Data & Knowledge Engineering最新文献

{"title":"Editorial introduction for special issue on research challenges and practices in conceptual modeling – ER 2023","authors":"João Paulo A Almeida , José Borbinha , Giancarlo Guizzardi , Sebastian Link , Jelena Zdravkovic","doi":"10.1016/j.datak.2025.102487","DOIUrl":"10.1016/j.datak.2025.102487","url":null,"abstract":"","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102487"},"PeriodicalIF":2.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"RankT: Ranking-Triplets-based adversarial learning for knowledge graph link prediction","authors":"Jinlei Zhu,&nbsp;Xin Zhang,&nbsp;Xin Ding","doi":"10.1016/j.datak.2025.102463","DOIUrl":"10.1016/j.datak.2025.102463","url":null,"abstract":"<div><div>Aiming at completing the missing edges between entities in the knowledge graph, many state-of-the-art models are proposed to predict the links. Those models mainly focus on predicting the link score between source and target entities with certain relations, but ignore the similarities or differences of the whole meanings of triplets in different subgraphs. However, the triplets interact with each other in different ways and the link prediction model may lack interaction. In other word, the link prediction is superimposed with potential triplet uncertainties. To address this issue, we propose a Ranking-Triplet-based uncertainty adversarial learning (RankT) framework to improve the embedding representation of triplet for link prediction. Firstly, the proposed model calculates the node and edge embeddings by the node-level and edge-level neighborhood aggregation respectively, and then fuses the embeddings by a self-attention transformer to gain the interactive embedding of the triplet. Secondly, to reduce the uncertainty of the probability distribution of predicted links, a ranking-triplet-based adversarial loss function based on the confrontation of highest certainty and highest uncertainty links is designed. Lastly, to strengthen the stability of the adversarial learning, a ranking-triplet-based consistency loss is designed to make the probability of the highest positive links converge in the same direction. The ablation studies show the effectiveness of each part of the proposed model. The comparison of experimental results shows that our model significantly outperforms the state-of-the-art models. In conclusion, the proposed model improves the link prediction performance while discovering the similar or different meanings of triplets.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102463"},"PeriodicalIF":2.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"ROSI: A hybrid solution for omni-channel feature integration in E-commerce","authors":"Luyi Ma ,&nbsp;Shengwei Tang ,&nbsp;Anjana Ganesh,&nbsp;Jiao Chen,&nbsp;Aashika Padmanabhan,&nbsp;Malay Patel,&nbsp;Jianpeng Xu,&nbsp;Jason Cho,&nbsp;Evren Korpeoglu,&nbsp;Sushant Kumar,&nbsp;Kannan Achan","doi":"10.1016/j.datak.2025.102465","DOIUrl":"10.1016/j.datak.2025.102465","url":null,"abstract":"<div><div>Efficient integration of customer behavior data across multiple channels, including online and in-store interactions, is essential for developing recommendation systems that enhance customer experiences and maintain a competitive edge in e-commerce. However, the integration process faces several challenges, including data synchronization and discrepancies in data schemas. In this study, we introduce a hybrid data pipeline, <span>ROSI</span> (Retail Online-Store Integration), designed to integrate real-time streaming data from online platforms with batch data from in-store interactions. <span>ROSI</span> employs scalable, fault-tolerant streaming systems for online data and periodic batch processing for offline data, ensuring effective synchronization despite variations in data volume, update frequency, and schema. Our approach incorporates in-memory storage, sliding time windows, and feature registries to support applications such as machine learning model training and real-time inference in recommendation systems. Experimental results on a real-world retail data demonstrate that <span>ROSI</span> is highly robust, with a reduced growth rate of overall latency when data size increases linearly. Additionally, sequential recommendation systems built on the integrated dataset show a 6.25% improvement in ranking metrics. Overall, the proposed hybrid pipeline facilitates more personalized, omnichannel customer experiences while enhancing operational efficiency.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102465"},"PeriodicalIF":2.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"PSA-GAT: Integrating position-syntax and cross-aspect graph attention networks for aspect-based sentiment analysis","authors":"Ning Zhou,&nbsp;Linfu Sun,&nbsp;Min Han,&nbsp;Songlin He","doi":"10.1016/j.datak.2025.102477","DOIUrl":"10.1016/j.datak.2025.102477","url":null,"abstract":"<div><div>Aspect-based sentiment analysis (ABSA) is widely applied in analyzing user review data on web platforms to identify sentiment polarity toward specific aspects of web reviews. However, individual reviews often contain multiple conditions and coordinating and conflicting elements or relationships, which significantly increases the complexity of this task. In recent years, exploiting semantic–syntactic information with graph neural networks has been widely used to address such tasks. However, such methods overlook the features of the location influence factor of words and may provide irrelevant or even interfering noisy signals for ABSA because of the word association relationships mined by the syntax tree and semantic composition tree. To alleviate the effect of noise information and fully strengthen the context for multiple-aspect representation in ABSA, we propose a new framework, PSA-GAT, that mines information on position importance, syntactic–semantic dependencies and cross-aspect correlations. Overall, the structural features of the multi-aspect sentiment set are learned by using various variations of graph neural networks. Moreover, the experimental results on four real-world datasets demonstrate the effectiveness of PSA-GAT compared to state-of-the-art methods. The code is available at <span><span>https://github.com/zhouning6000/PSA_GAT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102477"},"PeriodicalIF":2.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Data and knowledge engineering: Insights from forty years of publication","authors":"Jacky Akoka ,&nbsp;Isabelle Comyn-Wattiau ,&nbsp;Nicolas Prat ,&nbsp;Veda C. Storey","doi":"10.1016/j.datak.2025.102492","DOIUrl":"10.1016/j.datak.2025.102492","url":null,"abstract":"<div><div>The journal, <em>Data and Knowledge Engineering (DKE),</em> first published by Elsevier in 1985, has now been in existence for forty years. This journal has evolved and matured to play an important role in establishing and progressing research on conceptual modeling and related areas. To accurately characterize the history and current state of the research contributions and their impact, we analyze its publications in three phases, by employing bibliometric techniques of co-citation, bibliographic coupling, main path analysis, and topic modeling. Using descriptive bibliometrics, the results from the first phase provide an overview of the articles that have been published in the journal. It analyzes the dynamics and trend patterns of publications, specifically, their main topics and contributions. Using bibliometric mapping, the second phase identifies the journal's intellectual structure, its primary research themes, and the pathways through which knowledge is disseminated between the most influential articles. The third phase entails a comparison of DKE with other scientific journals that share at least some of its scope. In addition to delineating the strengths of DKE, we provide insights into how DKE might continue to evolve and progress the contributions to the field.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102492"},"PeriodicalIF":2.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Advancing credit risk assessment in the retail banking industry: A hybrid approach using time series and supervised learning models","authors":"Sebastian H. Goldmann,&nbsp;Marcos R. Machado,&nbsp;Joerg R. Osterrieder","doi":"10.1016/j.datak.2025.102490","DOIUrl":"10.1016/j.datak.2025.102490","url":null,"abstract":"<div><div>Credit risk assessment remains a central challenge in retail banking, with conventional models often falling short in predictive accuracy and adaptability to granular customer behavior. This study explores the potential of Time Series Classification (TSC) algorithms to enhance credit risk modeling by analyzing customers’ historical end-of-day balance data. We compare traditional Machine Learning (ML) models – including Logistic Regression and XGBoost – with advanced TSC methods such as Shapelets, Long Short-Term Memory (LSTM) networks, and Canonical Interval Forests (CIF). Our results show that TSC algorithms, particularly CIF and Shapelet-based methods, significantly outperform traditional approaches. When using CIF-derived Probability of Default (PD) estimates as additional features in an XGBoost model, predictive performance improved notably: the combined model achieved an Area under the Curve (AUC) of 0.81, compared to 0.79 for CIF alone and 0.77 for XGBoost without the CIF input. These findings underscore the value of integrating temporal features into credit risk assessment frameworks. Moreover, the complementary strengths of the TSC and XGBoost models across different Receiver Operating Characteristic (ROC) curve regions demonstrate the practical benefits of model stacking. However, performance dropped when using aggregated monthly data, highlighting the importance of preserving high-frequency behavioral signals. This research contributes to more accurate, interpretable, and robust credit risk models and offers a pathway for banks to leverage time series data for improved risk forecasting.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102490"},"PeriodicalIF":2.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Efficient opinion mining for imbalanced customer reviews in last-mile services","authors":"Sangbaek Kim ,&nbsp;Hongchul Lee ,&nbsp;Jiho Kim","doi":"10.1016/j.datak.2025.102466","DOIUrl":"10.1016/j.datak.2025.102466","url":null,"abstract":"<div><div>Last-mile (LM) service manages the final stage of delivering products to customers in supply chains and logistics. Consumer opinion mining has recently become essential for providing high-level LM service quality. However, existing methods face challenges with domain-specific terminology and class imbalance. Therefore, we propose LM-BERT, a BERT-based text classification model specialized in LM service sentiment analysis. In addition, we introduce a teacher–student LM-BERT framework that alleviates data imbalance in online e-commerce reviews through high-confidence pseudo-labeling. After evaluating six Transformer models, KLUE-BERT was identified as the most suitable for our baseline. Experimental results demonstrate that domain-specific knowledge transfer improves performance by 1.78 % on seen data and 1.31 % on unseen data. Statistical verification and explainable artificial intelligence techniques were employed to confirm the reliability of our approach to enhance qualitative performance and expand domain knowledge. We also conducted an ablation study confirming that high-confidence pseudo-labeling (<em>t</em> = 0.99) outperforms the traditional resampling method. The proposed LM-BERT model can effectively support LM service quality evaluation and management based on the voice of the customer in e-commerce.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102466"},"PeriodicalIF":2.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Corrigendum to “Large Language Models for Conceptual Modeling: Assessment and Application Potential” [Knowledge and Data Engineering Volume 160, November 2025, 102480]","authors":"Veda C. Storey ,&nbsp;Jeffrey Parsons ,&nbsp;Arturo Castellanos Bueso ,&nbsp;Monica Chiarini Tremblay ,&nbsp;Roman Lukyanenko ,&nbsp;Alfred Castillo ,&nbsp;Wolfgang Maaß","doi":"10.1016/j.datak.2025.102499","DOIUrl":"10.1016/j.datak.2025.102499","url":null,"abstract":"","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102499"},"PeriodicalIF":2.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145117780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"TEDA-driven adaptive stream clustering for concept drift detection","authors":"Zahra Rezaei ,&nbsp;Hedieh Sajedi","doi":"10.1016/j.datak.2025.102484","DOIUrl":"10.1016/j.datak.2025.102484","url":null,"abstract":"<div><div>The rapid growth of data-driven applications has underlined the need for strong methods to analyze and cluster streaming data. Data stream clustering is envisioned to uncover interesting knowledge concealed within data streams, typically fast, structure- and pattern-evolving. However, most current methods suffer significant challenges like the inability to detect clusters with arbitrarily shaped, handling outliers, adaptation to concept drift, and reducing dependency on predefined parameters. To tackle these challenges, we propose a novel Typicality and Eccentricity Data Analysis (TEDA)-based concept drift detection stream clustering algorithm, which can divide the clustering problem into two subproblems, micro-clusters and macro-clusters. Our methodology utilizes a TEDA-based concept drift detection approach to enhance data stream clustering. Our method employs two models in monitoring the data stream to keep the information of a previous concept while tracking the emergence of a new concept. The models represent two distinct concepts when the intersection of data samples is significantly low, as described by the Jaccard Index. TEDA-CDD is compared to known methods from the literature in experiments using synthetic and real-world datasets simulating real-world applications. By dynamically updating clusters through model reuse or creation, our algorithm ensures adaptability to real-time changes in data distributions. The proposed algorithm was comprehensively evaluated using the KDDCup-99 dataset, an intrusion detection system benchmark under diverse scenarios, including concept drifts, evolving data distributions, varying cluster sizes, and outlier conditions. Empirical results demonstrated the algorithm’s superiority over baseline approaches such as DenStream, DStream, ClusTree, and DGStream, achieving perfect performance metrics. These findings emphasize the effectiveness of our algorithm in addressing real-world streaming data challenges, combining high sensitivity to concept drift with computational efficiency, adaptability, and robust clustering capabilities.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102484"},"PeriodicalIF":2.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144712898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Large language models for conceptual modeling: Assessment and application potential","authors":"Veda C. Storey ,&nbsp;Oscar Pastor ,&nbsp;Giancarlo Guizzardi ,&nbsp;Stephen W. Liddle ,&nbsp;Wolfgang Maaß ,&nbsp;Jeffrey Parsons ,&nbsp;Jolita Ralyté ,&nbsp;Maribel Yasmina Santos","doi":"10.1016/j.datak.2025.102480","DOIUrl":"10.1016/j.datak.2025.102480","url":null,"abstract":"<div><div>Large Language Models (LLMs) are being rapidly adopted for many activities in organizations, business, and education. Included in their applications are capabilities to generate text, code, and models. This leads to questions about their potential role in the conceptual modeling part of information systems development. This paper reports on a panel presented at the <em>43rd International Conference on Conceptual Modeling</em> where researchers discussed the current and potential role of LLMs in conceptual modeling. The panelists discussed applications and interest levels and expressed both optimism and caution in the adoption of LLMs. Suggested is a need for much continued research by the conceptual modeling community on LLM development and their role in research and teaching.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102480"},"PeriodicalIF":2.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}