Data & Knowledge Engineering最新文献_第6页

Coupling MDL and Markov chain Monte Carlo to sample diverse pattern sets 耦合MDL和马尔可夫链蒙特卡罗采样不同的模式集

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-12-20 DOI: 10.1016/j.datak.2024.102393

François Camelin , Samir Loudni , Gilles Pesant , Charlotte Truchet

引用次数: 0

HiBenchLLM: Historical Inquiry Benchmarking for Large Language Models HiBenchLLM：大型语言模型的历史查询基准测试

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-12-15 DOI: 10.1016/j.datak.2024.102383

Mathieu Chartier , Nabil Dakkoune , Guillaume Bourgeois , Stéphane Jean

引用次数: 0

Clustering of timed sequences – Application to the analysis of care pathways 时间序列的聚类。护理路径分析中的应用

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-12-10 DOI: 10.1016/j.datak.2024.102401

Thomas Guyet , Pierre Pinson , Enoal Gesny

引用次数: 0

Semantic vs. LLM-based approach: A case study of KOnPoTe vs. Claude for ontology population from French advertisements 语义vs.基于法学硕士的方法：KOnPoTe vs. Claude对法国广告本体人口的案例研究

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-12-04 DOI: 10.1016/j.datak.2024.102392

Aya Sahbi , Céline Alec , Pierre Beust

引用次数: 0

Curvature constrained MPNNs: Improving message passing with local structural properties 曲率约束的mpnn：利用局部结构特性改进消息传递

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-11-29 DOI: 10.1016/j.datak.2024.102382

Hugo Attali, Davide Buscaldi, Nathalie Pernelle

引用次数: 0

Improving multi-view ensemble learning with Round-Robin feature set partitioning 利用循环特征集划分改进多视图集成学习

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-11-24 DOI: 10.1016/j.datak.2024.102380

Aditya Kumar , Jainath Yadav

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引用次数: 0

White box specification of intervention policies for prescriptive process monitoring 规定性流程监控干预政策的白盒规范

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-11-23 DOI: 10.1016/j.datak.2024.102379

Mahmoud Shoush, Marlon Dumas

{"title":"White box specification of intervention policies for prescriptive process monitoring","authors":"Mahmoud Shoush, Marlon Dumas","doi":"10.1016/j.datak.2024.102379","DOIUrl":"10.1016/j.datak.2024.102379","url":null,"abstract":"<div><div>Prescriptive process monitoring methods seek to enhance business process performance by triggering real-time interventions, such as offering discounts to increase the likelihood of a positive outcome (e.g., a purchase). At the core of a prescriptive process monitoring method lies an intervention policy, which determines under which conditions and when to trigger an intervention. While state-of-the-art prescriptive process monitoring approaches rely on black-box intervention policies derived through reinforcement learning, algorithmic decision-making requirements sometimes dictate that the business stakeholders must be able to understand, justify, and adjust these intervention policies manually. To address this requirement, this article proposes <em>WB-PrPM</em> (White-Box Prescriptive Process Monitoring), a framework that enables stakeholders to define intervention policies in business processes. WB-PrPM is a rule-based system that helps decision-makers balance the demand for effective interventions with the imperatives of limited resource capacity. The framework incorporates an automated method for tuning the parameters of the intervention policies to optimize a total gain function. An evaluation is presented using real-life datasets to examine the tradeoffs among various parameters. The evaluation reveals that different variants of the proposed framework outperform existing baselines in terms of total gain, even when default parameter values are used. Additionally, the automated parameter optimization approach further enhances the total gain.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"155 ","pages":"Article 102379"},"PeriodicalIF":2.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721292","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

A goal-oriented document-grounded dialogue based on evidence generation 基于证据生成的以目标为导向、以文件为基础的对话

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-11-22 DOI: 10.1016/j.datak.2024.102378

Yong Song , Hongjie Fan , Junfei Liu , Yunxin Liu , Xiaozhou Ye , Ye Ouyang

{"title":"A goal-oriented document-grounded dialogue based on evidence generation","authors":"Yong Song , Hongjie Fan , Junfei Liu , Yunxin Liu , Xiaozhou Ye , Ye Ouyang","doi":"10.1016/j.datak.2024.102378","DOIUrl":"10.1016/j.datak.2024.102378","url":null,"abstract":"<div><div>Goal-oriented Document-grounded Dialogue (DGD) is used for retrieving specific domain documents, assisting users in document content retrieval, question answering, and document management. Existing methods typically employ keyword extraction and vector space models to understand the content of documents, identify the intent of questions, and generate answers based on the capabilities of generation models. However, challenges remain in semantic understanding, long text processing, and context understanding. The emergence of Large Language Models (LLMs) has brought new capabilities in context learning and step-by-step reasoning. These models, combined with Retrieval Augmented Generation(RAG) methods, have made significant breakthroughs in text comprehension, intent detection, language organization, offering exciting prospects for DGD research. However, the “hallucination” issue arising from LLMs requires complementary methods to ensure the credibility of their outputs. In this paper we propose a goal-oriented document-grounded dialogue approach based on evidence generation using LLMs. It designs and implements methods for document content retrieval & reranking, fine-tuning and inference, and evidence generation. Through experiments, the method of combining LLMs with vector space model, or with key information matching technique is used as a comparison, the accuracy of the proposed method is improved by 21.91% and 12.81%, while the comprehensiveness is increased by 10.89% and 69.83%, coherence is enhanced by 38.98% and 53.27%, and completeness is boosted by 16.13% and 36.97%, respectively, on average. Additional, ablation analysis conducted reveals that the evidence generation method also contributes significantly to the comprehensiveness and completeness.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"155 ","pages":"Article 102378"},"PeriodicalIF":2.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705366","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

Data-aware process models: From soundness checking to repair 数据感知流程模型：从健全性检查到修复

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-11-17 DOI: 10.1016/j.datak.2024.102377

Matteo Zavatteri, Davide Bresolin, Massimiliano de Leoni, Aurelo Makaj

引用次数: 0

Context normalization: A new approach for the stability and improvement of neural network performance 上下文正常化：稳定和提高神经网络性能的新方法

IF 2.7 3区计算机科学

Data & Knowledge Engineering Pub Date : 2024-11-15 DOI: 10.1016/j.datak.2024.102371

Bilal Faye , Hanane Azzag , Mustapha Lebbah , Fangchen Feng

引用次数: 0