0269-8889
Published by: Cambridge University Press
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 678 | 19 | 20 | 8 |
The journal mainly deals with areas of study such as Artificial intelligence, Knowledge management, Data science, Management science and Cognitive science. Most of the Artificial intelligence studies addressed also intersect with Machine learning. Specifically, studies on Knowledge engineering are prevalent in the Knowledge management works discussed.
The most cited publications mainly tackle studies in Artificial intelligence, Knowledge management, Management science, Data science and Ontology (information science). While the most cited papers focused on Knowledge management, they were also able to explore topics like Software agent, Context (language use), Point (typography) and Argumentation theory. The journal publications facilitate discussions on Ontology (information science) that incorporate concepts from other fields like Software engineering and World Wide Web.
The journal explores disciplines such as Medical education, Artificial intelligence, Mathematics education, Developmental psychology and Social psychology. The featured Medical education research zeroes in on concepts in Career development and Career planning but also tackles themes under Evaluation system and Training program. Reinforcement learning is a focus of the presented Artificial intelligence works and it dives deep in Reinforcement learning.
Field (Bourdieu), Curriculum framework and Convergence (relationship) are some topics wherein Mathematics education research discussed in the journal have an impact. The studies in Learned helplessness under the umbrella field of Developmental psychology overlap with concepts in Life stress, Experiential avoidance and Smartphone addiction. It explores themes in Social psychology like Social comparison theory, Identity (social science) and Psychological well-being and links them with other fields of study like Burnout.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Knowledge Engineering Review (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Knowledge Engineering Review (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 92.98% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 0.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 0.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 50.00% of all publications and 50.00% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
The applications and prominence of knowledge engineering extend not only to academic research but also to vast career prospects across sectors. A common career path for individuals specializing in knowledge engineering is becoming an educator, especially in middle and high school settings, where knowledge engineering concepts are just being introduced to the students. To emphasize the significance of the Knowledge Engineering Review and its topics in career development, it is essential to establish links to real-world job scenarios. For instance, a career in teaching middle schoolers in Indiana requires an understanding of these research articles and trends. Notably, the state's emphasis on instilling Artificial Intelligence (AI) knowledge from an early age has heightened the demand for teachers specializing in knowledge engineering topics. Thus, studying the Knowledge Engineering Review and its published papers could be instrumental in preparing future educators for such a profession. For those interested in such a career, it is important to inquire: {anchor}. The process of becoming a middle school teacher specializing in math and more importantly, AI, in Indiana is detailed therein. Job prospects for such professionals are amplified in an era where technology and AI are core to various aspects of society, inculcating the importance of knowledge acquired through critical journals like the Knowledge Engineering Review. This linkage between academic research and real-world applications enhances the relevancy of the journal and encourages the continuity of research in the field, contributing to societal advancements.
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(2021)Arushi Jain;Khimya Khetarpal;Doina Precup
(2021)Jomar da Silva;Kate Revoredo;Fernanda Araujo Baião;Jérôme Euzenat
(2020)Roxana Rădulescu;Patrick Mannion;Yijie Zhang;Diederik M. Roijers
(2020)Gabriel De Oliveira Ramos;Bruno Castro da Silva;Roxana Radulescu;Ana Bazzan
(2020)Naipeng Dong;Guangdong Bai;Lung-Chen Huang;Edmund Kok Heng Lim
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