Data & Knowledge Engineering最新文献

{"title":"Multi-treatment uplift evaluation on non-random assignment biased data","authors":"Nathan Le Boudec ,&nbsp;Nicolas Voisine ,&nbsp;Bruno Crémilleux","doi":"10.1016/j.datak.2026.102565","DOIUrl":"10.1016/j.datak.2026.102565","url":null,"abstract":"<div><div>Uplift quantifies the impact of an action (marketing, medical treatment) on an individual’s behavior. Uplift prediction is based on the assumption that the target and control groups are equivalent. However, in real-world scenarios, customers are often selected for actions based on their prior behavior, introducing non-random assignment bias that distorts uplift estimation. This issue is even more present in the case of multi-treatment, as in the context of offer recommendation system, where multiple actions are possible for an individual. To the best of our knowledge, the effect of bias in multi-treatment uplift has not yet been studied. In this paper, we propose a novel protocol for evaluating multi-treatment uplift under non-random assignment bias. Using this protocol, we assess the performance of the main multi-treatment uplift methods from the literature. Our results show significant differences in their robustness to bias, providing valuable insights and guidelines for practical applications in biased settings.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"163 ","pages":"Article 102565"},"PeriodicalIF":2.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146090307","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":"A contextual hierarchical attention network for detecting mental health disorders using social media","authors":"Ron Hochstenbach,&nbsp;Flavius Frasincar,&nbsp;Jasmijn Klinkhamer","doi":"10.1016/j.datak.2026.102560","DOIUrl":"10.1016/j.datak.2026.102560","url":null,"abstract":"<div><div>Growing parts of the population suffer from mental health problems and psychologists lack capacity to diagnose, let alone treat, all those in need of it. Given recent advancements in the field, deep learning-based NLP techniques could help by detecting those in need of help based on their written text. To this end, this work improves the current state-of-the-art Hierarchical Attention Network (HAN) model by incorporating contextual awareness through BERT-based word embeddings and a multi-head self-attention user-encoder yielding the Context-HAN model. When trained and tested on the eRisk data sets on Self-Harm, Anorexia, and Depression, Context-HAN outperformed the HAN model across all data sets based on various evaluation measures. Furthermore, we find and discuss some interesting insights from analysis of the attention scores, such as that longer and more recently written posts are more important for classification. This work shows the potential of attention mechanisms to leverage contextual information to improve the effectiveness of NLP methods at detecting mental health disorders from user-written text.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"163 ","pages":"Article 102560"},"PeriodicalIF":2.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039183","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":"An integrated approach to GDPR-compliant data sharing employing consent, contracts, and licenses","authors":"Amar Tauqeer ,&nbsp;Tek Raj Chhetri ,&nbsp;Robert David ,&nbsp;Albin Ahmeti ,&nbsp;Anna Fensel","doi":"10.1016/j.datak.2025.102510","DOIUrl":"10.1016/j.datak.2025.102510","url":null,"abstract":"<div><div>GDPR defines six legal bases, at least one of which needs to be followed in order to process (or share) personally identifiable data in a lawful manner. Most of the research today is centered around the legal bases of consent and contracts. This limits the options for legal bases that one can select (or use) for data sharing, especially in circumstances where there is a need to use multiple legal bases. For example, one can consent to share data but may want to place restrictions on how it can be used, which requires a license (an extension/add-on to data sharing contracts) in scenarios, where digital assets licensing is involved. Overcoming these limitations and enabling data sharing via multiple legal bases require combining multiple legal bases. However, incorporating additional (or multiple) legal bases, such as licenses (as an add-on to contracts), in a GDPR-compliant manner remains a challenging task. This is because combining multiple legal bases requires an understanding of each individual legal basis—a task challenging in itself—and designing a system in a manner that is both compliant with regulatory requirements and practically pertinent. Therefore, in this paper, we present our semantic-based approach and tool that enables GDPR-compliant data sharing via multiple legal bases, consent, and contracts (using licenses as an add-on). This work extends our previous work, GDPR Contract Compliance Verification (CCV) tool, which enables GDPR-compliant data sharing via consent and contracts only. We add licenses as a further add-on to contracts, make our previous work more semantically compliant by utilizing SHACL validation for compliance checking, secure the contract signing process with digital signatures, introduce SHACL repairs to automatically fix data inconsistencies, and evaluate the performance of the tool and the SHACL components. We demonstrate the effectiveness of SHACL and the enhancement of the tool with GDPR-complaint data sharing based on multiple legal bases by performance testing.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"162 ","pages":"Article 102510"},"PeriodicalIF":2.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684540","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":"2026-03-01","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":"Debiasing judgmental decisions by providing individual error pattern feedback","authors":"Nathalie Balla,&nbsp;Thomas Setzer","doi":"10.1016/j.datak.2025.102530","DOIUrl":"10.1016/j.datak.2025.102530","url":null,"abstract":"<div><div>We present a Decision Support System (DSS) that provides experts with feedback on their personal potential bias based on their previous error pattern. Feedback is calculated using a knowledge database containing a library of biases and typical error patterns that suggest them. An error pattern means any identifiable structure of errors. For instance, an inference engine might detect continuously too high forecasts of an expert submitted via a user interface, regularly exceeding the actual quantities observed later. The engine might then positively evaluate a rule indicating an overestimation bias and provide feedback on the detected error pattern and/or the presumed bias, potentially including further explanations. As the feedback stems from an expert’s own error pattern, it intends to enhance their self-reflection and support wise consideration of the feedback. We assume that this allows experts to acquire knowledge about their own flawed judgmental heuristics, that experts are able to apply the feedback systematically and selectively to different decision tasks and to therefore reduce their potential bias and error. To test these assumptions, we conduct experiments with the DSS. Therein, subjects provide point estimations as well as certainty intervals and subsequently receive error feedback given by a machine based on his or her previous answers. After the feedback, subjects answer further questions. Results indicate that subjects reflect on their own error pattern and apply the feedback selectively to further, upcoming estimations and reduce overall bias and error.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"162 ","pages":"Article 102530"},"PeriodicalIF":2.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617828","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":"2026-03-01","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":"Query-based automatic text summarization using query expansion approach","authors":"Hiteshwar Kumar Azad","doi":"10.1016/j.datak.2025.102531","DOIUrl":"10.1016/j.datak.2025.102531","url":null,"abstract":"<div><div>The amount of information available on the Web has grown dramatically and continues to grow on a daily basis. The massive amount of Web data poses significant challenges to the reliability and accuracy of current information retrieval systems. The purpose of information retrieval is to discover relevant documents within a huge group of documents whose contents match a user-initiated query. Because most users struggle to formulate well-defined queries, the query expansion technique is critical for retrieving the most relevant information. Obtaining relevant results in a concise manner is a significant challenge in this scenario. Automatic text summarization can condense a lengthy document while retaining its informative content and key concepts. It could be a potential solution to information overload. This paper proposed a query-based automatic text summarization technique that employs query expansion to improve text summarization and provide the relevant information in a concise manner. To produce a relevant text summary, this article employs a query-based extractive text summarization method, which involves selecting sentences based on the four best features retrieved from each sentence. In this process, the words are scored by the expanded query’s score, and the sentences are scored by four important features, including sentence terms, position, similarity to the first sentence, and proper noun. Extensive experiments with different ROUGE variants on various evaluation metrics, including precision, recall, and F-score, were carried out on the DUC 2007 dataset, with gains of approximately 44%, 46%, and 45% respectively, in the best scenario. It is observed that the suggested approach outperforms both DUC participatory systems and cutting-edge approaches in summary generation.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"162 ","pages":"Article 102531"},"PeriodicalIF":2.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145532425","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":"Editorial Preface for “Selected Papers from the 18th International Conference on Research Challenges in Information Science (RCIS 2024)”","authors":"Jose Luis de la Vara ,&nbsp;João Araújo ,&nbsp;Maribel Yasmina Santos ,&nbsp;Saïd Assar","doi":"10.1016/j.datak.2026.102555","DOIUrl":"10.1016/j.datak.2026.102555","url":null,"abstract":"","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"162 ","pages":"Article 102555"},"PeriodicalIF":2.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187679","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":"2026-03-01","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":"“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":"2026-03-01","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}