Review
Multidisciplinary Sciences
Olivier Binette, Rebecca C. Steorts
Summary: This article discusses the importance of integrating information from multiple sources, as well as the application of modern probabilistic and Bayesian methods in statistics, computer science, machine learning, database management, economics, and political science.
Article
Computer Science, Artificial Intelligence
Haoyue Shi, Le Wang, Nanning Zheng, Gang Hua, Wei Tang
Summary: This paper comprehensively studies the impact of different loss functions on pose guided person image generation, finding that a combination of adversarial loss, perceptual loss, and PSSIM loss yields optimal results.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Yinan Liu, Wei Shen, Zonghai Yao, Jianyong Wang, Zhenglu Yang, Xiaojie Yuan
Summary: Knowledge bases play a critical role in various applications, but they are often incomplete. Enriching knowledge bases with new entities and location attributes is becoming increasingly important. This study introduces NELPTW, an unsupervised framework for predicting named entity location by leveraging knowledge from Twitter and Web, which significantly outperforms baselines in accuracy.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Automation & Control Systems
Debjit Sarkar, Sourodeep Roy, Samir Malakar, Ram Sarkar
Summary: Graph neural networks (GNN) maintain the essence of irregularly structured information in a graph through message passing and feature aggregation. A weighting scheme called VecGNN is proposed to incorporate inter-node feature-level correlational information, considering the relative position of nodes in the feature space. VecGNN outperforms baseline models GCN, GAT, and JKNets by 2%-4% on citation datasets.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Samudra Herath, Matthew Roughan, Gary Glonek
Summary: Entity resolution is a key task in data integration, with accurate and efficient resolution having a significant impact across various fields. The lack of real training data and privacy concerns make simulation tools important for testing algorithms in entity resolution research.
Proceedings Paper
Computer Science, Interdisciplinary Applications
Antonio Correia, Diogo Guimaraes, Dennis Paulino, Shoaib Jameel, Daniel Schneider, Benjamim Fonseca, Hugo Paredes
Summary: This paper presents an approach to handle name ambiguity problems through crowdsourcing as a complementary means to traditional unsupervised approaches, demonstrating its potential for improving author name disambiguation and highlighting the importance of adopting hybrid crowd-algorithm collaboration strategies.
PROCEEDINGS OF THE 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN (CSCWD)
(2021)
Article
Computer Science, Information Systems
Syed Afeef Ahmed Shah, Muhammad Ali Masood, Amanullah Yasin
Summary: Extracting information from e-commerce platforms is a challenging task due to the increasing number of marketplaces. Existing data mining techniques may not provide sufficient accuracy. In this study, we propose a Bi-directional LSTM with CNN model for detecting e-commerce entities, achieving high accuracy on dark web and Conll-2003 datasets.
Article
Computer Science, Information Systems
Shimaa Ibrahim, Said Fathalla, Jens Lehmann, Hajira Jabeen
Summary: This paper proposes a Multilingual Ontology Matching (MoMatch) approach for matching ontologies in different natural languages. It uses machine translation and various string similarity techniques to identify correspondences across different ontologies. The paper also presents a Quality Assessment Suite for Ontologies (QASO) that evaluates the quality of the matching process and the ontology. The results show that MoMatch outperforms five state-of-the-art matching approaches in terms of precision, recall, and F-measure.
Article
Computer Science, Artificial Intelligence
Mona Alghamdi, Plamen Angelov, Lopez Pellicer Alvaro
Summary: This paper presents an approach for person identification based on knuckle creases and fingernails. It introduces a framework that includes localization, recognition, segmentation, and similarity matching of hand components. The results show that knuckle patterns and fingernails play a significant role in person identification.
Article
Computer Science, Artificial Intelligence
Sengodan Mani, Samukutty Annadurai
Summary: A new modified model of similarity spreading for ontology mapping is proposed in this paper, which aims to address the heterogeneity issue between ontologies for interoperability. By utilizing node clustering based on edge affinity and coefficient similarity propagation, the model achieves graph matching. The evaluation shows that the proposed model outperforms similar systems.
INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Xin Xing, Ning Wang
Summary: Entity matching (EM) is the process of identifying tuples from different data sources that refer to the same real-world entity. Existing research focuses on attribute heterogeneity and selecting similarity measures for different types of attributes. However, they overlook matching information from various aspects and the impact of dirty data. In this paper, we propose an entity matching method that incorporates attribute-aware and multi-perspective similarity measurement. Experimental results demonstrate its superiority over state-of-the-art methods on multiple real-world datasets.
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING
(2023)
Proceedings Paper
Computer Science, Information Systems
Mehdi Akbarian Rastaghi, Ehsan Kamalloo, Davood Rafiei
Summary: The study found that data imbalance in the training data is a key issue affecting model robustness, and data augmentation alone is not sufficient to ensure model robustness. Simple modifications can improve the robustness of PLM-based EM models.
PROCEEDINGS OF THE 31ST ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2022
(2022)
Review
Engineering, Electrical & Electronic
Riddhi Pahariya, Lalit Purohit
Summary: Conventional web services have a minor role in semantics, while the maximum matching process in semantic web service selection plays a crucial role in achieving accurate results. Most existing web service selection methods rely on keyword-based searching, disregarding semantic understanding and resulting in irrelevant outcomes. This paper reviews the latest research on semantic web service selection, discussing techniques applicable to both web service composition and selection, and presents the application of two network flow-based approaches to achieve improved web service selection.
IETE JOURNAL OF RESEARCH
(2022)
Article
Computer Science, Hardware & Architecture
Yun Zhang, Yuling Liu, Ge Cheng, Jie Wang
Summary: This paper presents a method that uses a neural network model to learn the semantic and structural features of functions on control-flow graphs, to detect the similarity between functions in different compiled forms. Experiments show that this method outperforms other models in detecting binary functions with large control-flow graphs.
Proceedings Paper
Computer Science, Artificial Intelligence
Shangmei Li, Yangsen Zhang, Xiang Chen, Han Chen, Gaijuan Huang
Summary: Peer review is a common method used in the evaluation of scientific research projects or academic papers. The ambiguity caused by experts with the same name is a common problem in the selection of peer experts. This paper proposes a multi-feature expert disambiguation method that incorporates personal experience, by constructing an expert disambiguation feature representation model, providing similarity measurement methods and a similarity threshold. This method can effectively solve the problem.
2022 INTERNATIONAL CONFERENCE ON ASIAN LANGUAGE PROCESSING (IALP 2022)
(2022)
Article
Computer Science, Information Systems
Alessio Cecconi, Luca Barbaro, Claudio Di Ciccio, Arik Senderovich
Summary: This paper introduces a framework for designing probabilistic measures for declarative process specifications, which can assess the degree of compliance between process data and specifications. Through experiments, the applicability of the approach for various process mining tasks is demonstrated.
INFORMATION SYSTEMS
(2024)
Article
Computer Science, Information Systems
Mahei Manhai Li, Philipp Reinhard, Christoph Peters, Sarah Oeste-Reiss, Jan Marco Leimeister
Summary: This article introduces a novel human-in-the-loop (HIL) design for ITSM support ticket recommendations by incorporating a value co-creation perspective. The design incentivizes ITSM agents to provide labels during their everyday ticket-handling procedures, and the evaluation shows that recommendations after label improvement have increased user ratings.
INFORMATION SYSTEMS
(2024)
Article
Computer Science, Information Systems
Anton Yeshchenko, Jan Mendling
Summary: This paper presents the development of event sequence data analysis techniques in different fields and proposes an integrated framework to facilitate collaboration and research synergy across various domains.
INFORMATION SYSTEMS
(2024)
Article
Computer Science, Information Systems
Iris Reinhartz-Berger, Alan Hartman, Doron Kliger
Summary: Many IT departments provide solutions that partially meet the needs of business units. This research aims to develop a data-driven analysis method to support the selection of solutions with higher prospects of adoption and identify design gaps and barriers.
INFORMATION SYSTEMS
(2024)
Article
Computer Science, Information Systems
Orlenys Lopez-Pintado, Marlon Dumas, Jonas Berx
Summary: Business process simulation is a versatile technique that predicts the impact of changes on process performance. However, previous approaches have limitations due to their treatment of resources as undifferentiated entities. This article addresses this issue by proposing a new simulation approach that treats each resource as an individual entity with its own performance and availability. The article also presents methods for discovering simulation models with differentiated resources and optimizing resource availability calendars. Empirical evaluation demonstrates that differentiated resource models better replicate cycle time distributions and work rhythm, and iterative optimization of resource allocations and calendars leads to improved cost-time tradeoffs.
INFORMATION SYSTEMS
(2024)