Pub Date : 2024-11-24DOI: 10.1016/j.datak.2024.102380
Aditya Kumar , Jainath Yadav
Multi-view Ensemble Learning (MEL) techniques have shown remarkable success in improving the accuracy and resilience of classification algorithms by combining multiple base classifiers trained over different perspectives of a dataset, known as views. One crucial factor affecting ensemble performance is the selection of diverse and informative feature subsets. Feature Set Partitioning (FSP) methods address this challenge by creating distinct views of features for each base classifier. In this context, we propose the Round-Robin Feature Set Partitioning (-FSP) technique, which introduces a novel approach to feature allocation among views. This novel approach evenly distributes highly correlated features across views, thereby enhancing ensemble diversity, promoting balanced feature utilization, and encouraging the more equitable distribution of correlated features, -FSP contributes to the advancement of MEL techniques. Through experiments on various datasets, we demonstrate that -FSP offers improved classification accuracy and robustness, making it a valuable addition to the arsenal of FSP techniques for MEL.
多视图集成学习（MEL）技术通过组合在数据集的不同视角（称为视图）上训练的多个基本分类器，在提高分类算法的准确性和弹性方面取得了显著的成功。影响集成性能的一个关键因素是选择多样化和信息丰富的特征子集。Feature Set Partitioning （FSP）方法通过为每个基本分类器创建不同的特征视图来解决这一挑战。在此背景下，我们提出了循环特征集分区（RR-FSP）技术，该技术引入了一种新的视图间特征分配方法。该方法将高度相关的特征均匀分布在视图中，从而增强了集成多样性，促进了特征的平衡利用，并促进了相关特征的更公平分布。RR-FSP有助于MEL技术的发展。通过对各种数据集的实验，我们证明了RR-FSP提供了更高的分类精度和鲁棒性，使其成为用于MEL的FSP技术库的一个有价值的补充。
{"title":"Improving multi-view ensemble learning with Round-Robin feature set partitioning","authors":"Aditya Kumar , Jainath Yadav","doi":"10.1016/j.datak.2024.102380","DOIUrl":"10.1016/j.datak.2024.102380","url":null,"abstract":"<div><div>Multi-view Ensemble Learning (MEL) techniques have shown remarkable success in improving the accuracy and resilience of classification algorithms by combining multiple base classifiers trained over different perspectives of a dataset, known as views. One crucial factor affecting ensemble performance is the selection of diverse and informative feature subsets. Feature Set Partitioning (FSP) methods address this challenge by creating distinct views of features for each base classifier. In this context, we propose the Round-Robin Feature Set Partitioning (<span><math><mi>RR</mi></math></span>-FSP) technique, which introduces a novel approach to feature allocation among views. This novel approach evenly distributes highly correlated features across views, thereby enhancing ensemble diversity, promoting balanced feature utilization, and encouraging the more equitable distribution of correlated features, <span><math><mi>RR</mi></math></span>-FSP contributes to the advancement of MEL techniques. Through experiments on various datasets, we demonstrate that <span><math><mi>RR</mi></math></span>-FSP offers improved classification accuracy and robustness, making it a valuable addition to the arsenal of FSP techniques for MEL.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"156 ","pages":"Article 102380"},"PeriodicalIF":2.7,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743038","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}
Pub Date : 2024-11-23DOI: 10.1016/j.datak.2024.102379
Mahmoud Shoush, Marlon Dumas
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 WB-PrPM (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.
{"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}
Pub Date : 2024-11-22DOI: 10.1016/j.datak.2024.102378
Yong Song , Hongjie Fan , Junfei Liu , Yunxin Liu , Xiaozhou Ye , Ye Ouyang
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.
{"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}
Pub Date : 2024-11-17DOI: 10.1016/j.datak.2024.102377
Matteo Zavatteri, Davide Bresolin, Massimiliano de Leoni, Aurelo Makaj
Process-aware Information Systems support the enactment of business processes and rely on a model that prescribes which executions are allowed. As a result, the model needs to be sound for the process to be carried out. Traditionally, soundness has been defined and studied by only focusing on the control-flow. Some works proposed techniques to repair the process model to ensure soundness, ignoring data and decision perspectives. This paper puts forward a technique to repair the data perspective of process models, keeping intact the control flow structure. Processes are modeled by Data Petri nets. Our approach repairs the Constraint Graph, a finite symbolic abstraction of the infinite state–space of the underlying Data Petri net. The changes in the Constraint Graph are then projected back onto the Data Petri net.
流程感知信息系统支持业务流程的制定，并依赖于一个规定允许执行哪些流程的模型。因此，要执行流程，该模型必须是合理的。传统上，对合理性的定义和研究只关注控制流。一些著作提出了修复流程模型以确保其合理性的技术，但忽略了数据和决策视角。本文提出了一种在保持控制流结构不变的情况下修复流程模型数据视角的技术。流程由数据 Petri 网建模。我们的方法修复了约束图，它是底层数据 Petri 网无限状态空间的有限符号抽象。然后，将约束图中的变化投射回数据 Petri 网。
{"title":"Data-aware process models: From soundness checking to repair","authors":"Matteo Zavatteri, Davide Bresolin, Massimiliano de Leoni, Aurelo Makaj","doi":"10.1016/j.datak.2024.102377","DOIUrl":"10.1016/j.datak.2024.102377","url":null,"abstract":"<div><div>Process-aware Information Systems support the enactment of business processes and rely on a model that prescribes which executions are allowed. As a result, the model needs to be sound for the process to be carried out. Traditionally, soundness has been defined and studied by only focusing on the control-flow. Some works proposed techniques to repair the process model to ensure soundness, ignoring data and decision perspectives. This paper puts forward a technique to repair the data perspective of process models, keeping intact the control flow structure. Processes are modeled by Data Petri nets. Our approach repairs the Constraint Graph, a finite symbolic abstraction of the infinite state–space of the underlying Data Petri net. The changes in the Constraint Graph are then projected back onto the Data Petri net.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"155 ","pages":"Article 102377"},"PeriodicalIF":2.7,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705367","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}
Deep neural networks face challenges with distribution shifts across layers, affecting model convergence and performance. While Batch Normalization (BN) addresses these issues, its reliance on a single Gaussian distribution assumption limits adaptability. To overcome this, alternatives like Layer Normalization, Group Normalization, and Mixture Normalization emerged, yet struggle with dynamic activation distributions. We propose ”Context Normalization” (CN), introducing contexts constructed from domain knowledge. CN normalizes data within the same context, enabling local representation. During backpropagation, CN learns normalized parameters and model weights for each context, ensuring efficient convergence and superior performance compared to BN and MN. This approach emphasizes context utilization, offering a fresh perspective on activation normalization in neural networks. We release our code at https://github.com/b-faye/Context-Normalization.
{"title":"Context normalization: A new approach for the stability and improvement of neural network performance","authors":"Bilal Faye , Hanane Azzag , Mustapha Lebbah , Fangchen Feng","doi":"10.1016/j.datak.2024.102371","DOIUrl":"10.1016/j.datak.2024.102371","url":null,"abstract":"<div><div>Deep neural networks face challenges with distribution shifts across layers, affecting model convergence and performance. While Batch Normalization (BN) addresses these issues, its reliance on a single Gaussian distribution assumption limits adaptability. To overcome this, alternatives like Layer Normalization, Group Normalization, and Mixture Normalization emerged, yet struggle with dynamic activation distributions. We propose ”Context Normalization” (CN), introducing contexts constructed from domain knowledge. CN normalizes data within the same context, enabling local representation. During backpropagation, CN learns normalized parameters and model weights for each context, ensuring efficient convergence and superior performance compared to BN and MN. This approach emphasizes context utilization, offering a fresh perspective on activation normalization in neural networks. We release our code at <span><span>https://github.com/b-faye/Context-Normalization</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"155 ","pages":"Article 102371"},"PeriodicalIF":2.7,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-07DOI: 10.1016/j.datak.2024.102374
Jamila Oukharijane , Mohamed Amine Chaâbane , Imen Ben Said , Eric Andonoff , Rafik Bouaziz
Self-adaptation of business processes has become the focus of several research studies aiming at avoiding a manual adaptation of processes at run-time, which is error-prone and time-consuming. In fact, several contributions addressing the self-adaptation of processes have been proposed in the literature, but none of them has comprehensively studied and analyzed the literature to assess the current state of progress in the self-adaptation of processes. To address this gap, we propose in this paper a comprehensive taxonomy that identifies a set of characteristics to serve as support for the comparative analysis of solutions addressing self-adaptation of processes. Our taxonomy includes 25 characteristics that address relevant questions regarding self-adaptation of processes. While creating our taxonomy, we built on existing literature and involved academic experts from different universities. These experts did not only validate our taxonomy regarding completeness and understandability, but also rectified and enriched it with their knowledge. Finally, we report the application of this taxonomy to evaluate some existing contributions on self-adaptation of processes.
{"title":"An assessment taxonomy for self-adaptation business process solutions","authors":"Jamila Oukharijane , Mohamed Amine Chaâbane , Imen Ben Said , Eric Andonoff , Rafik Bouaziz","doi":"10.1016/j.datak.2024.102374","DOIUrl":"10.1016/j.datak.2024.102374","url":null,"abstract":"<div><div>Self-adaptation of business processes has become the focus of several research studies aiming at avoiding a manual adaptation of processes at run-time, which is error-prone and time-consuming. In fact, several contributions addressing the self-adaptation of processes have been proposed in the literature, but none of them has comprehensively studied and analyzed the literature to assess the current state of progress in the self-adaptation of processes. To address this gap, we propose in this paper a comprehensive taxonomy that identifies a set of characteristics to serve as support for the comparative analysis of solutions addressing self-adaptation of processes. Our taxonomy includes 25 characteristics that address relevant questions regarding self-adaptation of processes. While creating our taxonomy, we built on existing literature and involved academic experts from different universities. These experts did not only validate our taxonomy regarding completeness and understandability, but also rectified and enriched it with their knowledge. Finally, we report the application of this taxonomy to evaluate some existing contributions on self-adaptation of processes.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"155 ","pages":"Article 102374"},"PeriodicalIF":2.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1016/j.datak.2024.102375
Anouck Chan , Anthony Fernandes Pires , Thomas Polacsek , Stéphanie Roussel , François Bouissière , Claude Cuiller , Pierre-Eric Dereux
Traditional aircraft development follows a sequential approach: the aircraft is designed first, followed by the industrial system. This approach limits the industrial system’s performance due to constraints imposed by the pre-defined aircraft design. Collaborative approaches, however, advocate for simultaneous design of different products to create new opportunities. Within a project focused on co-designing aircraft and their industrial systems, we put goal modelling into practice to gain a comprehensive understanding of the objectives driving each system’s design and their interdependencies. The intention was to develop an approach for actively involving domain experts, even those lacking prior knowledge of Goal-Oriented Requirements Engineering (GORE).
This paper provides a detailed account of the iterative process employed to develop and refine our approach. For each iteration, we describe the organisation of modelling sessions with experts, the resulting models, and the collected feedback. We also report on the overall approach’s reception from both industry experts and academic participants. Furthermore, we highlight recommendations and research challenges that emerged from the encountered difficulties during the iterative process, suggesting avenues for further investigation and improvement.
{"title":"Goal modelling in aeronautics: Practical applications for aircraft and manufacturing designs","authors":"Anouck Chan , Anthony Fernandes Pires , Thomas Polacsek , Stéphanie Roussel , François Bouissière , Claude Cuiller , Pierre-Eric Dereux","doi":"10.1016/j.datak.2024.102375","DOIUrl":"10.1016/j.datak.2024.102375","url":null,"abstract":"<div><div>Traditional aircraft development follows a sequential approach: the aircraft is designed first, followed by the industrial system. This approach limits the industrial system’s performance due to constraints imposed by the pre-defined aircraft design. Collaborative approaches, however, advocate for simultaneous design of different products to create new opportunities. Within a project focused on co-designing aircraft and their industrial systems, we put goal modelling into practice to gain a comprehensive understanding of the objectives driving each system’s design and their interdependencies. The intention was to develop an approach for actively involving domain experts, even those lacking prior knowledge of Goal-Oriented Requirements Engineering (GORE).</div><div>This paper provides a detailed account of the iterative process employed to develop and refine our approach. For each iteration, we describe the organisation of modelling sessions with experts, the resulting models, and the collected feedback. We also report on the overall approach’s reception from both industry experts and academic participants. Furthermore, we highlight recommendations and research challenges that emerged from the encountered difficulties during the iterative process, suggesting avenues for further investigation and improvement.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"155 ","pages":"Article 102375"},"PeriodicalIF":2.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661002","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}
Pub Date : 2024-11-04DOI: 10.1016/j.datak.2024.102373
Sergio España , Chris van der Maaten , Jens Gulden , Óscar Pastor
Information and communication technology (ICT) brings about numerous advantages across various domains of our lives. However, alongside these benefits, there is a growing awareness of its potential negative ethical, social, and environmental impacts. Consequently, stakeholders ranging from conceptual modellers to policy makers often find themselves grappling with ethical considerations stemming from ICT engineering and usage. This paper presents a review of 10 ethical reasoning methods suitable for the ICT domain. We have employed a method engineering technique to author metamodels for the methods, which were subsequently subjected to validation by experts proficient in the respective methods. Following a situational method engineering approach, we have also characterised each ethical reasoning method and validated the characterisation with the experts. This has allowed us to develop a tool that helps select the method that is most suitable for a given ethical reasoning situation. Furthermore, we deliberate on the practical application of ethical reasoning methods within conceptual modelling contexts. We are confident that we have laid the groundwork for further research into ethical reasoning of ICT, with a specific emphasis on its role during conceptual modelling.
{"title":"Ethical reasoning methods for ICT: What they are and when to use them","authors":"Sergio España , Chris van der Maaten , Jens Gulden , Óscar Pastor","doi":"10.1016/j.datak.2024.102373","DOIUrl":"10.1016/j.datak.2024.102373","url":null,"abstract":"<div><div>Information and communication technology (ICT) brings about numerous advantages across various domains of our lives. However, alongside these benefits, there is a growing awareness of its potential negative ethical, social, and environmental impacts. Consequently, stakeholders ranging from conceptual modellers to policy makers often find themselves grappling with ethical considerations stemming from ICT engineering and usage. This paper presents a review of 10 ethical reasoning methods suitable for the ICT domain. We have employed a method engineering technique to author metamodels for the methods, which were subsequently subjected to validation by experts proficient in the respective methods. Following a situational method engineering approach, we have also characterised each ethical reasoning method and validated the characterisation with the experts. This has allowed us to develop a tool that helps select the method that is most suitable for a given ethical reasoning situation. Furthermore, we deliberate on the practical application of ethical reasoning methods within conceptual modelling contexts. We are confident that we have laid the groundwork for further research into ethical reasoning of ICT, with a specific emphasis on its role during conceptual modelling.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"155 ","pages":"Article 102373"},"PeriodicalIF":2.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-02DOI: 10.1016/j.datak.2024.102372
Lin Zhu, Xinyi Duan, Luyi Bai
Real-world knowledge graphs are growing in size with the explosion of data and rapid expansion of knowledge. There are some studies on knowledge graph query, but temporal knowledge graph (TKG) query is still a relatively unexplored field. A temporal knowledge graph is a knowledge graph that contains temporal information and contains knowledge that is likely to change over time. It introduces a temporal dimension that can characterize the changes and evolution of entities and relationships at different points in time. However, in the existing temporal knowledge graph query, the entity labels are one-sided, which cannot accurately reflect the semantic relationships of temporal knowledge graphs, resulting in incomplete query results. For the processing of temporal information in temporal knowledge graphs, we propose a temporal frame filtering approach and measure the acceptability of temporal frames by the new definition simtime based on the proposed three temporal frames and nine rules. For measuring the semantic relationship of predicates between entities, we vectorize the semantic similarity between predicates, i.e., edges, using the knowledge embedding model, and propose the new definition simpre to measure the semantic similarity of predicates. Based on these, we propose a new semantic temporal knowledge graph query method , and perform pruning operations to optimize the query efficiency of the algorithm based on connectivity. Extensive experiments show that can return more accurate and complete results that meet the query conditions in the semantic query and can improve the performance of the querying on the temporal knowledge graph.
{"title":"SSQTKG: A Subgraph-based Semantic Query Approach for Temporal Knowledge Graph","authors":"Lin Zhu, Xinyi Duan, Luyi Bai","doi":"10.1016/j.datak.2024.102372","DOIUrl":"10.1016/j.datak.2024.102372","url":null,"abstract":"<div><div>Real-world knowledge graphs are growing in size with the explosion of data and rapid expansion of knowledge. There are some studies on knowledge graph query, but temporal knowledge graph (TKG) query is still a relatively unexplored field. A temporal knowledge graph is a knowledge graph that contains temporal information and contains knowledge that is likely to change over time. It introduces a temporal dimension that can characterize the changes and evolution of entities and relationships at different points in time. However, in the existing temporal knowledge graph query, the entity labels are one-sided, which cannot accurately reflect the semantic relationships of temporal knowledge graphs, resulting in incomplete query results. For the processing of temporal information in temporal knowledge graphs, we propose a temporal frame filtering approach and measure the acceptability of temporal frames by the new definition <em>sim</em><sub><em>time</em></sub> based on the proposed three temporal frames and nine rules. For measuring the semantic relationship of predicates between entities, we vectorize the semantic similarity between predicates, i.e., edges, using the knowledge embedding model, and propose the new definition <em>sim</em><sub><em>pre</em></sub> to measure the semantic similarity of predicates. Based on these, we propose a new semantic temporal knowledge graph query method <span><math><msub><mrow><mi>SSQ</mi></mrow><mrow><mi>TKG</mi></mrow></msub></math></span>, and perform pruning operations to optimize the query efficiency of the algorithm based on connectivity. Extensive experiments show that <span><math><msub><mrow><mi>SSQ</mi></mrow><mrow><mi>TKG</mi></mrow></msub></math></span> can return more accurate and complete results that meet the query conditions in the semantic query and can improve the performance of the querying on the temporal knowledge graph.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"155 ","pages":"Article 102372"},"PeriodicalIF":2.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661003","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}
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