Data & Knowledge Engineering最新文献_第6页

Multi-feature classification for fake news detection using multiscale and atrous convolution-based adaptive temporal convolution network 基于多尺度自适应时域卷积网络的多特征分类假新闻检测

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-11-01 Epub Date: 2025-06-03 DOI: 10.1016/j.datak.2025.102469

Rashmi Rane , R. Subhashini

{"title":"Multi-feature classification for fake news detection using multiscale and atrous convolution-based adaptive temporal convolution network","authors":"Rashmi Rane , R. Subhashini","doi":"10.1016/j.datak.2025.102469","DOIUrl":"10.1016/j.datak.2025.102469","url":null,"abstract":"<div><div>In the exponential growth of social media platforms, Facebook, Twitter, YouTube and Instagram are the main sources for providing news and information about anything at anywhere. Sometimes, fake information is quickly spread by uploading from particular people affecting the media usage of people. In this research work, a novel deep learning-based framework is proposed to effectively detect fake news for enhancing the trust of social media users. At first, the required text data is gathered from the benchmark resources and given to the preprocessing stage. Then, the preprocessed data is fed into the feature extraction phase here, the Bidirectional Encoder Representations from Transformers (BERT), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN) mechanisms are utilized to effectively extract the meaningful information from the data and improve the accuracy. Also, it can generate three sets of BERT, temporal, and spatial features in the extraction phase and then given to the detection phase. Here, the Multiscale and Atrous Convolution-based Adaptive Temporal Convolution Network (MAC-ATCN) is used for ultimately identifying and categorizing the false information to ensure more reliable outcomes and decision-making processes. Additionally, the Modified Osprey Optimization Algorithm (MOOA) algorithm is employed to fine-tune the parameters to prevent overfitting issues when dealing with larger data. It helps to easily address the imbalanced dataset issues by varying the hyperparameters in the training process. Finally, the overall detection performance is validated with various performance measures and compared with existing works. Also, the developed method achieved better accuracy value for dataset 1 is 93.74 % and dataset 2 is 92.82%. By effectively identifying the fake news in social media can help users to make timely informed decisions. This helps to prevent the spread of misinformation and protects individuals from harmful consequences.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102469"},"PeriodicalIF":2.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271234","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}

引用次数: 0

CredBERT: Credibility-aware BERT model for fake news detection 用于假新闻检测的可信度感知BERT模型

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-11-01 Epub Date: 2025-05-20 DOI: 10.1016/j.datak.2025.102461

Anju R., Nargis Pervin

{"title":"CredBERT: Credibility-aware BERT model for fake news detection","authors":"Anju R., Nargis Pervin","doi":"10.1016/j.datak.2025.102461","DOIUrl":"10.1016/j.datak.2025.102461","url":null,"abstract":"<div><div>The spread of fake news on social media poses significant challenges, especially in distinguishing credible sources from unreliable ones. Existing methods primarily rely on text analysis, often neglecting user credibility, a key factor in enhancing detection accuracy. To address this, we propose CredBERT, a framework that combines credibility scores derived from user interactions and domain expertise with BERT-based text embeddings. CredBERT employs a multi-classifier ensemble, integrating Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), BiLSTM, Logistic Regression, and k-Nearest Neighbors, with predictions aggregated using majority voting, ensuring robust performance across both balanced and imbalanced class datasets. This approach effectively merges user credibility with content-based features, improving prediction accuracy and reducing biases. Compared to state-of-the art baselines FakeBERT and BiLSTM, CredBERT achieves 6.45% and 4.21% higher accuracy, respectively. By evaluating user credibility and content features, our model not only enhances fake news detection but also contributes to mitigating misinformation by identifying unreliable sources.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102461"},"PeriodicalIF":2.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134719","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}

引用次数: 0

Supporting Sound Multi-Level Modeling—Specification and Implementation of a Multi-Dimensional Modeling Approach 支持健全的多层次建模——多维建模方法的规范与实现

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-11-01 Epub Date: 2025-07-19 DOI: 10.1016/j.datak.2025.102481

Thomas Kühne , Manfred A. Jeusfeld

引用次数: 0

Dirigo: A method to extract event logs for object-centric processes Dirigo：为以对象为中心的进程提取事件日志的方法

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-11-01 Epub Date: 2025-07-05 DOI: 10.1016/j.datak.2025.102485

Jia Wei , Chun Ouyang , Ying Wang , Lei Huang

{"title":"Dirigo: A method to extract event logs for object-centric processes","authors":"Jia Wei , Chun Ouyang , Ying Wang , Lei Huang","doi":"10.1016/j.datak.2025.102485","DOIUrl":"10.1016/j.datak.2025.102485","url":null,"abstract":"<div><div>Real-world processes involve multiple object types with intricate interrelationships. Traditional event logs (in XES format), which record process execution centred around the case notion, are restricted to a single-object perspective, making it difficult to capture the behaviour of multiple objects and their interactions. To address this limitation, object-centric event logs (OCEL) have been introduced to capture both the objects involved in a process and their interactions with events. The object-centric event data (OCED) metamodel extends the OCEL format by further capturing dynamic object attributes and object-to-object relations. Recently OCEL 2.0 has been proposed based on OCED metamodel. Current research on generating OCEL logs requires specific input data sources, and resulting log data often fails to fully conform to OCEL 2.0. Moreover, the generated OCEL logs vary across different representational formats and their quality remains unevaluated. To address these challenges, a set of quality criteria for evaluating OCEL log representations is established. Guided by these criteria, <em>Dirigo</em> is proposed—a method for extracting event logs that not only conforms to OCEL 2.0 but also extends it by capturing the temporal aspect of dynamic object-to-object relations. Object-role Modelling (ORM), a conceptual data modelling technique, is employed to describe the artifact produced at each step of <em>Dirigo</em>. To validate the applicability of <em>Dirigo</em>, it is applied to a real-life use case. The quality of the log representation of the extracted event log is compared to those of existing OCEL logs using the established quality criteria.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102485"},"PeriodicalIF":2.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614765","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}

引用次数: 0

Corrigendum to “Unraveling the foundations and the evolution of conceptual modeling—Intellectual structure, current themes, and trajectories” [Knowledge and Data Engineering 154, 2024, 102351] “揭示概念建模的基础和演变——知识结构、当前主题和轨迹”的勘误表[j] .知识与数据工程154,2024,102351。

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-11-01 Epub Date: 2025-08-11 DOI: 10.1016/j.datak.2025.102498

Jacky Akoka , Isabelle Comyn-Wattiau , Nicolas Prat , Veda C. Storey

引用次数: 0

Inference-based schema discovery for RDF data RDF数据的基于推理的模式发现

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-11-01 Epub Date: 2025-07-19 DOI: 10.1016/j.datak.2025.102491

Redouane Bouhamoum , Zoubida Kedad , Stéphane Lopes

{"title":"Inference-based schema discovery for RDF data","authors":"Redouane Bouhamoum , Zoubida Kedad , Stéphane Lopes","doi":"10.1016/j.datak.2025.102491","DOIUrl":"10.1016/j.datak.2025.102491","url":null,"abstract":"<div><div>The Semantic Web represents a huge information space where an increasing number of datasets, described in RDF, are made available to users and applications. In this context, the data is not constrained by a predefined schema. In RDF datasets, the schema may be incomplete or even missing. While this offers high flexibility in creating data sources, it also makes their use difficult. Several works have addressed the problem of automatic schema discovery for RDF datasets, but existing approaches rely only on the explicit information provided by the data source, which may limit the quality of the results. Indeed, in an RDF data source, an entity is described by explicitly declared properties, but also by implicit properties that can be derived using reasoning rules. These implicit properties are not considered by existing schema discovery approaches.</div><div>In this work, we propose a first contribution towards a hybrid schema discovery approach capable of exploiting all the semantics of a data source, which is represented not only by the explicitly declared triples, but also by the ones that can be inferred through reasoning. By considering both explicit and implicit properties, the quality of the generated schema is improved. We provide a scalable design of our approach to enable the processing of large RDF data sources while improving the quality of the results. We present some experiments which demonstrate the efficiency of our proposal and the quality of the discovered schema.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102491"},"PeriodicalIF":2.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670538","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}

引用次数: 0

Explainable artificial intelligence for natural language processing: A survey 用于自然语言处理的可解释人工智能：综述

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-11-01 Epub Date: 2025-06-13 DOI: 10.1016/j.datak.2025.102470

Md. Mehedi Hassan , Anindya Nag , Riya Biswas , Md Shahin Ali , Sadika Zaman , Anupam Kumar Bairagi , Chetna Kaushal

{"title":"Explainable artificial intelligence for natural language processing: A survey","authors":"Md. Mehedi Hassan , Anindya Nag , Riya Biswas , Md Shahin Ali , Sadika Zaman , Anupam Kumar Bairagi , Chetna Kaushal","doi":"10.1016/j.datak.2025.102470","DOIUrl":"10.1016/j.datak.2025.102470","url":null,"abstract":"<div><div>Recently, artificial intelligence has gained a lot of momentum and is predicted to surpass expectations across a range of industries. However, explainability is a major challenge due to sub-symbolic techniques like Deep Neural Networks and Ensembles, which were absent during the boom of AI. The practical application of AI in numerous application areas is greatly undermined by this lack of explainability. In order to counter the lack of perception of AI-based systems, Explainable AI (XAI) aims to increase transparency and human comprehension of black-box AI models. Explainable AI (XAI) also strives to promote transparency and human comprehension of black-box AI models. The explainability problem has been approached using a variety of XAI strategies; however, given the complexity of the search space, it may be tricky for ML developers and data scientists to construct XAI applications and choose the optimal XAI algorithms. This paper provides different frameworks, surveys, operations, and explainability methodologies that are currently available for producing reasoning for predictions from Natural Language Processing models in order to aid developers. Additionally, a thorough analysis of current work in explainable NLP and AI is undertaken, providing researchers worldwide with exploration, insight, and idea development opportunities. Finally, the authors highlight gaps in the literature and offer ideas for future research in this area.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102470"},"PeriodicalIF":2.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297314","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}

引用次数: 0

Corrigendum to “Domain knowledge in artificial intelligence: Using conceptual modeling to increase machine learning accuracy and explainability” [Knowledge and Data Engineering Volume 160, November 2025, 102482] “人工智能领域知识：使用概念建模来提高机器学习的准确性和可解释性”的勘误表[知识与数据工程卷160，十一月2025,102482]

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-11-01 Epub Date: 2025-08-05 DOI: 10.1016/j.datak.2025.102500

Veda C. Storey , Jeffrey Parsons , Arturo Castellanos Bueso , Monica Chiarini Tremblay , Roman Lukyanenko , Alfred Castillo , Wolfgang Maaß

引用次数: 0

IDL-BiGRU: Integrated deep learning assisted smart scheduling of big data over cloud environment IDL-BiGRU：集成深度学习辅助云环境下大数据智能调度

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-11-01 Epub Date: 2025-07-06 DOI: 10.1016/j.datak.2025.102489

Rama Satish K V , Vibha M B , Lovely Sasidharan

{"title":"IDL-BiGRU: Integrated deep learning assisted smart scheduling of big data over cloud environment","authors":"Rama Satish K V , Vibha M B , Lovely Sasidharan","doi":"10.1016/j.datak.2025.102489","DOIUrl":"10.1016/j.datak.2025.102489","url":null,"abstract":"<div><div>The rapid expansion of Internet of Things (IoT) applications generates a continuous and massive flow of data, creating significant challenges in both data processing and storage management. Cloud computing offers scalable infrastructure to handle such data intensive workloads, but optimal task scheduling remains critical to ensure performance and resource efficiency. Traditional scheduling algorithms often fall short due to limited adaptability and consideration of only a few system parameters. In this paper, a novel integrated deep learning-assisted scheduling framework is utilized for scheduling big data over a cloud environment. The proposed framework integrated deep reinforcement learning with the bidirectional gated recurrent unit (IDL-BiGRU) model to intelligently schedule tasks based on real-time system states. The IDL-BiGRU model leverages the advantage of deep Q-learning for decision making and BiGRU's ability to capture bidirectional temporal dependencies in task and resource usage patterns. In this work, RAM, CPU, bandwidth utilization of the network, and disk storage are considered for scheduling purposes. The suggested method is to shorten the makespan and increase resource utilization. The Java tool is utilized for conducting the experimental verifications. Analysis and comparison of the suggested deep learning framework's performance with current methods are done. For 1000 tasks, the proposed method attains 0.90 degrees of imbalance, 291.17 ms downtime, 1050 ms throughput, and 721.58 makespan. The performance analysis demonstrates that the suggested strategy outperforms previous methods.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102489"},"PeriodicalIF":2.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711633","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}

引用次数: 0

Conceptual modeling: Foundations, a historical perspective, and a vision for the future 概念建模：基础、历史视角和对未来的展望

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2025-11-01 Epub Date: 2025-07-07 DOI: 10.1016/j.datak.2025.102483

John Mylopoulos , Giancarlo Guizzardi , Nicola Guarino

引用次数: 0