Data & Knowledge Engineering最新文献

{"title":"Rule-guided process discovery","authors":"Ali Norouzifar ,&nbsp;Marcus Dees ,&nbsp;Wil van der Aalst","doi":"10.1016/j.datak.2025.102508","DOIUrl":"10.1016/j.datak.2025.102508","url":null,"abstract":"<div><div>Event data extracted from information systems serves as the foundation for process mining, enabling the extraction of insights and identification of improvements. Process discovery focuses on deriving descriptive process models from event logs, which form the basis for conformance checking, performance analysis, and other applications. Traditional process discovery techniques predominantly rely on event logs, often overlooking supplementary information such as domain knowledge and process rules. These rules, which define relationships between activities, can be obtained through automated techniques like declarative process discovery or provided by domain experts based on process specifications. When used as an additional input alongside event logs, such rules have significant potential to guide process discovery. However, leveraging rules to discover high-quality imperative process models, such as BPMN models and Petri nets, remains an underexplored area in the literature. To address this gap, we propose an enhanced framework, IMr, which integrates discovered or user-defined rules into the process discovery workflow via a novel recursive approach. The IMr framework employs a divide-and-conquer strategy, using rules to guide the selection of process structures at each recursion step in combination with the input event log. We evaluate our approach on several real-world event logs and demonstrate that the discovered models better align with the provided rules without compromising their conformance to the event log. Additionally, we show that high-quality rules can improve model quality across well-known conformance metrics. This work highlights the importance of integrating domain knowledge into process discovery, enhancing the quality, interpretability, and applicability of the resulting process models.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"161 ","pages":"Article 102508"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048987","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 optimization enabled Hierarchical Attention -Deep LSTM model for sentiment analysis on cloth products from customer rating","authors":"Zhijun Chen ,&nbsp;Tsungshun Hsieh ,&nbsp;Ze Chen","doi":"10.1016/j.datak.2025.102523","DOIUrl":"10.1016/j.datak.2025.102523","url":null,"abstract":"<div><div>The primary aim of the study endeavour is to introduce a deep learning approaches augmented with optimization techniques to conduct sentiment analysis on apparel products, utilize customer reviews and ratings as foundational data. Consequently, a review of a clothing item is utilized as input, which undergoes pre-processing involving the elimination of stop words and stemming to eradicate superfluous information. In parallel, critical features are extracted from the pre-processed data to facilitate effective categorization. Thereafter, feature extraction is executed through execution of Term frequency-inverse document frequency (TF-IDF), SentiWordNet features, positive sentiment scores, negative sentiment scores, the count of capitalized words, and hashtags. Subsequently, feature fusion is conducted utilizing the proposed Trend factor smoothing-Siberian Tiger Optimization (TS-STO), which is innovatively premeditated by integrating trend factor smoothing within the update process of Siberian Tiger Optimization (STO). Ultimately, sentiment analysis is conducted through the implementation of HA-Deep LSTM, which is conceived by merging Hierarchical Attention Network with Deep LSTM. Experimental analysis portrayed that presented approach conquered an accuracy of 95.9 %, a sensitivity of 96.1 % and specificity of 94.2 %.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"161 ","pages":"Article 102523"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266451","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":"Elevating human-machine collaboration in NLP for enhanced content creation and decision support","authors":"Priyanka V. Deshmukh,&nbsp;Aniket K. Shahade","doi":"10.1016/j.datak.2025.102505","DOIUrl":"10.1016/j.datak.2025.102505","url":null,"abstract":"<div><div>Human-machine collaboration in Natural Language Processing (NLP) is revolutionizing content creation and decision support by seamlessly combining the strengths of both entities for enhanced efficiency and quality. The lack of seamless integration between human creativity and machine efficiency in NLP hinders optimal content creation and decision support. The objective of this study is to explore and promote the integration of human-machine collaboration in NLP to enhance both content creation and decision support processes. Data Acquisition for NLP requests involves defining the task and target audience, identifying relevant data sources like text documents and web data, and incorporating human expertise for data curation through validation and annotation. Machine processing techniques like tokenization, stemming/lemmatization, and removal of stop words, as well as human input for tasks like data annotation and error correction, to improve data quality and relevance for NLP applications. The combination of automated processing and human feedback leads to more precise and dependable effects. Techniques such as sentiment analysis, topic modelling, and entity recognition are utilized to excerpt valued perceptions from the data and enhance collaboration between humans and machines. These techniques help to streamline the NLP process and ensure that the system is providing accurate and relevant information to users. The analysis of NLP models in machine processing involves training the models to perform specific tasks, such as summarization, sentiment analysis, information extraction, trend identification, and creative content generation. The results show that social media leads with 90% usage, pivotal for audience engagement, while blogs at 78% highlight their depth in content creation implementation using Python software. These trained models are then used to improve decision-making processes, generate creative content, and enhance the accuracy of search results. The future scope involves leveraging advanced NLP techniques to deepen the collaboration between humans and machines for more effective content creation and decision support.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"161 ","pages":"Article 102505"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908780","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":"Knowledge graph question generation based on crucial semantic information","authors":"Mingtao Zhou ,&nbsp;Juxiang Zhou ,&nbsp;Jianhou Gan ,&nbsp;Jun Wang ,&nbsp;Jiatian Mei","doi":"10.1016/j.datak.2025.102529","DOIUrl":"10.1016/j.datak.2025.102529","url":null,"abstract":"<div><div>The aim of the knowledge graph-based question generation (KGQG) task is to generate an answerable, fluent question from a ternary knowledge graph and a target answer. Existing KGQG that study knowledge graph subgraphs and the question of target answer generation do not effectively capture the critical semantic information between tokens within nodes/edges in subgraphs and fail to make full use of target answers and answer markers. This has led to the generation of disfluent and unanswerable questions. To address these problems, we propose a model called knowledge graph question generation based on crucial semantic information (KGQG-CSI). Our proposed model utilizes the critical semantic information encoding module to dynamically learn the degree of significance of tokens within the edges and nodes of fused answers, capturing critical semantic information that would remedy disfluency. In addition, the target answers and answer markers are sufficiently integrated with the nodes to make the generated questions answerable. First, the attention mechanism is used to allow the nodes to interact with the target answers, thereby expressing the semantic information related to the answers more accurately. The nodes that have been processed through the critical semantic information encoding module are then spliced with the answer markers to reduce the ambiguous information. The experimental results on two public datasets show that the results of the proposed model outperform the existing methods.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"161 ","pages":"Article 102529"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361577","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":"Conceptual modeling of user perspectives — From data warehouses to alliance-driven data ecosystems","authors":"Sandra Geisler ,&nbsp;Christoph Quix ,&nbsp;István Koren ,&nbsp;Matthias Jarke","doi":"10.1016/j.datak.2025.102502","DOIUrl":"10.1016/j.datak.2025.102502","url":null,"abstract":"<div><div>The increasing complexity of modern information systems has highlighted the need for advanced conceptual modeling techniques that incorporate multi-perspective and view-based approaches. This paper explores the role of multi-perspective modeling and view modeling in designing distributed, heterogeneous systems while addressing diverse user requirements and ensuring semantic consistency. These methods enable the representation of multiple viewpoints, traceability, and dynamic integration across different levels of abstraction. Key advancements in schema mapping, view maintenance, and semantic metadata management are examined, illustrating how they support query optimization, data quality, and interoperability. We discuss how data management architectures, such as data ecosystems, data warehouses, and data lakes, leverage these innovations to enable flexible and sustainable data sharing. By integrating user-centric and goal-oriented modeling frameworks, the alignment of technical design with organizational and social requirements is emphasized. Future challenges include the need for enhanced reasoning capabilities and collaborative tools to manage the growing complexity of interconnected systems while maintaining adaptability and trust.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"161 ","pages":"Article 102502"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988186","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":"LQ-FJS: A logical query-digging fake-news judgment system with structured video-summarization engine using LLM","authors":"Jhing-Fa Wang ,&nbsp;Din-Yuen Chan ,&nbsp;Hsin-Chun Tsai ,&nbsp;Bo-Xuan Fang","doi":"10.1016/j.datak.2025.102507","DOIUrl":"10.1016/j.datak.2025.102507","url":null,"abstract":"<div><div>The proliferation of online social platforms can greatly benefit people by fostering remote relationships, but it also inevitably amplifies the impact of multimodal fake news on societal trust and ethics. Existing fake-news detection AI systems are still vulnerable to the inconspicuous and indiscernible multimodal misinformation, and often lacking interpretability and accuracy in cross-platform settings. Hence, we propose a new innovative logical query-digging fake-news judgment system (LQ-FJS) to tackle the above problem based on multimodal approach. The LQ-FJS verifies the truthfulness of claims made within multimedia news by converting video content into structured textual summaries. It then acts as an interpretable agent, explaining the reasons for identified fake news by the structured video-summarization engine (SVSE) to act as an interpretable detection intermediary agent. The SVSE generates condensed captions for raw video content, converting it into structured textual narratives. Then, LQ-FJS exploits these condensed captions to retrieve reliable information related to the video content from LLM. Thus, LQ-FJS cross-verifies external knowledge sources and internal LLM responses to determine whether contradictions exist with factual information through a multimodal inconsistency verification procedure. Our experiments demonstrate that the subtle summarization produced by SVSE can facilitate the generation of explanatory reports that mitigate large-scale trust deficits caused by opaque “black-box” models. Our experiments show that LQ-FJS improves F1 scores by 4.5% and 7.2% compared to state-of-the-art models (FactLLaMA 2023 and HiSS 2023), and increases 14% user trusts through interpretable conclusions.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"161 ","pages":"Article 102507"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019198","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":"ELEVATE-ID: Extending Large Language Models for End-to-End Entity Linking Evaluation in Indonesian","authors":"Ria Hari Gusmita ,&nbsp;Asep Fajar Firmansyah ,&nbsp;Hamada M. Zahera ,&nbsp;Axel-Cyrille Ngonga Ngomo","doi":"10.1016/j.datak.2025.102504","DOIUrl":"10.1016/j.datak.2025.102504","url":null,"abstract":"<div><div>Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks. However, their effectiveness in low-resource languages remains underexplored, particularly in complex tasks such as end-to-end Entity Linking (EL), which requires both mention detection and disambiguation against a knowledge base (KB). In earlier work, we introduced IndEL — the first end-to-end EL benchmark dataset for the Indonesian language — covering both a general domain (news) and a specific domain (religious text from the Indonesian translation of the Quran), and evaluated four traditional end-to-end EL systems on this dataset. In this study, we propose ELEVATE-ID, a comprehensive evaluation framework for assessing LLM performance on end-to-end EL in Indonesian. The framework evaluates LLMs under both zero-shot and fine-tuned conditions, using multilingual and Indonesian monolingual models, with Wikidata as the target KB. Our experiments include performance benchmarking, generalization analysis across domains, and systematic error analysis. Results show that GPT-4 and GPT-3.5 achieve the highest accuracy in zero-shot and fine-tuned settings, respectively. However, even fine-tuned GPT-3.5 underperforms compared to DBpedia Spotlight — the weakest of the traditional model baselines — in the general domain. Interestingly, GPT-3.5 outperforms Babelfy in the specific domain. Generalization analysis indicates that fine-tuned GPT-3.5 adapts more effectively to cross-domain and mixed-domain scenarios. Error analysis uncovers persistent challenges that hinder LLM performance: difficulties with non-complete mentions, acronym disambiguation, and full-name recognition in formal contexts. These issues point to limitations in mention boundary detection and contextual grounding. Indonesian-pretrained LLMs, Komodo and Merak, reveal core weaknesses: template leakage and entity hallucination, respectively—underscoring architectural and training limitations in low-resource end-to-end EL.<span><span>¹</span></span></div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"161 ","pages":"Article 102504"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889217","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 survey of processing methods for different types of concept drift","authors":"Shurong Yang,&nbsp;Meng Han,&nbsp;Shineng Zhu,&nbsp;Wenyan Yang,&nbsp;Zhenlong Dai,&nbsp;Juan Li,&nbsp;Jian Ding","doi":"10.1016/j.datak.2025.102525","DOIUrl":"10.1016/j.datak.2025.102525","url":null,"abstract":"<div><div>For large, dynamic and continuously changing data streams, the characteristics and categories of data samples change with the increase of time, which is called concept drift. Concept drift has an impact on the stability and performance of the model, if ignored, the prediction will be inaccurate. More and more attention has been paid to the study of multi-data streams. Group concept drift is more complicated than single data stream concept drift. This paper studies the processing methods of group concept drift, and classifies the processing methods from three directions of single type, multiple type and group type for the first time. Within the scope of methods designed to address a single type of concept drift, this paper provides a systematic summary based on the classification of drift types, distinguishing between approaches that target real concept drift and those that address virtual concept drift. Solving real concept drift includes abrupt, gradual, recurrent and local concept drift processing methods. In this paper, the methods of dealing with the concept drift of multiple type are introduced from the aspects of solving the drift of gradual and abrupt, abrupt, gradual and incremental, abrupt, gradual and recurrent. This paper analyzes and summarizes the methods to solve the group type concept drift problem. The methods considered in this paper are compared and summarized from the aspects of processing technology, applicable drift type, comparison methods, advantages and disadvantages.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"161 ","pages":"Article 102525"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465448","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 framework for purpose-guided event logs generation","authors":"Andrea Burattin ,&nbsp;Barbara Re ,&nbsp;Lorenzo Rossi ,&nbsp;Francesco Tiezzi","doi":"10.1016/j.datak.2025.102526","DOIUrl":"10.1016/j.datak.2025.102526","url":null,"abstract":"<div><div>Process mining is a prominent discipline in business process management. It collects a variety of techniques for gathering information from event logs, each fulfilling a different mining purpose. Event logs are always necessary for assessing and validating mining techniques in relation to specific purposes. Unfortunately, event logs are hard to find and usually contain noise that can influence the validity of the results of a mining technique. In this paper, we propose a framework, named <span>purple</span>, for generating, through business model simulation, event logs tailored for different mining purposes, i.e., discovery, what-if analysis, and conformance checking. It supports the simulation of models specified in different languages, by projecting their execution onto a common behavioral model, i.e., a labeled transition system. We present eleven instantiations of the framework implemented in a software tool by-product of this paper. The framework is validated against reference log generators through experiments on the purposes presented in the paper.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"161 ","pages":"Article 102526"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145319482","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":"Unveiling cancellation dynamics: A two-stage model for predictive analytics","authors":"Soumyadeep Kundu ,&nbsp;Soumya Roy ,&nbsp;Archit Shukla ,&nbsp;Arqum Mateen","doi":"10.1016/j.datak.2025.102467","DOIUrl":"10.1016/j.datak.2025.102467","url":null,"abstract":"<div><div>Booking cancellations have an adverse impact on the performance of firms in the hospitality industry. Most of the studies in this domain have considered the questions of whether a booking would be cancelled or not (if). While useful, given the nature of the industry, it would be important to understand the timing of cancellation as well (when). Answering the inter-temporal nature of the question would help hotels to devise appropriate strategies to accommodate this change. In our study, we have proposed a novel two-stage model, which predicts both the likelihood (if) as well as the timing (when) of cancellation, using various statistical and machine learning techniques. We find that significant predictors include the average daily rate (which is an indicator of average rental revenue earned for an occupied room per day), month of arrival, day of arrival, and the lead time. Our insights can help hotels design bespoke cancellation policies and exercise personalised services and interventions for guests.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"160 ","pages":"Article 102467"},"PeriodicalIF":2.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279321","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}