Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from EDBT 2026 / reviewers in the wild / expert

Johannes Darms

dblp:226/2139 · DBLP profile ↗
← Back
2ranked-venue papers
0as first author
1since 2021 · last 2021
0000-0001-5809-2276ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 1 since 2021Artificial intelligence and machine learning · 1Software engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › knowledge representation in biology
biomedical ontology		 0.512021
The COVID-19 Ontology · Bioinform. 2021

Methods — techniques the papers use, named apart from their topics

ontology engineering · 0.5
YearPublicationVenuePosition
2021 The COVID-19 Ontology
abstract
MOTIVATION: The COVID-19 pandemic has prompted an impressive, worldwide response by the academic community. In order to support text mining approaches as well as data description, linking and harmonization in the context of COVID-19, we have developed an ontology representing major novel coronavirus (SARS-CoV-2) entities. The ontology has a strong scope on chemical entities suited for drug repurposing, as this is a major target of ongoing COVID-19 therapeutic development. RESULTS: The ontology comprises 2270 classes of concepts and 38 987 axioms (2622 logical axioms and 2434 declaration axioms). It depicts the roles of molecular and cellular entities in virus-host interactions and in the virus life cycle, as well as a wide spectrum of medical and epidemiological concepts linked to COVID-19. The performance of the ontology has been tested on Medline and the COVID-19 corpus provided by the Allen Institute. AVAILABILITYAND IMPLEMENTATION: COVID-19 Ontology is released under a Creative Commons 4.0 License and shared via https://github.com/covid-19-ontology/covid-19. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/COVID-19. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Astghik Sargsyan, Alpha Tom Kodamullil, Shounak Baksi, Johannes Darms, Sumit Madan, Stephan Gebel, Oliver Keminer, Geena Mariya Jose, Helena Balabin, Lauren Nicole Delong, Manfred Kohler, Marc Jacobs 0001, Martin Hofmann-Apitius
 Bioinform.4
2018 What was the Question? A Systematization of Information Retrieval and NLP Problems
abstract
In this paper we suggest a novel systematization of Information Retrieval and Natural Language Processing problems.Using this rather general description of problems we are able to discuss and proof the equivalence of some problems.We provide reformulations of well-known problems like Named Entity Recognition using our novel description and discuss further research and the expected outcome.We will discuss the relation of two problems, cluster labeling and search query finding.With these results we are able to provide a novel optimization approach to both problems.This novel systematization approach provides a yet unknown view generating new classes of problems in NLP.It brings application and algorithmic approaches together and offers a better description with concepts of theoretical computer science.
Jens Dörpinghaus, Johannes Darms, Marc Jacobs 0001
 FedCSIS2