the reasoning process; on the other hand, we design an action perceptron based on the importance of reasoning context to reduce the introduction of noisy triples. Additionally, we analyze the phenomenon of data augmentation introducing spurious paths, and further utilize data augmentation-based potential subgraph rules to guide the reasoning process. This dual mechanism demonstrates stronger robustness in addressing sparsity challenges and spurious paths. Diverse experiments demonstrate that our model outperforms the existing multi-hop reasoning models across five datasets. Our implementations will be publicly available at: <span><span>https://github.com/jianruichen/PreKGR</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114483"},"PeriodicalIF":7.6,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108884","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 to extract and aggregate contexts for link prediction in heterogeneous graphs","authors":"Jimin Woo ,&nbsp;Minbae Park ,&nbsp;Hyunjoon Kim","doi":"10.1016/j.knosys.2025.114478","DOIUrl":"10.1016/j.knosys.2025.114478","url":null,"abstract":"<div><div>Many diverse real-world graph datasets are heterogeneous graphs, and link prediction on these graphs is a fundamental task. The current trends of link prediction on heterogeneous graphs emphasize leveraging contextual information from either a path between a source node and a target node, or a sub-graph sampled around these two nodes. However, these approaches face limitations in identifying only beneficial contextual nodes around source and target and then effectively aggregating the representations of these nodes for improving overall prediction accuracy. To address these limitations, we claim that carefully-extracted context nodes can aid in accurate link prediction, and these context nodes should be similar to a source node or a target node in a representation space. To this end, we propose a new link prediction framework LEACH which learns to extract the beneficial context nodes and to aggregate their representations in heterogeneous graphs. Specifically, our approach involves three steps to learn: (i) generating heterogeneity-aware representations of nodes in the heterogeneous graph, (ii) selecting the context nodes based on the relatedness to the source and target nodes; and (iii) aggregating the representations of the context nodes to obtain the source and target representations. Extensive experiments demonstrate that LEACH significantly outperforms existing baselines on three publicly available heterogeneous graph datasets. We provide analytical insights into the rationale behind the superior performance of LEACH on link prediction.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114478"},"PeriodicalIF":7.6,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120246","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 neural network-based iterative heuristic algorithm for the polynomial robust knapsack problem","authors":"José González-Cortés,&nbsp;Carlos Contreras-Bolton","doi":"10.1016/j.knosys.2025.114439","DOIUrl":"10.1016/j.knosys.2025.114439","url":null,"abstract":"<div><div>The polynomial robust knapsack problem (PRKP) is a variant of the classic knapsack problem by incorporating uncertain costs and benefits from item combinations, leading to a nonlinear objective function and exponential solution space. These complexities make the PRKP suitable for real-world scenarios where interactions between items unpredictably impact outcomes. However, existing algorithms struggle to efficiently solve large instances of the PRKP due to its computational complexity. Therefore, this paper presents an iterative heuristic algorithm leveraging a neural network (NN) to address the PRKP, reducing the solution space and enabling efficient resolution of subproblems. The framework integrates an NN trained in two steps: general training and fine-tuning. The trained model is then embedded in the iterative heuristic algorithm to tackle the PRKP. A synthetic dataset comprising 2500 instances, ranging from 100 to 1500 items, is created to train the NN. Comparative evaluations are conducted using 1600 benchmark instances from the literature and 140 larger instances containing between 2000 and 15,000 items. We compare our approach against two state-of-the-art algorithms for the PRKP: a genetic algorithm and a random forest-based heuristic. Computational results demonstrate that the proposed algorithm outperforms the genetic algorithm, providing superior solution quality with significantly reduced computing times. Meanwhile, against random forest-based heuristic, it delivers better solution quality with only a moderate increase in computing time. For larger instances, it maintains its advantage in solution quality while remaining computationally efficient. These results highlight the algorithm’s scalability, effectiveness, and potential to address the PRKP.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114439"},"PeriodicalIF":7.6,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108825","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":"MLDCGAN: A multimodal latent diffusion conditioned GAN model for accelerated and high-fidelity MRI-CT synthesis in radiotherapy planning","authors":"Can Hu ,&nbsp;Chunchao Xia ,&nbsp;Chuanbing Wang ,&nbsp;Xiayu Hang ,&nbsp;Xiuhan Li ,&nbsp;Han Zhou ,&nbsp;Ning Cao","doi":"10.1016/j.knosys.2025.114491","DOIUrl":"10.1016/j.knosys.2025.114491","url":null,"abstract":"<div><div>Magnetic resonance imaging (MRI) offers significant advantages in soft tissue contrast. However, it cannot directly provide electron density information for radiotherapy, relying instead on time-consuming and error-prone MRI-CT image registration. Synthetic CT (sCT) technology, which directly generates CT images from MRI, is pivotal for achieving only MRI-based radiotherapy. However, existing synthesis methods based on generative adversarial network (GAN) and diffusion models face challenges such as prolonged inference times and insufficient utilization of multimodal information, which severely hinder the clinical application of synthetic images. In this study, we propose a novel Multimodal Latent Diffusion Conditioned GAN (MLDCGAN) Model. First, we design a non-parametric non-Gaussian complex denoising distribution based on a conditional GAN, employing a multimodal distribution to achieve large-step denoising. This is combined with a pre-trained autoencoder to compress the image into a low-dimensional latent space, significantly reducing inference time. Second, we fully leverage multimodal MRI information by constructing a local refinement conditional generator with multimodal inputs, including T1-Weighted (T1W), T2-Weighted (T2W), and Mask images. The generator is enhanced by an adaptive weighted multi-sequence fusion module and an enhanced cross-attention module, significantly improving the structural consistency and detail fidelity of the generated sCT images. Finally, by jointly optimizing the style loss and content loss, we ensure the perceptual quality and clinical accuracy of the synthetic images. Experimental results demonstrate that MLDCGAN outperforms existing state-of-the-art methods on both public and private datasets, showing significant improvements in both image quality and inference speed. Subjective evaluations from multiple experienced clinicians indicate that the generated sCT images exhibit no significant difference from real CT in terms of key anatomical structure clarity and overall quality (<em>P</em> &gt; 0.05). Further assessments of clinical target delineation and dose distribution confirm that sCT retains anatomical features well and provides dose distributions consistent with real CT, ensuring the reliability of dose calculations in radiotherapy planning. This study provides a more reliable and efficient technical foundation for achieving only MRI-based radiotherapy. It is expected to assist clinicians in developing more precise radiotherapy plans, ultimately improving treatment outcomes in future clinical practice.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"329 ","pages":"Article 114491"},"PeriodicalIF":7.6,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096170","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 neural network to surrogate computational bone remodelling in the calcaneus","authors":"Ana Pais ,&nbsp;Jorge Lino Alves ,&nbsp;Jorge Belinha","doi":"10.1016/j.knosys.2025.114445","DOIUrl":"10.1016/j.knosys.2025.114445","url":null,"abstract":"<div><div>This study proposes a data-driven approach using surrogate models based on Multi-Layer Perceptrons to predict bone remodelling outcomes in the calcaneus, both with and without fractures. The objective is to develop and train a neural network that accurately captures the biomechanical factors influencing the problem and predicts the resulting bone density distribution in the calcaneus. Given the complexity of bone healing processes, a comprehensive dataset was collected to train and validate the models under two distinct scenarios: an intact calcaneus and a fractured calcaneus treated with a surgical screw.</div><div>Key parameters of the surrogate model, namely, the number of hidden layers, hidden layer size, and activation function, were optimized to enhance model performance. Additionally, training parameters such as learning rate and batch size were tuned. The hyperbolic tangent activation function was found to yield a lower mean squared error compared to the rectified linear units. Larger batch sizes and learning rates were found to improve model performance. The neural network designed to predict bone density in the intact model outperformed the one used for the fractured calcaneus with a screw, largely due to the increased variability in the fractured data. When the fracture did not significantly alter the trabecular distribution, prediction accuracy improved.</div><div>Finally, the structural response of the models was evaluated, and it was observed that the trabecular arrangement inferred by the neural network tended to produce less stiff responses compared to those from the finite element method, likely due to the smoother density field predicted by the network.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114445"},"PeriodicalIF":7.6,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120249","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":"Proposed quaternion fractional dual-Hahn moments for color image reconstruction and encryption","authors":"Karim El-khanchouli ,&nbsp;Hanaa Mansouri ,&nbsp;Ahmed Bencherqui ,&nbsp;Hicham Karmouni ,&nbsp;Nour-Eddine Joudar ,&nbsp;Mhamed Sayyouri","doi":"10.1016/j.knosys.2025.114467","DOIUrl":"10.1016/j.knosys.2025.114467","url":null,"abstract":"<div><div>Moments are essential descriptors for capturing fundamental characteristics of a signal, such as its shape and texture, thereby enabling a compact and easily analyzable representation. This article introduces a new family of discrete fractional moments, the quaternion Cartesian fractional dual-Hahn moments (QCFrDHOMs). These moments are derived from the fractional dual-Hahn moments (FrDHOMs), which are constructed from the matrix of fractional dual-Hahn orthogonal polynomials (FrDHOPs), obtained through the spectral decomposition of the classical dual-Hahn orthogonal polynomials (DHOPs). To ensure the stability of the computations, particularly for high-degree polynomials, a recursive method is proposed to calculate the initial terms of the DHOPs, thereby reducing the risk of numerical instability. The FrDHOMs are then generalized into QCFrDHOMs for efficient analysis of color images using quaternion algebra. Experimental results demonstrate that the QCFrDHOMs outperform classical DHOMs in terms of robustness and reconstruction capability. Additionally, an encryption and decryption scheme using QCFrDHOMs and chaotic systems is presented. Tests show that this scheme provides significant resistance to various attacks while maintaining nearly intact quality in the decrypted images. This not only highlights the effectiveness of the encryption scheme but also the enhanced security and robustness of the approach. Compared to other existing methods, our scheme stands out for its exceptional reliability and robustness, making a significant contribution to the secure protection of color images.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"329 ","pages":"Article 114467"},"PeriodicalIF":7.6,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095594","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":"CausalEnhance: knowledge-enhanced pre-training for causality identification and extraction","authors":"Meiyun Wang ,&nbsp;Kiyoshi Izumi ,&nbsp;Hiroki Sakaji","doi":"10.1016/j.knosys.2025.114447","DOIUrl":"10.1016/j.knosys.2025.114447","url":null,"abstract":"<div><div>Causality identification and extraction are crucial in understanding causal relationships in text. Current studies heavily rely on datasets annotated with causal relationships. However, acquiring such datasets poses a challenge due to substantial costs, hindering progress in this research field. To address this, we introduce CausalEnhance, a novel approach designed to bridge this gap by combining weakly-guided pre-training with external causal knowledge. Our method starts with a rule-based system that automates causal annotation, enriching external data with explicit causal knowledge and creating pseudo labels. These pseudo-labels are then incorporated into a weakly supervised pre-training framework. We introduce three innovative pre-training tasks: the Pre-training Causal Clues Fill-Mask task (PCM) to pinpoint causality origins, the Pre-training Causality Identification task (PCI) to capture general causal patterns, and the Pre-training Causality Extraction task (PCE) for understanding explicit causal pairs and inferring implicit ones. Our experiments, conducted across eight datasets in two languages, English and Chinese, demonstrate CausalEnhance’s effectiveness in both identifying and extracting causality, highlighting its potential as a robust method for textual causality analysis in different linguistic contexts.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114447"},"PeriodicalIF":7.6,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120248","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}