@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_30, title = {ICBO_2018_30: An Expertise Ontology for Cooperative Extension}, 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 national Cooperative Extension System is a non-formal educational network with a mission of advancing agriculture, the environment, human health and well-being, and community economic development that is coordinated through and distributed across the state land-grant universities. At present there is no easy way to query knowledge assets across individual extension organizations with respect to expertise, accomplished projects, or successful interventions. In collaboration with the umbrella organization eXtension.org, we have developed a prototype ontology for describing expertise across the extension network. This ontology aims to provide a framework enabling linking experts, projects, organizations, competencies, digital resources, and other related assets. There are 14 major classes in this ontology: persons, roles, organizations, competencies, expertise types, subject domains, programs, networks, projects, activities, information resources, audiences, issues, and stakeholders. All these classes are anchored in the Basic Formal Ontology (Arp et al. 2015).Other ontologies used for these classes are FOAF, SKOS, the VIVO Ontology for Researcher Discovery (Mitchell 2018}),and the ASI Sustainable Sourcing Ontology (Hollander 2018). These classes fall into several categories. A couple of these classes such as information resources and subject domains tie into existing taxonomies, for instance subject domains being drawn from the National Institute of Food and Agriculture{\textquoteright}s Manual of Classification for Agricultural and Forestry Research, Education, and Extension (NIFA 2005). Other classes here are intended to support development of databases of instances, for instance directories of persons and organizations with information on subject domain expertise and competencies. Finally, several of these classes occupy structural positions in the ontology, for instance role serving as a class that links persons and organizations. A total of 197 classes are presentlydefined, including those enumerated from various taxonomies. Properties for this ontology have beendrawn from the Relations Ontology (Mungall 2018) and VIVO. At present 14 object properties havebeen incorporated in the ontology.Our development of this expertise ontology is part of a broader initiative to create a set of ontologies describing entities and interactions across the entirety of the food system, ranging from food production, impacts and linkages to the environment, to food consumption, nutrition, and human well-being.

}, keywords = {agriculture, Cooperative Extension, expertise, ontology development}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_30.pdf }, author = {Allan D. Hollander and Christine Geith and Matthew C. Lange} } @conference {ICBO_2018_37, title = {ICBO_2018_37: Ontology-Enhanced Representations of Non-image Data in The Cancer Image Archive}, 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 Cancer Image Archive (TCIA) hosts over 11 million de-identified medical images related to cancer for research reuse. These are organized around DICOM-format radiological collections that are grouped by disease type, modality, or research focus. Many collections also include diverse non-image datasets in a variety of formats without a common approach to representing the entities that the data are about. This paper describes work to make these diverse non-image data more accessible and usable by transforming them into integrated semantic representations using Open Biomedical Ontologies, highlights obstacles encountered in the data, and presents detailed representations data found in select collections.

}, keywords = {Cancer, imaging, ontology development, semantics}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_37.pdf }, author = {Jonathan Bona and Tracy Nolan and Mathias Brochhausen} } @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} } @conference {174, title = {ICBO_2018_75: ECOCORE: An ontology for core ecological concepts}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

The ontologies in the biological and biomedical domain represent entities from a broad spectrum of the life sciences such as molecules, diseases, phenotypes, and environments. However, until now, ontologies representing ecological concepts, such as trophic modes like autotrophy and insectivory, have been missing. In response to the needs of aggregators such as the Encyclopedia of Life and DataONE, we have developed an ontology of core ecological concepts, ECOCORE. The initial focus of development for ECOCORE is trophic dynamics. An example demonstrating this initial focus is the autotroph class, which is a subclass of organism. It is equivalent to organism and ({\textquoteleft}capable of{\textquoteright} some autotrophy). Autotrophy is a process. ECOCORE development calls on classes from many OBO ontologies, but involves specifically important collaborations with the Environment Ontology, the Behavior Ontology, and the Relations Ontology. ECOCORE development is open to community participation at the GitHub site https://github.com/EcologicalSemantics/ecocore. This presentation will describe ECOCORE and show some of its uses.

}, keywords = {biodiversity, ecology, evolutionary biology, Ontology, ontology development, trophic dynamics}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Anne Thessen and Pier Luigi Buttigieg and Ramona Walls and Jennifer Verdolin and Katja Schulz} }