@conference {ICBO_2018_15, title = {ICBO_2018_15: Quality Assurance of Ontology Content Reuse}, 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 ontologies is difficult and time-consuming. As such, content reuse has been promoted as an important guiding principle in ontology development. Reusing content from other ontologies can reduce the overall effort involved in new ontology construction and provide better alignment with existing knowledge modeling. However, reuse is not a panacea, and it comes with its own attendant difficulties. In this paper, we investigate some common quality assurance issues associated with reuse, such as duplicated content and versioning problems. Some heuristic-based approaches are proposed for analyzing ontologies for these kinds of quality assurance issues. An analysis is carried out on a sample of the large collection of BioPortal-hosted ontologies, many of which employ reuse. The findings indicate that curators and authors, particularly those new to the reuse process, should be on the alert when developing an ontology with reused content to avoid introducing problems into their own ontologies.

}, keywords = {BioPortal, modeling, Ontology, ontology quality assurance, ontology reuse}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_15.pdf }, author = {Michael Halper and Christopher Ochs and Yehoshua Perl and Sivaram Arabandi and Mark Musen} } @conference {ICBO_2018_23, title = {ICBO_2018_23: Prot{\'e}g{\'e} 5.5 {\textendash} Improvements for Editing Biomedical Ontologies}, 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 = {

We present Prot{\'e}g{\'e} 5.5, a significant update to the Prot{\'e}g{\'e} Desktop software, which contains new features that are geared towards editing biomedical ontologies. This version of Prot{\'e}g{\'e} contains user-interface enhancements and optimizations that should make the browsing and editing of OBO-library-style biomedical ontologies easier, faster and more efficient when compared to previous versions of Prot{\'e}g{\'e}.

}, keywords = {Biomedical Ontology Editing, OWL, Prot{\'e}g{\'e}}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_23.pdf }, author = {Matthew Horridge and Rafael S Gon{\c c}alves and Csongor I Nyulas and Tania Tudorache and Mark Musen} } @conference {ICBO_2018_32, title = {ICBO_2018_32: Ontolobridge {\textendash} A Semi-Automated Ontology Update Request System for Better FAIR-ifying BioAssay Ontology, Drug Target Ontology, and LINCS Ontology}, 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 = {Ontologies are products that are becoming more relevant for data science as the need for standardized vocabulary and meta-data is increasing. However, if they want to stay relevant, ontologies have a constant need for evolving, especially in domains that involve dynamic data, like life-sciences data. Based on the need pointed out by domain experts for updating and/or requesting ontology terms while annotating BioAssay Protocols, we are developing a semi-automated technology that will allow users to request new terms and update existing ones easier. This need was pointed out by domain experts who are using CDD{\textquoteright}s new tool BioAssay Express (BAE). BAE allows users to annotate their bioassays in a semi-automated and standardized fashion using the highly-accessed ontologies (BioAssay Ontology (BAO), Gene Ontology (GO), Disease Ontology (DOID), and Drug Target Ontology (DTO) among others) in the background. Our goal in the Ontolobridge project is to help various users of BAE (researchers performing curation, dedicated curators, IT specialists, ontology owners, and librarians/repositories) request and update the existing vocabulary provided by BAO in a semi-automated way, with a user-friendly interface. Furthermore, APIs and tools including templates from CEDAR will be created in order to would allow users to request new ontology terms or changes to existing terms easily during the annotation process. In this way, we{\textquoteright}re aiming to increase the Findability, Interoperability, Accessibility, and Reproducibility (FAIR) of above mentioned ontologies and BioAssay Protocols better.}, keywords = {annotation, big data, BioAssay Express, BioAssay Ontology, CEDAR, FAIR, FAIR data, life sciences big data, ontology term request, semi-automated ontology updates}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Hande K{\"u}{\c c}{\"u}k-Mcginty and Alex Clark and John Graybeal and Daniel Cooper and John Turner and Michael Dorf and Mark Musen and Barry Bunin and Stephan Schurer} } @conference {ICBO_2018_39, title = {ICBO_2018_39: TOCSOC: A temporal ontology for comparing the survival outcomes of clinical trials in oncology}, 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 outcome of clinical trials for cancer is typically summarized in terms of survival. However, different trials for the same disease may use different measures of survival, or use differing vocabulary to refer to the same outcome measure. This makes it harder to automate an objective comparison of treatments. We propose a temporal ontology of survival outcome measures that a) helps to standardize the vocabulary for reporting survival outcomes and b) makes it possible to automatically rank the relative efficacy of different treatments. The approach has been illustrated by examples from the oncology literature. The temporal ontology and the accompanying reasoner are freely available on Github (https://github.com/pdddinakar/TOCSOC)

}, keywords = {clinical trials, oncology, reasoning, survival outcome, temporal ontology}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_39.pdf }, author = {Deendayal Dinakarpandian and Bhavish Dinakar and Michaela Liedtke and Mark Musen} }