| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 819 | 28 | 30 | 5 |
International Journal of Semantic Computing focuses on Artificial intelligence, Information retrieval, Natural language processing, World Wide Web and Semantic computing. The studies on Artificial intelligence discussed can also contribute to research in the domains of Machine learning, Computer vision and Pattern recognition. Specifically, studies on Support vector machine are prevalent in the Machine learning works discussed.
Semantic Web Stack, Semantic search, Linked data, Search engine indexing and RDF are among the concentrations of Information retrieval that garnered much attention in the journal. Most of the Linked data studies addressed also intersect with SPARQL. Semantic similarity is a major topic of Natural language processing research presented in it.
World Wide Web research presented in it encompasses a variety of subjects, including Ontology (information science), Context (language use) and Multimedia. The subject of Semantic grid, which is connected to the field of Semantic technology, serves as the foundation of the Semantic computing research featured in it. Social Semantic Web is a major topic of Semantic Web research.
The published papers primarily tackle Artificial intelligence, Natural language processing, Semantic computing, Information retrieval and Software engineering. The published articles with studies in Artificial intelligence featured incorporate elements of Machine learning, Dialog box and Cognitive computing. In addition to Information retrieval research, the published articles aim to explore topics under Annotation, Scalability, Semantic representation and Representation (arts).
International Journal of Semantic Computing mainly tackles studies in Artificial intelligence, Computer vision, Speech recognition, World Wide Web and Machine learning. The journal holds forums on Artificial intelligence that merges themes from other disciplines such as Composition (language) and Natural language processing. It connects the study in Computer vision with the closely related area of Search engine indexing.
Topics in Speech recognition explored in International Journal of Semantic Computing were investigated in conjunction with research in Spotting, Word embedding, Coherence (statistics), Event (computing) and Convolutional neural network. Web content research are fields of study within World Wide Web but they also intertwine with concepts in Publication. The studies in Machine learning featured incorporate elements of Tree based and Error detection and correction.
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 International Journal of Semantic Computing (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 International Journal of Semantic Computing (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, 0.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 12.50% 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 31.25% of all publications and 56.25% 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.
One of the pathways to contribute to the international journal of Semantic Computing could be through attaining a sound expertise in any of its main research fields such as Artificial Intelligence, Information retrieval or Natural Language processing. Formal education lays the foundation of these proficiencies. As an example, let's consider the domain of education. Educational experts with a solid understanding of Machine Learning and Natural Language Processing can significantly contribute to enhancing the learning experience of students, thereby impacting Semantic Computing in an educational context. A manifestation of such an educational expert could be an elementary school teacher who understands how to incorporate technological advances into their pedagogy. However, embarking on such an educational career requires careful planning and commitment. Thus, to offer insights on that, we have detailed the requirements and guidelines on how to become an elementary school teacher in Alabama. This article provides valuable information on education requirements, certification procedures, and other pertinent details relevant to aspiring elementary school teachers with an interest in Semantic Computing. This cross-disciplinary approach to career planning, integrating Semantic Computing with traditional roles in education, paves the way for innovative practices in teaching that can be beneficial in the technologically evolving educational landscape.
Olav A. Norgård Rongved;Steven Alexander Hicks;Vajira Thambawita;Håkon Kvale Stensland
(2021)Domenico Lembo;Federico Maria Scafoglieri
(2020)Bohui Xia;Hiroyuki Seshime;Xueting Wang;Toshihiko Yamasaki
(2020)Mojtaba Karimi;Edwin Babaians;Martin Oelsch;Eckehard Steinbach
(2021)Felix Kuhr;Tanya Braun;Magnus Bender;Ralf Möller
(2020)James R. Kubricht;Alberto Santamaría-Pang;Chinmaya Devaraj;Aritra Chowdhury
(2020)For students interested in expanding their expertise beyond traditional Computer Science, there are several promising online degree options. Many aspiring engineers pursue an engineer degree online to gain the technical skills required in fields like software, hardware, and systems development. These programs often offer flexible schedules suitable for working professionals.
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