Data & Knowledge Engineering最新文献

{"title":"MRV-RSA: Developed Modified Random Value Reptile Search Algorithm and Deep Learning based Fraud Detection Model in Banking Sector","authors":"V. Backiyalakshmi ,&nbsp;B. Umadevi","doi":"10.1016/j.datak.2026.102557","DOIUrl":"10.1016/j.datak.2026.102557","url":null,"abstract":"<div><div>The banking sector is significant in economic growth in each nation. Also, each and every person has a separate account in diverse banks for effectively transmitting the money at any time. The proliferation of online banking has brought about a concerning rise in fraudulent transactions, posing a persistent challenge for fraud detection. This contains a collection of fraudulent activities, as well as insurance, credit card, and accounting fraud. Despite the numerous benefits of online transactions, the prevalence of financial fraud and unauthorized transactions poses significant risks. Several researchers have constantly developed various techniques in the past few years to improve detection performance. Yet, it takes more duration for handling massive amounts of various client data sizes to detect abnormal activities. With the aim of resolving these issues, a deep learning based new approach is designed in this research work. Initially, the prescribed data are gathered from the benchmark database, then the gathered data is given to the phase of feature extraction. In this phase, the Principal Component Analysis (PCA), statistical features, and T-distributed Stochastic Neighbor Embedding (t-SNE) mechanisms are utilized to effectively extract the informative features from the collected data. It can optimally minimize the noise and irrelevant information to enhance the training speed. Then, the extracted features are combined and the optimal weighted fused features are determined by utilizing the Modified Random Value Reptile Search Algorithm (MRV-RSA) optimization algorithm. It can effectively improve the training speed and overall performance enabling better detection. Also, the optimal weighted fused features are given to the detection phase using the Dilated Convolution Long Short Term Memory (ConvLSTM) with Multi-scale Dense Attention (DCL-MDA) technique. It can handle massive complex datasets without incurring generalization problems. Further, the classified detected result is provided with a limited duration. Therefore, the efficiency of the model is validated by using the different metrics and contrasted over other traditional models. Hence, the suggested system overwhelms the desired value for finding the fraudulent user to enhance the security level in the banking sector. From the evaluation process, the implemented framework has attained a reliable accuracy rate of 93.86% in Dataset 1 and 97.15% in Dataset 2 to prove its superior performance. This performance enhancement in the developed model could accurately detect fraud at an earlier stage.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"163 ","pages":"Article 102557"},"PeriodicalIF":2.7,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146090258","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":"The tendency-based multi-criteria group recommendation systems","authors":"Tugba Turkoglu Kaya","doi":"10.1016/j.datak.2025.102553","DOIUrl":"10.1016/j.datak.2025.102553","url":null,"abstract":"<div><div>Aggregation strategies in group recommender systems often fall short in balancing diverse user preferences and ensuring fair satisfaction within the group. These limitations become more pronounced in single-criteria frameworks, where the multidimensional nature of user–item interactions is overlooked, thereby restricting the system’s capacity to capture subtle preference variations. While multi-criteria recommendation offers a promising solution by incorporating multiple evaluation dimensions, the adaptation of single-criteria aggregation mechanisms to a multi-criteria setting remains an open research question. For the purpose, in the study, new aggregation techniques and top-<em>n</em> recommendation system mechanism are developed for a new multi-criteria group recommendation system. While user tendencies and qualitative sequences of user evaluations are taken into account in the new combining techniques called weighted preference aggregation, preference without weighted aggregation and weighted without preference vector aggregation the newly developed top-<em>n</em> recommendation system aims to prepare a recommendation list according to group tendencies by using product characteristic structures. In the studies carried out on two different data sets (Yahoo!Movies, TripAdvisor) for three group size (1, 5, 10%), a comparative analysis of each of the proposed methods is made with the methods available in the literature. When the results are examined, it is seen that the proposed methods give very successful results.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"162 ","pages":"Article 102553"},"PeriodicalIF":2.7,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925118","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":"From primes to paths: Enabling fast multi-relational graph analysis","authors":"Konstantinos Bougiatiotis ,&nbsp;Georgios Paliouras","doi":"10.1016/j.datak.2026.102554","DOIUrl":"10.1016/j.datak.2026.102554","url":null,"abstract":"<div><div>Multi-relational networks capture intricate relationships in data and have diverse applications across fields such as biomedical, financial, and social sciences. As networks derived from increasingly large datasets become more common, identifying efficient methods for representing and analyzing them becomes crucial. This work extends the Prime Adjacency Matrices (PAMs) framework, which employs prime numbers to represent distinct relations within a network uniquely. This enables a compact representation of a complete multi-relational graph using a single adjacency matrix, which, in turn, facilitates quick computation of multi-hop adjacency matrices. In this work, we enhance the framework by introducing a lossless algorithm for calculating the multi-hop matrices and propose the Bag of Paths (BoP) representation, a versatile feature extraction methodology for various graph analytics tasks, at the node, edge, and graph level. We demonstrate the efficiency of the framework across various tasks and datasets, showing that simple BoP-based models perform comparably to or better than commonly used neural models while improving speed by orders of magnitude.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"163 ","pages":"Article 102554"},"PeriodicalIF":2.7,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145941279","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":"Unified access to interdisciplinary open data platforms: Open Science Data Network","authors":"Vincent-Nam Dang ,&nbsp;Nathalie Aussenac-Gilles ,&nbsp;Imen Megdiche ,&nbsp;Franck Ravat","doi":"10.1016/j.datak.2025.102552","DOIUrl":"10.1016/j.datak.2025.102552","url":null,"abstract":"<div><div>Open Science is based on a collaborative network to develop transparent, accessible, and shared knowledge. Open Research Data Platforms (ORDPs) are deployed to fulfill the needs for data sharing of a specific community and/or scientific discipline. The high variety of research areas creates a barrier to data sharing between research entities. To enable this research data to be found by the research entities that need it, it is necessary to establish access to different ORDPs that are unknown to these research entities. The goal of this article is to provide a quantitative analysis showing the current limitations of data sharing between ORDPs in Open Science. We then propose a solution to improve data access and sharing based on theoretical foundations and an experimental approach.</div><div>We propose to extend our theoretical interoperability model, which helps us to define the necessary steps to interoperate ORDPs. We present and discuss a quantitative evaluation of ORDPs’ interoperability. Based on this exploratory study, we propose a solution that enables research entities to discover unknown ORDPs, thereby facilitating access to relevant data. This solution is the Open Science Data Network (OSDN), a decentralized, distributed, and federated network of ORDPs that integrates a query propagation process and robustness features. To enable the deployment of OSDN at an Open Science scale, we designed our solution by considering its adoption cost relative to a non-organized interoperability approach. With two ORDPs integrated into the OSDN, the adoption cost is estimated to be reduced by at least 17%. This reduction approaches 100% as the number of integrated ORDPs increases.</div><div>To demonstrate the feasibility of the solution, we developed a Proof of Concept (POC) and applied it to two research projects from different domains and involving distinct research communities. For the first research project, we measured a 7% increase in the volume of accessed data and an 80% reduction in the time needed to find this data. In addition, researcher from this experiment was able to formulate new intra- and interdisciplinary research questions thanks to the newly accessed data. In the second research project, we observed an increase in data volume of up to a factor of 3968. More importantly, this process led to the discovery of new essential data that was previously missing.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"162 ","pages":"Article 102552"},"PeriodicalIF":2.7,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790029","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":"Targeting language models for compile-time computing resource optimization: A novel approach based on masked graph autoencoders","authors":"Federico Cichetti ,&nbsp;Emanuele Parisi ,&nbsp;Andrea Acquaviva ,&nbsp;Francesco Barchi","doi":"10.1016/j.datak.2025.102551","DOIUrl":"10.1016/j.datak.2025.102551","url":null,"abstract":"<div><div>Deep learning-based source code analysis has proven beneficial in supporting complex compile-time decisions that impact performance in heterogeneous devices. Graph-based representations of source code are particularly appealing, as they express dependencies that would otherwise be challenging to identify in textual representations. In this work, we propose DeepCodeGraph (DCG), a technique for constructing a general graph-based language model which learns to extract expressive patterns for the identification of better compilation strategies, optimal hardware configurations and software transformation opportunities. DCG includes: (i) A dataset containing over 100<!--> <!-->k graphs. (ii) A Graph Neural Network (GNN) to implement a graph-based language model. (iii) A self-supervised pre-training framework leveraging Masked Graph AutoEncoding (MGAE). The performance of DCG is evaluated on three downstream tasks: heterogeneous device mapping, thread block size prediction and algorithm classification. DCG achieves state-of-the-art performance on all tasks, reaching average accuracies of 87%, 53% and 99% on the three tasks respectively.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"162 ","pages":"Article 102551"},"PeriodicalIF":2.7,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883257","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":"All-words pronunciation estimation of Japanese homographs","authors":"Kanako Komiya ,&nbsp;Taichiro Kobayashi ,&nbsp;Masayuki Asahara ,&nbsp;Hiroyuki Shinnou","doi":"10.1016/j.datak.2025.102537","DOIUrl":"10.1016/j.datak.2025.102537","url":null,"abstract":"<div><div>The Japanese language has many homographs, which are words that share the same kanji characters, regardless of their pronunciations. Therefore, pronunciation estimation of homographs is necessary to read Japanese sentences accurately. We developed a system to estimate the pronunciations of homographs using a Bidirectional Encoder Representations from the Transformer model. This is the first research paper on pronunciation estimation of all homographs and we achieved this goal using the technique for all-word word sense disambiguation. We used the Corpus of Spontaneous Japanese (CSJ), a transcription of spoken Japanese, as the test data and utilized the non-core data of the Balanced Corpus of Contemporary Written Japanese, for which pronunciations are automatically tagged by a Japanese morphological analyzer, in addition to CSJ, as training data to reduce the cost of transcription. We also investigated the case where pseudo-pronunciation data was assigned to CSJ using a morphological analyzer as training examples. We show that automatically tagged data can improve the accuracy of pronunciation estimation.</div><div>Additionally, to evaluate an all-words pronunciation estimation system, we developed a dataset through crowdsourcing. We asked 20 crowdworkers to select pronunciations for the sentences from the Nihon Keizai Shimbun newspaper (the NIKKEI). For this NIKKEI data, multiple correct pronunciations were allowed, and the answer provided by the majority of crowdworkers was treated as the correct answer for evaluation purposes. When comparing the model trained on pseudo-data from BCCWJ with the model trained on pseudo-data from CSJ, the model using BCCWJ pseudo-data demonstrated superior performance.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"162 ","pages":"Article 102537"},"PeriodicalIF":2.7,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790026","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":"Exploring cutting-edge data ecosystems: A comprehensive analysis","authors":"Ioannis Chrysakis ,&nbsp;David Chaves-Fraga ,&nbsp;Giorgos Flouris ,&nbsp;Erik Mannens ,&nbsp;Anastasia Dimou","doi":"10.1016/j.datak.2025.102539","DOIUrl":"10.1016/j.datak.2025.102539","url":null,"abstract":"<div><div>Data-driven innovation has recently changed the mindset in data sharing from centralized architectures and monolithic data exploitation by data providers (data platforms) to decentralized architectures and different data sharing options among all involved participants (data ecosystems). Data sharing is further strengthened through the establishment of several legal frameworks (e.g., European Strategy for Data, Data Act, Data Governance Act) and the emerging initiatives that provide the means to build data ecosystems, which is evident in the formulated communities, established use cases, and the technical solutions. However, the data ecosystems have not been thoroughly studied so far. The differences between the various data ecosystems are not clear, making it hard to choose the most suitable for each use case, negatively impacting their adoption. Since the domain is growing fast, a review of the state-of-the-art data ecosystem initiatives is needed to analyze what each initiative offers, identify collaboration prospects, and highlight features for improvement and open research topics. In this paper, we review the state-of-the-art data ecosystem initiatives, describe their innovative aspects, compare their technical and business features, and identify open research challenges. We aim to assist practitioners in choosing the most suitable data ecosystem for their use cases and scientists to explore emerging research opportunities. Furthermore, we will provide a framework that outlines the key criteria for evaluating these initiatives, ensuring that stakeholders can make informed decisions based on their specific needs and objectives. By synthesizing our findings, we hope to foster a deeper understanding of the evolving landscape of data ecosystems and encourage further advancements in this critical field.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"162 ","pages":"Article 102539"},"PeriodicalIF":2.7,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736659","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":"Enhancing clustering stability, compactness, and separation in multimodal data environments","authors":"Fillipe dos Santos Silva ,&nbsp;Júlio Cesar dos Reis ,&nbsp;Marcelo da Silva Reis","doi":"10.1016/j.datak.2025.102536","DOIUrl":"10.1016/j.datak.2025.102536","url":null,"abstract":"<div><div>Effective customer segmentation, crucial for tailored marketing strategies, relies on stable and distinct clustering methods. Traditional clustering approaches often focus on structured data, limiting their effectiveness when handling multimodal information. This study originally introduces a multimodal framework to enhance clustering stability, compactness, and separation by integrating categorical, numerical, and textual data. Our framework addresses existing limitations through three core components: a transformer-based embedding model for textual analysis, a data fusion layer for integrating diverse data types, and a generative model for refining cluster consistency. We rigorously assess the effectiveness of our framework using five stability metrics: Adjusted Rand Index (ARI), Adjusted Mutual Information Score (AMIS), BagClust (BG), Hierarchical Agglomerative Nesting (HAN), and Optimal Transport Alignment (OTA). Additionally, we use the Davies–Bouldin Score (DBS) to evaluate cluster compactness and separation. Real-world datasets (Yelp, Melbourne Airbnb, PetFinder.my, Women’s Clothing Reviews) were used to benchmark our approach against four existing methods. Results demonstrate that our framework achieves superior clustering stability, compactness, and separation, advancing multimodal learning for more nuanced customer segmentation.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"162 ","pages":"Article 102536"},"PeriodicalIF":2.7,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618297","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":"S3: A simple strong sample-effective multimodal dialog system","authors":"Elisei Rykov ,&nbsp;Alexander Panchenko","doi":"10.1016/j.datak.2025.102538","DOIUrl":"10.1016/j.datak.2025.102538","url":null,"abstract":"<div><div>In this work, we present a conceptually simple yet powerful baseline for multimodal dialog task, an S<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span> model, that achieves near state-of-the-art results on four compelling leaderboards: MMMU, RealWorldQA, POPE, and AI Journey Contest 2023. The system is based on a pre-trained large language model, pre-trained modality encoders for image and audio, and a trainable modality projector. The proposed effective data mixture for training such an architecture demonstrates that a multimodal model based on a strong language model and trained on a small amount of multimodal data can perform efficiently in the task of multimodal dialog.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"162 ","pages":"Article 102538"},"PeriodicalIF":2.7,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684461","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":"“Detectors Lead, LLMs Follow”: Integrating LLMs and traditional models on implicit hate speech detection to generate faithful and plausible explanations","authors":"Greta Damo ,&nbsp;Nicolás Benjamín Ocampo ,&nbsp;Elena Cabrio,&nbsp;Serena Villata","doi":"10.1016/j.datak.2025.102535","DOIUrl":"10.1016/j.datak.2025.102535","url":null,"abstract":"<div><div>Social media platforms face a growing challenge in addressing abusive content and hate speech, particularly as traditional natural language processing methods often struggle with detecting nuanced and implicit instances. To tackle this issue, our study enhances Large Language Models (LLMs) in the detection and explanation of implicit hate speech, outperforming classical approaches. We focus on two key objectives: (1) determining whether jointly predicting and generating explanations for why a message is hateful improves LLMs’ accuracy, especially for implicit cases, and (2) evaluating whether incorporating information from BERT-based models can further boost detection and explanation performance. Our method evaluates and enhances LLMs’ ability to detect hate speech and explain their predictions. By combining binary classification (Hate Speech vs. Non-Hate Speech) with natural language explanations, our approach provides clearer insights into why a message is considered hateful, advancing the accuracy and interpretability of hate speech detection.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"162 ","pages":"Article 102535"},"PeriodicalIF":2.7,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617829","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}