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2022 International Conference on Industry Sciences and Computer Science Innovation, iSCSi 2022

作者： Trabelsi, Salma Zahaf, Sahbi Zekri, Manel Aouicha, Mohamed Ben Data Engineering and Semantics Research Unit Faculty of Sciences of Sfax University of Sfax Tunisia University of Tunis El-Manar Institut Superieur d'Informatique Tunis Tunisia MIRACL Laboratory Higher Institute of Computer and Multimedia Sakiet Ezzit Technopole Sfax242-3021 Tunisia Department of Computer Science Faculty of Sciences Tunis Tunisia

"Three fit" problems in the Bid Process Information System (BPIS) are still an open issue. The BPIS must be: integrated, flexible and interoperable in order to deal with the urbanization approach. Hence, operational, organizational, decision-making, and cooperative dimensions are four dimensions that have been identified to deal with such failures. Therefore, in this work, we are particularly interested in handling this failure by proposing an approach that focuses on the decision dimension of BPIS. This improves at the end of the various stages of the data warehouse lifecycle and takes into consideration the user's knowledge by focusing on their individual needs too. © 2022 Elsevier B.V.. All rights reserved.

关键词： Decision making

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A Decade of Scholarly Research on Open Knowledge Graphs

arXiv

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arXiv 2023年

作者： Turki, Houcemeddine Owodunni, Abraham Toluwase Taieb, Mohamed Ali Hadj Bile, René Fabrice Aouicha, Mohamed Ben Data Engineering and Semantics Research Unit University of Sfax Sfax Tunisia Masakhane Abuja Nigeria National Polytechnic School of Maroua University of Maroua Maroua Cameroon SisonkeBiotik Research Community Johannesburg South Africa University of the People PasadenaCA United States

The proliferation of open knowledge graphs has led to a surge in scholarly research on the topic over the past decade. This paper presents a bibliometric analysis of the scholarly literature on open knowledge graphs published between 2013 and 2023. The study aims to identify the trends, patterns, and impact of research in this field, as well as the key topics and research questions that have emerged. The work uses bibliometric techniques to analyze a sample of 4445 scholarly articles retrieved from Scopus. The findings reveal an ever-increasing number of publications on open knowledge graphs published every year, particularly in developed countries (+50 per year). These outputs are published in highly referred scholarly journals and conferences. The study identifies three main research themes: (1) knowledge graph construction and enrichment, (2) evaluation and reuse, and (3) fusion of knowledge graphs into NLP systems. Within these themes, the study identifies specific tasks that have received considerable attention, including entity linking, knowledge graph embedding, and graph neural networks. © 2023, CC BY.

关键词： Knowledge graph

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Decision-Making lifecycle of the Bid Process Information System

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Procedia Computer Science 2022年 204卷 930-936页

作者： Salma Trabelsi Sahbi Zahaf Manel Zekri Mohamed Ben Aouicha Data Engineering and Semantics Research Unit Faculty of Sciences of Sfax University of Sfax Tunisia University of Tunis El-Manar Institut Supérieur d'Informatique Tunis Tunisia MIRACL Laboratory Higher Institute of Computer and Multimedia Sakiet Ezzit Technopole Sfax 242-3021 Tunisia Department of Computer Science Faculty of Sciences of Tunis Tunisia

“Three fit” problems in the Bid Process Information System (BPIS) are still an open issue. The BPIS must be: integrated, flexible and interoperable in order to deal with the urbanization approach. Hence, operational, organizational, decision-making, and cooperative dimensions are four dimensions that have been identified to deal with such failures. Therefore, in this work, we are particularly interested in handling this failure by proposing an approach that focuses on the decision dimension of BPIS. This improves at the end of the various stages of the data warehouse lifecycle and takes into consideration the user's knowledge by focusing on their individual needs too.

关键词： Bid Process Information System Decision-Making data Warehouse Ontology Multidimensional Design

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Toward Optimal Periodic Crowd Tracking via Unmanned Aerial Vehicles

SSRN

引用

SSRN 2022年

作者： Chebil, Khalil Htiouech, Skander Khemakhem, Mahdi Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Data Engineering and Semantics Research Unit Faculty of Sciences of Sfax University of Sfax Sfax Tunisia Department of Mathematics and Business Intelligence College of Electronics and Telecommunications Engineering of Sfax University of Sfax Sfax Tunisia University of Carthage Tunisia

The crowd management and analysis (CMA) systems gained a lot of interest by the vulgarization of unmanned aerial vehicles (UAVs) use. Crowd tracking using UAVs is among the most important services provided by a CMA. In this paper, we consider the periodic crowd tracking (PCT) problem. It consists to follow up crowds, with UAVs, during the life-cycle of an open crowded area (OCA). Two criteria are considered for this purpose. The first is related to the CMA initial investment, and the second is to guarantee quality of service (QoS). The existing works are either focused on a very specified hypotheses or highly committed to CMAs applications context. Motivated by a real-world applications, but with a consideration of a high level of abstraction, we outline a new binary linear programming (BLP) model to optimally solve the PCT. To be closest to many real-world contexts, we carefully define and study a set of parameters related to the OCA characteristics, behaviors and the CMA initial infrastructure investment (e.g. UAVs, Charging Stations (CSs)). In order to periodically update the UAVs/crowds and UAVs/CSs assignments, we integrate the proposed BLP into a linear algorithm called PCTs *** main goal in this research is to study the PCT problem from both theoretical and numerical viewpoints. To investigate the PCTs solver effectiveness, we generate a diversified set of PCTs instances with different scenarios for simulation purpose. The empirical analysis of the results brought us to validate the BLP model and the PCTs solver, then to release a set of new challenges for future research directions. © 2022, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Knowledge-based construction of confusion matrices for multi-label classification algorithms using semantic similarity measures

arXiv

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arXiv 2020年

作者： Turki, Houcemeddine Taieb, Mohamed Ali Hadj Aouicha, Mohamed Ben Data Engineering and Semantics Research Unit Faculty of Sciences of Sfax University of Sfax Sfax Tunisia

So far, multi-label classification algorithms have been evaluated using statistical methods that do not consider the semantics of the considered classes and that fully depend on abst ract computations such as Bayesian Reasoning. Currently, there are several at tempts to develop ontology-based methods for a better assessment of supervised classification algorithms. In this research paper, we define a novel approach that aligns expected labels with predicted labels in multi-label classification using ontology-driven feature-based semantic similarity measures and we use it to develop a method for creating precise confusion matrices for a more effective evaluation of multi-label classification algorithms. © 2020, CC BY-NC-SA.

关键词： Image resolution

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A framework for integrating biomedical knowledge in Wikidata with open biological and biomedical ontologies and MeSH keywords

引用

Heliyon 2024年第19期10卷 e38448页

作者： Turki, Houcemeddine Chebil, Khalil Dossou, Bonaventure F.P. Emezue, Chris Chinenye Owodunni, Abraham Toluwase Hadj Taieb, Mohamed Ali Ben Aouicha, Mohamed Data Engineering and Semantics Research Unit Faculty of Sciences of Sfax University of Sfax Sfax Tunisia SisonkeBiotik Research Community Johannesburg South Africa Department of Computer Science University of the People Pasadena CA United States Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al Kharj 11942 Saudi Arabia Mila Quebec AI Institute Montreal Canada McGill University Montreal Canada Lelapa AI Johannesburg South Africa Technical University of Munich Munich Germany Faculty of Life Sciences University of Ilorin Ilorin Nigeria

This study presents a comprehensive framework to enhance Wikidata as an open and collaborative knowledge graph by integrating Open Biological and Biomedical Ontologies (OBO) and Medical Subject Headings (MeSH) keywords from PubMed publications. The primary data sources include OBO ontologies and MeSH keywords, which were collected and classified using SPARQL queries for RDF knowledge graphs. The semantic alignment between OBO ontologies and Wikidata was evaluated, revealing significant gaps and distorted representations that necessitate both automated and manual interventions for improvement. We employed pointwise mutual information to extract biomedical relations among the 5000 most common MeSH keywords in PubMed, achieving an accuracy of 89.40 % for superclass-based classification and 75.32 % for relation type-based classification. Additionally, Integrated Gradients were utilized to refine the classification by removing irrelevant MeSH qualifiers, enhancing overall efficiency. The framework also explored the use of MeSH keywords to identify PubMed reviews supporting unsupported Wikidata relations, finding that 45.8 % of these relations were not present in PubMed, indicating potential inconsistencies in Wikidata. The contributions of this study include improved methodologies for enriching Wikidata with biomedical information, validated semantic alignments, and efficient classification processes. This work enhances the interoperability and multilingual capabilities of biomedical ontologies and demonstrates the critical role of MeSH keywords in verifying semantic relations, thereby contributing to the robustness and accuracy of collaborative biomedical knowledge graphs. © 2024 The Authors

关键词： Biomedical relation identification Crowdsourcing MeSH keywords Open biological and biomedical ontologies PubMed Wikidata

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Welcome to the Science Olympics!

引用

Science (New York, N.Y.) 2024年第6717期386卷 20-22页

作者： Gheed Mukhles Sara Shakir Ginevra Fulco Daniela de Angeli Dutra Katie Burnette Frantisek Mach Amy Leigh Klocko Howard Lopes Ribeiro Houcemeddine Turki Paulo Bezerra Zelong Lu Bongjae Kim Shihao He Rigoberto Medina Andres Adetayo Emmanuel Obasa Song Lin Chua University of Nicosia Medical School Engomi Nicosia Cyprus. Joint Research Unit (UMR) of Fruit Biology and Pathology (BFP) French National Research Institute for Agriculture Food and Environment (INRAE) Villenave d'Ornon Cedex France. Department of Physics Technical University of Munich Milan Italy. Department of Biology Stanford University Stanford CA USA. Department of Evolution Ecology and Organismal Biology University of California Riverside Riverside CA USA. Faculty of Electrical Engineering University of West Bohemia Pilsen Czech Republic. Department of Biology University of Colorado Colorado Springs Colorado Springs CO USA. Research and Drug Development Center (NPDM) Federal University of Ceará Fortaleza Ceará Brazil. Data Engineering and Semantics Research Unit University of Sfax Sfax Tunisia. Department of Engineering and Technology Federal Rural University of the Semi-Arid Region Pau dos Ferros RN Brazil. School of Electrical Engineering Xi'an Jiaotong University Xi'an Shaanxi China. Department of Physics Kyungpook National University Daegu South Korea. Peking Union Medical College Hospital Peking Union Medical College and Chinese Academy of Medical Sciences Beijing China. Centro de Estudios Técnicos en Computación (CETEC) Miraval Cuernavaca Morelos Mexico. Registrar Research Support Office Research and Internationalisation Development and Support Division Faculty of Medicine and Health Sciences Stellenbosch University Cape Town Western Cape Province South Africa. Department of Applied Biology and Chemical Technology Hong Kong Polytechnic University Hong Kong China.

We gave young scientists this prompt: Imagine that science is an Olympic sport. At what event in your fi eld would you excel? Narrate your progress in the event in the voice of an Olympics announcer. Read a selection ...

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