@conference {ICBO_2018_19, title = {ICBO_2018_19: Current Development in the Evidence and Conclusion Ontology (ECO)}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

The Evidence \& Conclusion Ontology (ECO) has been developed to provide standardized descriptions for types of evidence within the biological domain. Best practices in biocuration require that when a biological assertion is made (e.g. linking a Gene Ontology term for a molecular function to a protein), the type of evidence supporting it is captured. In recent development efforts, we have been working with other ontology groups to ensure that ECO classes exist for the types of curation they support. These include the Ontology for Microbial Phenotypes and the Gene Ontology. In addition, we continue to support user-level class requests through our GitHub issue tracker. To facilitate the addition and maintenance of new classes, we utilize ROBOT (a command line tool for working with Open Biomedical Ontologies) as part of our standard workflow. ROBOT templates allow us to define classes in a spreadsheet and convert them to Web Ontology Language (OWL) axioms, which can then be merged into ECO. ROBOT is also part of our automated release process. Additionally, we are engaged in ongoing work to map ECO classes to Ontology for Biomedical Investigation classes using logical definitions. ECO is currently in use by dozens of groups engaged in biological curation and the number of ECO users continues to grow. The ontology, in OWL and Open Biomedical Ontology (OBO) formats, and associated resources can be accessed through our GitHub site (https://github.com/evidenceontology/evidenceontology) as well as the ECO web page (http://evidenceontology.org/).

}, keywords = {biocuration, evidence, gene annotation, ontology development, ontology mapping}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_19.pdf }, author = {Rebecca Tauber and James B. Munro and Suvarna Nadendla and Binika Chunara and Marcus C. Chibucos and Michelle Giglio} } @conference {ICBO_2018_46, title = {ICBO_2018_46: Standardizing Ontology Workflows Using ROBOT}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, series = {Proceedings of the International Conference on Biological Ontology (2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

Building and maintaining ontologies can be challenging due to the need to automate a number of common tasks, such as running quality control checks, automatic classification using reasoners, generating standard reports, extracting application-specific subsets, and managing ontology dependencies. These workflows are in some aspects analogous to workflows used in software engineering as part of the normal product lifecycle. However, in contrast to software development, there is a lack of easy to use tooling to support the execution of these workflows for ontology developers

}, keywords = {automation, import management, ontology development, ontology release, OWL, quality control, reasoning, workflows}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_46.pdf }, author = {Rebecca Tauber and James Balhoff and Eric Douglass and Chris Mungall and James A. Overton} }