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Insights into commonalities of a sample: A visualization framework to explore unusual subset-dataset relationships Data Knowl. Eng. (IF 2.5) Pub Date : 2024-03-12 Nikolas Stege, Michael H. Breitner
Domain experts are driven by business needs, while data analysts develop and use various algorithms, methods, and tools, but often without domain knowledge. A major challenge for companies and organizations is to integrate data analytics in business processes and workflows. We deduce an interactive process and visualization framework to enable value creating collaboration in inter- and cross-disciplinary
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Time-Aware Structure Matching for Temporal Knowledge Graph Alignment Data Knowl. Eng. (IF 2.5) Pub Date : 2024-03-11 Wei Jia, Ruizhe Ma, Li Yan, Weinan Niu, Zongmin Ma
Entity alignment, aiming at identifying equivalent entity pairs across multiple knowledge graphs (KGs), serves as a vital step for knowledge fusion. As the majority of KGs undergo continuous evolution, existing solutions utilize graph neural networks (GNNs) to tackle entity alignment within temporal knowledge graphs (TKGs). However, this prevailing method often overlooks the consequential impact of
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A knowledge-sharing platform for space resources Data Knowl. Eng. (IF 2.5) Pub Date : 2024-02-29 Marcos Da Silveira, Louis Deladiennee, Emmanuel Scolan, Cedric Pruski
The ever-increasing interest of academia, industry, and government institutions in space resource information highlights the difficulty of finding, accessing, integrating, and reusing this information. Although information is regularly published on the internet, it is disseminated on many different websites and in different formats, including scientific publications, patents, news, and reports. We
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Knowledge graph-based image classification Data Knowl. Eng. (IF 2.5) Pub Date : 2024-02-28 Franck Anaël Mbiaya, Christel Vrain, Frédéric Ros, Thi-Bich-Hanh Dao, Yves Lucas
This paper introduces a deep learning method for image classification that leverages knowledge formalized as a graph created from information represented by pairs attribute/value. The proposed method investigates a loss function that adaptively combines the classical cross-entropy commonly used in deep learning with a novel penalty function. The novel loss function is derived from the representation
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Improving the identification of relevant variants in genome information systems: A methodological approach with a case study on early onset Alzheimer's disease Data Knowl. Eng. (IF 2.5) Pub Date : 2024-02-09 Mireia Costa, Ana León, Óscar Pastor
Alzheimer's disease is the most common type of dementia in the elderly. Nevertheless, there is an early onset form that is difficult to diagnose precisely. As the genetic component is the most critical factor in developing this disease, identifying relevant genetic variants is key to obtaining a more reliable and straightforward diagnosis. The information about these variants is stored in an extensive
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Fuzzy-Ontology based knowledge driven disease risk level prediction with optimization assisted ensemble classifier Data Knowl. Eng. (IF 2.5) Pub Date : 2024-02-04 Huma Parveen, Syed Wajahat Abbas Rizvi, Raja Sarath Kumar Boddu
Modern medicinal analysis is a complex procedure, requiring precise patient data, scientific knowledge obtained over numerous years and a theoretical understanding of related medical literature. To improve the accuracy and to reduce the time for diagnosis, clinical decision support systems (DSS) were introduced, which incorporate data mining schemes for enhancing the disease diagnosing accuracy. This
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Fusion learning of preference and bias from ratings and reviews for item recommendation Data Knowl. Eng. (IF 2.5) Pub Date : 2024-02-03 Junrui Liu, Tong Li, Zhen Yang, Di Wu, Huan Liu
Recommendation methods improve rating prediction performance by learning selection bias phenomenon-users tend to rate items they like. These methods model selection bias by calculating the propensities of ratings, but inaccurate propensity could introduce more noise, fail to model selection bias, and reduce prediction performance. We argue that learning interaction features can effectively model selection
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Multivariate hierarchical DBSCAN model for enhanced maritime data analytics Data Knowl. Eng. (IF 2.5) Pub Date : 2024-02-02 Nitin Newaliya, Yudhvir Singh
Clustering is an important data analytics technique and has numerous use cases. It leads to the determination of insights and knowledge which would not be readily discernible on routine examination of the data. Enhancement of clustering techniques is an active field of research, with various optimisation models being proposed. Such enhancements are also undertaken to address particular issues being
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AI system architecture design methodology based on IMO (Input-AI Model-Output) structure for successful AI adoption in organizations Data Knowl. Eng. (IF 2.5) Pub Date : 2024-01-28 Seungkyu Park, Joong yoon Lee, Jooyeoun Lee
With the advancement of AI technology, the successful AI adoption in organizations has become a top priority in modern society. However, many organizations still struggle to articulate the necessary AI, and AI experts have difficulties understanding the problems faced by these organizations. This knowledge gap makes it difficult for organizations to identify the technical requirements, such as necessary
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A new sentence embedding framework for the education and professional training domain with application to hierarchical multi-label text classification Data Knowl. Eng. (IF 2.5) Pub Date : 2024-01-19 Guillaume Lefebvre, Haytham Elghazel, Theodore Guillet, Alexandre Aussem, Matthieu Sonnati
In recent years, Natural Language Processing (NLP) has made significant advances through advanced general language embeddings, allowing breakthroughs in NLP tasks such as semantic similarity and text classification. However, complexity increases with hierarchical multi-label classification (HMC), where a single entity can belong to several hierarchically organized classes. In such complex situations
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Issues in inter-organizational data sharing: Findings from practice and research challenges Data Knowl. Eng. (IF 2.5) Pub Date : 2024-01-10 Ilka Jussen, Frederik Möller, Julia Schweihoff, Anna Gieß, Giulia Giussani, Boris Otto
Sharing data is highly potent in assisting companies in internal optimization and designing new products and services. While the benefits seem obvious, sharing data is accompanied by a spectrum of concerns ranging from fears of sharing something of value, unawareness of what will happen to the data, or simply a lack of understanding of the short- and mid-term benefits. The article analyzes data sharing
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Data analytics and knowledge discovery on big data: Algorithms, architectures, and applications Data Knowl. Eng. (IF 2.5) Pub Date : 2024-01-05 Robert Wrembel, Johann Gamper
Abstract not available
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A deep learning model for predicting the number of stores and average sales in commercial district Data Knowl. Eng. (IF 2.5) Pub Date : 2024-01-04 Suan Lee, Sangkeun Ko, Arousha Haghighian Roudsari, Wookey Lee
This paper presents a plan for preparing for changes in the business environment by analyzing and predicting business district data in Seoul. The COVID-19 pandemic and economic crisis caused by inflation have led to an increase in store closures and a decrease in sales, which has had a significant impact on commercial districts. The number of stores and sales are critical factors that directly affect
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A bitwise approach on influence overload problem Data Knowl. Eng. (IF 2.5) Pub Date : 2023-12-30 Charles Cheolgi Lee, Jafar Afshar, Arousha Haghighian Roudsari, Woong-Kee Loh, Wookey Lee
Increasingly developing online social networks has enabled users to send or receive information very fast. However, due to the availability of an excessive amount of data in today’s society, managing the information has become very cumbersome, which may lead to the problem of information overload. This highly eminent problem, where the existence of too much relevant information available becomes a
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A transformer-based neural network framework for full names prediction with abbreviations and contexts Data Knowl. Eng. (IF 2.5) Pub Date : 2023-12-30 Ziming Ye, Shuangyin Li
With the rapid spread of information, abbreviations are used more and more common because they are convenient. However, the duplication of abbreviations can lead to confusion in many cases, such as information management and information retrieval. The resultant confusion annoys users. Thus, inferring a full name from an abbreviation has practical and significant advantages. The bulk of studies in the
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Mining Keys for Graphs Data Knowl. Eng. (IF 2.5) Pub Date : 2023-12-27 Morteza Alipourlangouri, Fei Chiang
Keys for graphs are a class of data quality rules that use topological and value constraints to uniquely identify entities in a data graph. They have been studied to support object identification, knowledge fusion, data deduplication, and social network reconciliation. Manual specification and discovery of graph keys is tedious and infeasible over large-scale graphs. To make useful in practice, we
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An approach to on-demand extension of multidimensional cubes in multi-model settings: Application to IoT-based agro-ecology Data Knowl. Eng. (IF 2.5) Pub Date : 2023-12-23 Sandro Bimonte, Fagnine Alassane Coulibaly, Stefano Rizzi
Managing unstructured and heterogeneous data, integrating them, and enabling their analysis are among the key challenges in data ecosystems, together with the need to accommodate a progressive growth in these systems by seamlessly supporting extensibility. This is particularly relevant for OLAP analyses on multidimensional cubes stored in data warehouses (DWs), which naturally span large portions of
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Increase development productivity by domain-specific conceptual modeling Data Knowl. Eng. (IF 2.5) Pub Date : 2023-12-15 Martin Paczona, Heinrich C. Mayr, Guenter Prochart
This paper addresses the question of whether and how the development and use of a domain-specific modeling method (DSMM) can increase productivity in the development of technical systems in an industrial setting. This is because an essential prerequisite for DSMMs to become established in operational practice is that productivity increases can be achieved with them and qualitative benefits such as
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Improving speech emotion recognition by fusing self-supervised learning and spectral features via mixture of experts Data Knowl. Eng. (IF 2.5) Pub Date : 2023-12-13 Jonghwan Hyeon, Yung-Hwan Oh, Young-Jun Lee, Ho-Jin Choi
Speech Emotion Recognition (SER) is an important area of research in speech processing that aims to identify and classify emotional states conveyed through speech signals. Recent studies have shown considerable performance in SER by exploiting deep contextualized speech representations from self-supervised learning (SSL) models. However, SSL models pre-trained on clean speech data may not perform well
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Recognition algorithm for cross-texting in text chat conversations Data Knowl. Eng. (IF 2.5) Pub Date : 2023-12-10 Da-Young Lee, Hwan-Gue Cho
As the development of the Internet and IT technology, short-text based communication is so popular compared with voice based one. Chat-based communication enables rapid, short and massive exchange of message with many people, creates new social problems. ‘Cross-texting’ is one of them. It refers to accidentally sending a text to an unintended person during the concurrent conversations with separated
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Towards deep understanding of graph convolutional networks for relation extraction Data Knowl. Eng. (IF 2.5) Pub Date : 2023-12-07 Tao Wu, Xiaolin You, Xingping Xian, Xiao Pu, Shaojie Qiao, Chao Wang
Relation extraction aims at identifying semantic relations between pairs of named entities from unstructured texts and is considered an essential prerequisite for many downstream tasks in natural language processing (NLP). Owing to the ability in expressing complex relationships and interdependency, graph neural networks (GNNs) have been gradually used to solve the relation extraction problem and have
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Blockchain-based ontology driven reference framework for security risk management Data Knowl. Eng. (IF 2.5) Pub Date : 2023-12-04 Mubashar Iqbal, Aleksandr Kormiltsyn, Vimal Dwivedi, Raimundas Matulevičius
Security risk management (SRM) is crucial for protecting valuable assets from malicious harm. While blockchain technology has been proposed to mitigate security threats in traditional applications, it is not a perfect solution, and its security threats must be managed. This paper addresses the research problem of having no unified and formal knowledge models to support the SRM of traditional applications
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Generating psychological analysis tables for children's drawings using deep learning Data Knowl. Eng. (IF 2.5) Pub Date : 2023-12-06 Moonyoung Lee, Youngho Kim, Young-Kuk Kim
The usefulness of drawing-based psychological testing has been demonstrated in a variety of studies. By using the familiar medium of drawing, drawing-based psychological testing can be applied to a wide range of age groups and is particularly effective with children who have difficulty expressing themselves verbally. Drawing tests are usually implemented face-to-face, requiring specialized counseling
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Editorial for VSI:NLDB-saarbruecken-2021 Data Knowl. Eng. (IF 2.5) Pub Date : 2023-11-30 Helmut Horacek, Epaminondas Kapetanios, Elisabeth Metais, Farid Meziane
Abstract not available
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Integrated detection and localization of concept drifts in process mining with batch and stream trace clustering support Data Knowl. Eng. (IF 2.5) Pub Date : 2023-12-02 Rafael Gaspar de Sousa, Antonio Carlos Meira Neto, Marcelo Fantinato, Sarajane Marques Peres, Hajo Alexander Reijers
Process mining can help organizations by extracting knowledge from event logs. However, process mining techniques often assume business processes are stationary, while actual business processes are constantly subject to change because of the complexity of organizations and their external environment. Thus, addressing process changes over time – known as concept drifts – allows for a better understanding
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DeepScraper: A complete and efficient tweet scraping method using authenticated multiprocessing Data Knowl. Eng. (IF 2.5) Pub Date : 2023-11-30 Jaebeom You, Kisung Lee, Hyuk-Yoon Kwon
In this paper, we propose a scraping method for collecting tweets, which we call DeepScraper. DeepScraper provides the complete scraping for the entire tweets written by a certain group of users or them containing search keywords with a fast speed. To improve the crawling speed of DeepScraper, we devise a multiprocessing architecture while providing authentication to the multiple processes based on
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S_IDS: An efficient skyline query algorithm over incomplete data streams Data Knowl. Eng. (IF 2.5) Pub Date : 2023-11-30 Mei Bai, Yuxue Han, Peng Yin, Xite Wang, Guanyu Li, Bo Ning, Qian Ma
The efficient processing of mass stream data has attracted wide attention in the database field. The skyline query on the sensor data stream can monitor multiple targets in real time, to avoid abnormal events such as fire and explosion, which is very useful in the practical application of sensor data monitoring. However, real-world stream data may often contain incomplete data attributes due to faulty
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Explainable influenza forecasting scheme using DCC-based feature selection Data Knowl. Eng. (IF 2.5) Pub Date : 2023-11-26 Sungwoo Park, Jaeuk Moon, Seungwon Jung, Seungmin Rho, Eenjun Hwang
As influenza is easily converted to another type of virus and spreads very quickly from person to person, it is more likely to develop into a pandemic. Even though vaccines are the most effective way to prevent influenza, it takes a lot of time to produce them. Due to this, there has been an imbalance in the supply and demand of influenza vaccines every year. For a smooth vaccine supply, it is necessary
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Global and item-by-item reasoning fusion-based multi-hop KGQA Data Knowl. Eng. (IF 2.5) Pub Date : 2023-11-20 Tongzhao Xu, Turdi Tohti, Askar Hamdulla
Existing embedded multi-hop Question Answering over Knowledge Graph (KGQA) methods attempted to handle Knowledge Graph (KG) sparsity using Knowledge Graph Embedding (KGE) to improve KGQA. However, they almost ignore the intermediate path reasoning process of answer prediction, do not consider the information interaction between the question and the KG, and rarely consider the problem that the triple
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A-MKMC: An effective adaptive-based multilevel K-means clustering with optimal centroid selection using hybrid heuristic approach for handling the incomplete data Data Knowl. Eng. (IF 2.5) Pub Date : 2023-11-22 Hima Vijayan, Subramaniam M, Sathiyasekar K
In general, clustering is defined as partitioning similar and dissimilar objects into several groups. It has been widely used in applications like pattern recognition, image processing, and data analysis. When the dataset contains some missing data or value, it is termed incomplete data. In such implications, the incomplete dataset issue is untreatable while validating the data. Due to these flaws
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The power and potentials of Flexible Query Answering Systems: A critical and comprehensive analysis Data Knowl. Eng. (IF 2.5) Pub Date : 2023-11-19 Troels Andreasen, Gloria Bordogna, Guy De Tré, Janusz Kacprzyk, Henrik Legind Larsen, Sławomir Zadrożny
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CALEB: A Conditional Adversarial Learning Framework to enhance bot detection Data Knowl. Eng. (IF 2.5) Pub Date : 2023-11-14 Ilias Dimitriadis, George Dialektakis, Athena Vakali
The high growth of Online Social Networks (OSNs) over the last few years has allowed automated accounts, known as social bots, to gain ground. As highlighted by other researchers, many of these bots have malicious purposes and tend to mimic human behavior, posing high-level security threats on OSN platforms. Moreover, recent studies have shown that social bots evolve over time by reforming and reinventing
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What is the business value of your data? A multi-perspective empirical study on monetary valuation factors and methods for data governance Data Knowl. Eng. (IF 2.5) Pub Date : 2023-11-11 Frank Bodendorf, Jörg Franke
Digitalization has greatly increased the importance of data in recent years, making data an indispensable resource for value creation in our time. There is currently still a lack of theories as well as practicable methods and techniques for the monetary valuation of data, and data is therefore not yet sufficiently managed in terms of business management principles. In this context, this research is
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A framework for approximate product search using faceted navigation and user preference ranking Data Knowl. Eng. (IF 2.5) Pub Date : 2023-11-11 Damir Vandic, Lennart J. Nederstigt, Flavius Frasincar, Uzay Kaymak, Enzo Ido
One of the problems that e-commerce users face is that the desired products are sometimes not available and Web shops fail to provide similar products due to their exclusive reliance on Boolean faceted search. User preferences are also often not taken into account. In order to address these problems, we present a novel framework specifically geared towards approximate faceted search within the product
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Hierarchical framework for interpretable and specialized deep reinforcement learning-based predictive maintenance Data Knowl. Eng. (IF 2.5) Pub Date : 2023-10-31 Ammar N. Abbas, Georgios C. Chasparis, John D. Kelleher
Deep reinforcement learning holds significant potential for application in industrial decision-making, offering a promising alternative to traditional physical models. However, its black-box learning approach presents challenges for real-world and safety-critical systems, as it lacks interpretability and explanations for the derived actions. Moreover, a key research question in deep reinforcement learning
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Discovering and evaluating organizational knowledge from textual data: Application to crisis management Data Knowl. Eng. (IF 2.5) Pub Date : 2023-10-27 Dhouha Grissa, Eric Andonoff, Chihab Hanachi
Crisis management effectiveness relies mainly on the quality of the distributed human organization deployed for saving lives, limiting damage and reducing risks. Organizations set up in this context are not always predefined and static; they could evolve and new forms could emerge since actors, such as volunteers or NGO, could join dynamically to collaborate. To improve crisis resolution effectiveness
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User-generated short-text classification using cograph editing-based network clustering with an application in invoice categorization Data Knowl. Eng. (IF 2.5) Pub Date : 2023-10-21 Dewan F. Wahid, Elkafi Hassini
Rapid adaptation of online business platforms in every sector creates an enormous amount of user-generated textual data related to providing product or service descriptions, reviewing, marketing, invoicing and bookkeeping. These data are often short in size, noisy (e.g., misspellings, abbreviations), and do not have accurate classifying labels (line-item categories). Classifying these user-generated
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An efficient and scalable SPARQL query processing framework for big data using MapReduce and hybrid optimum load balancing Data Knowl. Eng. (IF 2.5) Pub Date : 2023-10-21 V. Naveen Kumar, Ashok Kumar P.S.
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Challenges of a Data Ecosystem for scientific data Data Knowl. Eng. (IF 2.5) Pub Date : 2023-10-19 Edoardo Ramalli, Barbara Pernici
Data Ecosystems (DE) are used across various fields and applications. They facilitate collaboration between organizations, such as companies or research institutions, enabling them to share data and services. A DE can boost research outcomes by managing and extracting value from the increasing volume of generated and shared data in the last decades. However, the adoption of DE solutions for scientific
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SkiQL: A unified schema query language Data Knowl. Eng. (IF 2.5) Pub Date : 2023-10-11 Carlos J. Fernández Candel, Jesús J. García-Molina, Diego Sevilla Ruiz
Most NoSQL systems are schema-on-read: data can be stored without first having to declare a schema that imposes a structure. This schemaless feature offers flexibility to evolve data-intensive applications when data change frequently. However, freeing from declaring schemas does not mean their absence, but rather that they are implicit in data and code. Therefore, diagramming tools similar to those
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Towards comparable ratings: Exploring bias in German physician reviews Data Knowl. Eng. (IF 2.5) Pub Date : 2023-10-11 Joschka Kersting, Falk Maoro, Michaela Geierhos
In this study, we evaluate the impact of gender-biased data from German-language physician reviews on the fairness of fine-tuned language models. For two different downstream tasks, we use data reported to be gender biased and aggregate it with annotations. First, we propose a new approach to aspect-based sentiment analysis that allows identifying, extracting, and classifying implicit and explicit
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Effective healthcare service recommendation with network representation learning: A recursive neural network approach Data Knowl. Eng. (IF 2.5) Pub Date : 2023-09-29 Mouhamed Gaith Ayadi, Haithem Mezni, Rana Alnashwan, Hela Elmannai
Recently, recommender systems have been combined with healthcare systems to recommend needed healthcare items for both patients and medical staff. By monitoring the patients’ states, healthcare services and their consumed smart medical objects can be recommended to a medical team according to the patient’s critical situation and requirements. However, a common drawback of the few existing solutions
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A novel ensemble framework driven by diversity and cooperativity for non-stationary data stream classification Data Knowl. Eng. (IF 2.5) Pub Date : 2023-09-28 Kuangyan Zhang, Tuyi Zhang, Sanmin Liu
Data stream classification is of great significance to numerous real-world scenarios. Nevertheless, the prevalent data stream classification techniques are influenced by concept drift and demonstrate unreliability in non-stationary environments. Ensemble models are typically successful when they increase diversity among their members. Several ensembles that enhance diversity have been proposed in literatures
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A novel hybrid approach for text encoding: Cognitive Attention To Syntax model to detect online misinformation Data Knowl. Eng. (IF 2.5) Pub Date : 2023-09-28 Géraud Faye, Wassila Ouerdane, Guillaume Gadek, Souhir Gahbiche, Sylvain Gatepaille
Most approaches for text encoding rely on the attention mechanism, at the core of the transformers architecture and large language models. The understanding of this mechanism is still limited and present inconvenients such as lack of interpretability, large requirements of data and low generalization. Based on current understanding of the attention mechanism, we propose CATS (Cognitive Attention To
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Robotic process automation using process mining — A systematic literature review Data Knowl. Eng. (IF 2.5) Pub Date : 2023-09-23 Najah Mary El-Gharib, Daniel Amyot
Process mining (PM) aims to construct, from event logs, process maps that can help discover, automate, improve, and monitor organizational processes. Robotic process automation (RPA) uses software robots to perform some tasks usually executed by humans. It is usually difficult to determine what processes and steps to automate, especially with RPA. PM is seen as one way to address such difficulty. This
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Preface - special issue on conceptual modeling – ER 2022 Data Knowl. Eng. (IF 2.5) Pub Date : 2023-09-20 Jolita Ralyté, Manfred Jeusfeld, Mukesh Mohania
Abstract not available
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LBP feature and hash function based dual watermarking algorithm for database Data Knowl. Eng. (IF 2.5) Pub Date : 2023-09-15 De Li, Chi Ma, Haoyang Gao, Xun Jin
In this paper, we propose a local binary pattern (LBP) feature and hash function based dual watermarking algorithm for database. Attribute feature columns are selected to generate zero watermarks using the Pearson correlation method. The zero watermarks are generated by the LBP. The attribute values of the selected feature columns are divided into two parts for embedding and extracting the watermark
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Mixed emotion extraction analysis and visualisation of social media text Data Knowl. Eng. (IF 2.5) Pub Date : 2023-09-11 Yuming Li, Johnny Chan, Gabrielle Peko, David Sundaram
With the widespread use of social media and accelerated development of artificial intelligence, sentiment analysis is regarded as an important way to help enterprises understand user needs and conduct brand monitoring. It can also assist businesses in making data-driven decisions about product development, marketing strategies, and customer service. However, as social media information continues to
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Legal powers, subjections, disabilities, and immunities: Ontological analysis and modeling patterns Data Knowl. Eng. (IF 2.5) Pub Date : 2023-09-07 Cristine Griffo, João Paulo A. Almeida, João A.O. Lima, Tiago Prince Sales, Giancarlo Guizzardi
The development of dependable information systems in legal contexts requires a precise understanding of the subtleties of the underlying legal phenomena. According to a modern understanding in the philosophy of law, much of these phenomena are relational in nature. In this paper, we employ a theoretically well-grounded legal core ontology (UFO-L) to conduct an ontological analysis focused on fundamental
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Evaluating the intelligence capability of smart homes: A conceptual modeling approach Data Knowl. Eng. (IF 2.5) Pub Date : 2023-08-28 Di Wu, Weite Feng, Tong Li, Zhen Yang
With the rapid development of Internet of Things technology, smart homes have gradually become an integral part of people’s lives, and the market share of smart homes has experienced a significant surge in recent years. As a result, there is a growing need for both producers and end-users to evaluate the intelligence of smart homes. While existing studies focus on simulating smart home environments
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Self supervised learning and the poverty of the stimulus Data Knowl. Eng. (IF 2.5) Pub Date : 2023-08-09 Csaba Veres, Jennifer Sampson
Diathesis alternations are the possible expressions of the arguments of verbs in different, systematically related subcategorization frames. Semantically similar verbs such as spill and spray can behave differently with respect to the alternations they can participate in. For example one can “spill/spray water on the plant”, but while one can “spray the plant with water”, it is odd to say “spill the
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A FAIR catalog of ontology-driven conceptual models Data Knowl. Eng. (IF 2.5) Pub Date : 2023-08-09 Tiago Prince Sales, Pedro Paulo F. Barcelos, Claudenir M. Fonseca, Isadora Valle Souza, Elena Romanenko, César Henrique Bernabé, Luiz Olavo Bonino da Silva Santos, Mattia Fumagalli, Joshua Kritz, João Paulo A. Almeida, Giancarlo Guizzardi
Multi-domain model catalogs serve as empirical sources of knowledge and insights about specific domains, about the use of a modeling language’s constructs, as well as about the patterns and anti-patterns recurrent in the models of that language crosscutting different domains. They may support domain and language learning, model reuse, knowledge discovery for humans, and reliable automated processing
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An extended taxonomy of advanced information visualization and interaction in conceptual modeling Data Knowl. Eng. (IF 2.5) Pub Date : 2023-08-06 Dominik Bork, Giuliano De Carlo
Conceptual modeling is integral to computer science research and is widely adopted in industrial practices, e.g., business process and enterprise architecture management. Providing adequate and usable modeling tools is necessary to adopt modeling languages efficiently. Meta-modeling platforms provide a rich and mature set of functionalities for realizing state-of-the-art modeling tools. These tools
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Decisive skyline queries for truly balancing multiple criteria Data Knowl. Eng. (IF 2.5) Pub Date : 2023-08-03 Akrivi Vlachou, Christos Doulkeridis, João B. Rocha-Junior, Kjetil Nørvåg
Skyline queries have emerged as an increasingly popular tool for identifying a set of interesting objects that balance different user-specified criteria. Although in several applications the user aims to detect data objects that have values as good as possible in all specified criteria, skyline queries fail to identify only those objects. Instead, objects whose values are good in a subset of the given
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An empiric validation of linguistic features in machine learning models for fake news detection Data Knowl. Eng. (IF 2.5) Pub Date : 2023-08-02 Eduardo Puraivan, René Venegas, Fabián Riquelme
The diffusion of fake news is a growing problem with a high and negative social impact. There are several approaches to address the detection of fake news. This work focuses on a hybrid approach based on functional linguistic features and machine learning. There are several recent works with this approach. However, there are no clear guidelines on which linguistic features are most appropriate nor
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Modified Hierarchical-Attention Network model for legal judgment predictions Data Knowl. Eng. (IF 2.5) Pub Date : 2023-07-28 G. Sukanya, J. Priyadarshini
The impact of Artificial Intelligence in Legal Research has reached a high level in simulating human thought processes. Case Pendency is a long-lasting problem in many countries. The judicial system has to be more competent and reliable to provide justice on time for any developing country. Litigants and attorneys devote more time and effort to trial case preparation in the courtroom. The task of decision
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Modelling temporal goals in runtime goal models Data Knowl. Eng. (IF 2.5) Pub Date : 2023-07-28 Rebecca Morgan, Simon Pulawski, Matt Selway, Aditya Ghose, Georg Grossmann, Wolfgang Mayer, Markus Stumptner, Ross Kyprianou
Achieving real-time agility and adaptation with respect to changing requirements in existing IT infrastructure can pose a complex challenge. We describe a goal-oriented approach to manage this complexity. We argue that a goal-oriented perspective can form an effective basis for devising and deploying responses to changed requirements at runtime. We offer an extended vocabulary of goal types by presenting
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BT-CKBQA: An efficient approach for Chinese knowledge base question answering Data Knowl. Eng. (IF 2.5) Pub Date : 2023-07-27 Erhe Yang, Fei Hao, Jiaxing Shang, Xiaoliang Chen, Doo-Soon Park
Knowledge Base Question Answering (KBQA), as an increasingly essential application, can provide accurate responses to user queries. ensuring that users obtain relevant information and make decisions promptly. The deep learning-based approaches have achieved satisfactory QA results by leveraging the neural network models. However, these approaches require numerous parameters, which increases the workload
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Corrigendum to “Mining Closed High Utility Itemsets based on Propositional Satisfiability” [Data Knowl. Eng. 136C (2021) 101927] Data Knowl. Eng. (IF 2.5) Pub Date : 2023-07-21 Amel Hidouri, Said Jabbour, Badran Raddaoui, Boutheina Ben Yaghlane
Abstract not available
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RINX: A system for information and knowledge extraction from resumes Data Knowl. Eng. (IF 2.5) Pub Date : 2023-07-14 Girish K. Palshikar, Sachin Pawar, Anindita Sinha Banerjee, Rajiv Srivastava, Nitin Ramrakhiyani, Sangameshwar Patil, Devavrat Thosar, Jyoti Bhat, Ankita Jain, Swapnil Hingmire, Saheb Chaurasia, Payodhi Mandloi, Durgesh Chalavadi
A resume is a detailed source of information about the candidate which summarizes the personal details, education, career history, project experience, certifications, trainings, awards, and any other achievements. For large organizations or job portals which receive thousands of resumes for recruitment or profile creation, it is not possible to manually go through each resume and identify the important