Learning Non-Taxonomic Relations of Ontologies

2021 ◽  
Vol 17 (1) ◽  
pp. 97-122
Author(s):  
Mohamed Hassan Mohamed Ali ◽  
Said Fathalla ◽  
Mohamed Kholief ◽  
Yasser Fouad Hassan
Keyword(s):  
Systematic Review ◽  
State Of The Art ◽  
Research Work ◽  
Semantic Knowledge ◽  
Future Research ◽  
Automatic Extraction ◽  
Ontology Learning ◽  
Comprehensive Understanding ◽  
Specific Domain ◽  
Taxonomic Relations

Ontologies, as semantic knowledge representation, have a crucial role in various information systems. The main pitfall of manually building ontologies is effort and time-consuming. Ontology learning is a key solution. Learning Non-Taxonomic Relationships of Ontologies (LNTRO) is the process of automatic/semi-automatic extraction of all possible relationships between concepts in a specific domain, except the hierarchal relations. Most of the research works focused on the extraction of concepts and taxonomic relations in the ontology learning process. This article presents the results of a systematic review of the state-of-the-art approaches for LNTRO. Sixteen approaches have been described and qualitatively analyzed. The solutions they provide are discussed along with their respective positive and negative aspects. The goal is to provide researchers in this area a comprehensive understanding of the drawbacks of the existing work, thereby encouraging further improvement of the research work in this area. Furthermore, this article proposes a set of recommendations for future research.

scholarly journals Recommendation Systems for Education: Systematic Review

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1611
Author(s):  
María Cora Urdaneta-Ponte ◽  
Amaia Mendez-Zorrilla ◽  
Ibon Oleagordia-Ruiz
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Formal Education ◽  
Research Work ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Educational Resources ◽  
Future Research ◽  
Collaborative Approach ◽  
Developmental Approach

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

Sport in business studies: a state-of-the-art literature review

2018 ◽  
Vol 8 (5) ◽  
pp. 529-546
Author(s):  
Christofer Laurell ◽  
Sten Soderman
Keyword(s):  
Systematic Review ◽  
Literature Review ◽  
Marketing Strategy ◽  
Design Methodology ◽  
State Of The Art ◽  
Future Research ◽  
Content Type ◽  
Business Studies ◽  
Current State ◽  
State Of Research

PurposeThe purpose of this paper is to provide a systematic review of articles on sport published in leading business studies journals within marketing, organisational studies and strategy.Design/methodology/approachBased on a review of 38 identified articles within the subfields of marketing, strategy and organisation studies published between 2000 and 2015, the articles’ topical, theoretical and methodological orientation within the studied subfields were analysed followed by a cross-subfield analysis.FindingsThe authors identify considerable differences in topical, theoretical and methodological orientation among the studied subfields’ associated articles. Overall, the authors also find that articles across all subfields tend to be focussed on contributing to mature theory, even though the subfield of marketing in particular exhibits contributions to nascent theory in contrast to organisation studies and strategy.Originality/valueThis paper contributes by illustrating the current state of research that is devoted or related to the phenomenon of sport within three subfields in business studies. Furthermore, the authors discuss the role played by leading business studies journalsvis-à-vissport sector-specific journals and offer avenues for future research.

scholarly journals Systematic Review on Security and Privacy Requirements in Edge Computing: State of the Art and Future Research Opportunities

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 76541-76567 ◽  
Author(s):  
Muktar Yahuza ◽  
Mohd Yamani Idna Bin Idris ◽  
Ainuddin Wahid Bin Abdul Wahab ◽  
Anthony T. S. Ho ◽  
Suleman Khan ◽  
...  
Keyword(s):  
Systematic Review ◽  
State Of The Art ◽  
Edge Computing ◽  
Security And Privacy ◽  
Future Research ◽  
Research Opportunities ◽  
Privacy Requirements

scholarly journals Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2272 ◽  
Author(s):  
Faisal Khan ◽  
Saqib Salahuddin ◽  
Hossein Javidnia
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Research Work ◽  
Depth Estimation ◽  
Autonomous Driving ◽  
Estimation Methods ◽  
Future Research ◽  
Comprehensive Overview ◽  
Ill Posed ◽  
Monocular Depth

Monocular depth estimation from Red-Green-Blue (RGB) images is a well-studied ill-posed problem in computer vision which has been investigated intensively over the past decade using Deep Learning (DL) approaches. The recent approaches for monocular depth estimation mostly rely on Convolutional Neural Networks (CNN). Estimating depth from two-dimensional images plays an important role in various applications including scene reconstruction, 3D object-detection, robotics and autonomous driving. This survey provides a comprehensive overview of this research topic including the problem representation and a short description of traditional methods for depth estimation. Relevant datasets and 13 state-of-the-art deep learning-based approaches for monocular depth estimation are reviewed, evaluated and discussed. We conclude this paper with a perspective towards future research work requiring further investigation in monocular depth estimation challenges.

scholarly journals A bibliometric approach to quantitatively assess current research trends in 5G security

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
M. Najmul Islam Farooqui ◽  
Junaid Arshad ◽  
Muhammad Mubashir Khan
Keyword(s):  
State Of The Art ◽  
Research Work ◽  
Quantitative Description ◽  
Research Trends ◽  
Security And Privacy ◽  
Future Research ◽  
Content Type ◽  
Research Areas ◽  
Art Research ◽  
5G Security

PurposeAlongside the remarkable evolution of cellular communication to 5G networks, significant security and privacy challenges have risen which can affect the widespread adoption of advanced communication technologies. In this context, the purpose of this paper is to examine research within security and privacy for 5G-based systems highlighting contributions made by the research community and identify research trends within different subdomains of 5G security where open issues still exist.Design/methodology/approachThis paper uses a bibliographic approach to review the state-of-the-art in the field of 5G security and is the pioneering effort to investigate 5G security research using this methodology. Specifically, the paper presents a quantitative description of the existing contributions in terms of authors, organizations, and countries. It then presents detailed keyword and co-citation analysis that shows the quantity and pattern of research work in different subfields. Finally, 5G security areas are identified having open challenges for future research work.FindingsThe study shows that China leads the world in terms of published research in the field of 5G security with USA and India ranked second and third respectively. Xidian University, China is ranked highest for number of publications and h-index followed by University Oulu and AALTO University Finland. IEEE Access, Sensors and IEEE Internet of Things Journal are the top publication venues in the field of 5G security. Using VOSViewer aided analysis with respect to productivity, research areas and keywords, the authors have identified research trends in 5G security among scientific community whilst highlighting specific challenges which require further efforts.Originality/valueExisting studies have focused on surveys covering state-of-the art research in secure 5G network (Zhang et al. 2019), physical layer security (Wu et al., 2018), security and privacy of 5G technologies (Khan et al., 2020) and security and privacy challenges when 5G is used in IoT (Sicari et al. 2020). However, our research has revealed no existing bibliometric studies in this area and therefore, to our best knowledge, this paper represents pioneering such effort for security within 5G.

scholarly journals Engineering Human–Machine Teams for Trusted Collaboration

2020 ◽  
Vol 4 (4) ◽  
pp. 35
Author(s):  
Basel Alhaji ◽  
Janine Beecken ◽  
Rüdiger Ehlers ◽  
Jan Gertheiss ◽  
Felix Merz ◽  
...  
Keyword(s):  
Systems Engineering ◽  
State Of The Art ◽  
Research Work ◽  
Future Research ◽  
Engineering Systems ◽  
Intensive Research ◽  
Intelligent Machines ◽  
Interdisciplinary Approaches ◽  
Industrial Assembly ◽  
Core Requirements

The way humans and artificially intelligent machines interact is undergoing a dramatic change. This change becomes particularly apparent in domains where humans and machines collaboratively work on joint tasks or objects in teams, such as in industrial assembly or disassembly processes. While there is intensive research work on human–machine collaboration in different research disciplines, systematic and interdisciplinary approaches towards engineering systems that consist of or comprise human–machine teams are still rare. In this paper, we review and analyze the state of the art, and derive and discuss core requirements and concepts by means of an illustrating scenario. In terms of methods, we focus on how reciprocal trust between humans and intelligent machines is defined, built, measured, and maintained from a systems engineering and planning perspective in literature. Based on our analysis, we propose and outline three important areas of future research on engineering and operating human–machine teams for trusted collaboration. For each area, we describe exemplary research opportunities.

scholarly journals The effects of interlingual and intralingual subtitles on second language learning/acquisition: a state-of-the-art review

2015 ◽  
Vol 54 (1) ◽  
pp. 161-182 ◽  
Author(s):  
Rafael Matielo ◽  
Raquel Carolina Souza Ferraz D'Ely ◽  
Luciane Baretta
Keyword(s):  
Systematic Review ◽  
Second Language ◽  
Second Language Acquisition ◽  
Language Learning ◽  
Second Language Learning ◽  
State Of The Art ◽  
Relevant Literature ◽  
Future Research ◽  
Disciplinary Field ◽  
Experimental Use

The disciplinary field of Second Language Acquisition (SLA) has witnessed an increasing interest in the investigation of the effects of subtitled and captioned audiovisual materials on domains of language learning/acquisition. In this context, this paper seeks to provide a systematic review of recent studies related to language learning aspects aided by the instructional/experimental use of subtitled and captioned materials. The present paper draws on relevant literature in the field of SLA that interfaces with subtitling/captioning, while outlining their goals and main findings. This paper also aims to unveil which dimensions have merited scholar attention the most in the last two decades. Finally, some considerations are made regarding possible avenues for future research, taking into account the existing literature and underinvestigated issues.

scholarly journals Organizational and Formational Structures of Networks in the Mental Lexicon: A State-Of-The-Art through Systematic Review

Languages ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Luke McCarthy ◽  
Imma Miralpeix
Keyword(s):  
Systematic Review ◽  
State Of The Art ◽  
Mental Lexicon ◽  
Small World ◽  
Structural Features ◽  
Future Research ◽  
Emergent Properties ◽  
Scale Free ◽  
Scale Free Networks ◽  
Network Patterns

This state-of-the-art presents a systematic exploration on the use of network patterns in global research efforts to understand, organize and represent the mental lexicon. Results have shown an increase over recent years in the usage of complex, small-world and scale-free network patterns within the literature. With the increasing complexity of network patterns, we see more potential in the inter-disciplinary exploration of the mental lexicon through universal and mathematically-describable, behavioral patterns in small-world and scale-free networks. A systematic review of 36 items of methodologically-selected literature serve as a means to explore how the greater literary body understands network structures within the mental lexicon. Network-based approaches are discriminated between three contrasting varieties. These include: ‘simple networks’, characterized by arbitrarily organized graph patterns of metaphorical importance; ‘connectionist networks’, a broad category of networks which explore the structural features of a system through the analysis of emergent properties; and lastly ‘complex networks’, distinguished as small-world, scale-free networks which follow a strict and mathematically-describable structure in agreement with the Barabási–Albert model. Each network approach is explored in terms of their discernible differences which relate to their parameters and affect their implications. A final evaluation of observed patterns within the selected literature is offered, as well as an elaboration on the sense of trajectory beheld in the research in order to offer insight and orientation for future research.

scholarly journals Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

Robotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 52
Author(s):  
Luiz F. P. Oliveira ◽  
António P. Moreira ◽  
Manuel F. Silva
Keyword(s):  
Computer Vision ◽  
State Of The Art ◽  
Research Work ◽  
Development Stage ◽  
Robotic Systems ◽  
Future Research ◽  
Yield Estimation ◽  
Commercial Development ◽  
Land Preparation ◽  
Smart Agriculture

The constant advances in agricultural robotics aim to overcome the challenges imposed by population growth, accelerated urbanization, high competitiveness of high-quality products, environmental preservation and a lack of qualified labor. In this sense, this review paper surveys the main existing applications of agricultural robotic systems for the execution of land preparation before planting, sowing, planting, plant treatment, harvesting, yield estimation and phenotyping. In general, all robots were evaluated according to the following criteria: its locomotion system, what is the final application, if it has sensors, robotic arm and/or computer vision algorithm, what is its development stage and which country and continent they belong. After evaluating all similar characteristics, to expose the research trends, common pitfalls and the characteristics that hinder commercial development, and discover which countries are investing into Research and Development (R&D) in these technologies for the future, four major areas that need future research work for enhancing the state of the art in smart agriculture were highlighted: locomotion systems, sensors, computer vision algorithms and communication technologies. The results of this research suggest that the investment in agricultural robotic systems allows to achieve short—harvest monitoring—and long-term objectives—yield estimation.

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