Published by: Springer
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 569 | 33 | 37 | 10 |
The objective of Journal of Biomedical Semantics is to combine knowledge in the areas of Ontology (information science), Data science, Information retrieval, Artificial intelligence and Natural language processing. The Ontology (information science) study tackled is a key component of adjacent topics in the area of Annotation. The journal holds forums on Data science that merges themes from other disciplines such as Semantic Web, Basic Formal Ontology, Data integration, Biological Ontologies and Interoperability.
The journal tackles topics on Semantic Web, which can potentially contribute to the wider field of World Wide Web. The research on Information retrieval tackled can also make contributions to studies in the areas of Domain (software engineering), Semantics and Metadata. In addition to Artificial intelligence research, it aims to explore topics under Context (language use), Machine learning, Named-entity recognition and Data mining.
While the primary focus in it is Data mining, it also dissects topics surrounding Computational biology and Gene as a whole. It tackles research in Information extraction and Semantic similarity as part of the general discipline of Natural language processing, however, it also discusses concepts in Structure (mathematical logic). It connects research in RDF with the related topic of Linked data.
Ontology (information science), Information retrieval, Data science, Open Biomedical Ontologies and Semantic Web are the main subjects of interest in the published articles. Aside from discussions in Ontology (information science), the published papers also deal with the subject of World Wide Web which intersects with Software versioning disciplines. The studies on Information retrieval discussed at the most cited papers can also contribute to research in the domains of Annotation, Semantics and Set (abstract data type).
The journal focuses largely on the fields of Ontology (information science), Artificial intelligence, Natural language processing, Data science and Domain (software engineering). The presentations focused mostly on Ontology (information science) in an attempt to further explore topics in Information retrieval. The presented Information retrieval study covers related areas such as Semantic enhancement and also touches on topics like Semi automatic.
The featured Artificial intelligence research zeroes in on concepts in Deep learning and Annotation but also tackles themes under Sequence. Topics in Natural language processing explored in the journal were investigated in conjunction with research in Artificial neural network, Recall and Multiple kernel learning. The work on Data science tackled in Journal of Biomedical Semantics brings together disciplines like Precision medicine, Turing, Biological Ontologies, Web Ontology Language and Phenomics.
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 Journal of Biomedical Semantics (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 Journal of Biomedical Semantics (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, 15.79% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 31.25% were posted by at least one author from the top 10 institutions publishing in the journal. Another 6.25% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 18.75% of all publications and 43.75% 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.
In an academic journal such as this, it can be equally enlightening to consider the educational and professional background of influential authors and researchers in these fields. As this can provide readers with an idea of the pathway to success in Biomedical Semantics, it is a helpful resource for those interested in pursuing a career in any of the discussed subjects. For instance, researchers who are breaking new ground in the merging of Semantic Web studies and Basic Formal Ontology may have graduated from esteemed universities with a degree in biomedical engineering. It's also likely that many have pursued higher education and achieved a master's degree or a Ph.D. in a closely related field. This might inspire prospective researchers to follow a similar academic trajectory. It is also worth noting that these fields often command interdisciplinary expertise. Thus, it's not unusual for these researchers to have knowledge and experience outside of conventional biomedical semantics. For example, a career in Biological Ontologies might involve understanding biology, biomedicine, or even molecular genetics as well as computer science to handle the data integration aspect. We understand that the educational pathway to being a researcher in these areas might be daunting. But don't worry, even a seemingly unrelated career path can lead you to these fields. Take a look at this guide on how to become an art teacher in North Dakota and see how transferable skills and passion can lead to surprising career switches. Understanding these background stories of prominent researchers give readers insights into their career trajectory, and could even inspire others to follow in their footsteps.
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(2021)Unknown
(2022)Sara Althubaiti;Şenay Kafkas;Marwa Abdelhakim;Robert Hoehndorf
(2020)Emma Norris;Emma Norris;Janna Hastings;Marta M Marques;Marta M Marques;Ailbhe N Finnerty Mutlu
(2021)Luke T Slater;William Bradlow;William Bradlow;Simon Ball;Simon Ball;Robert Hoehndorf
(2021)Şenay Kafkas;Sara Althubaiti;Georgios V Gkoutos;Robert Hoehndorf
(2021)Hans Moen;Kai Hakala;Laura-Maria Peltonen;Hanna-Maria Matinolli
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