@conference {IT406, title = {IT406-IP35: The Planteome Project}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

The Planteome project is a centralized online plant informatics portal which provides semantic integration of widely diverse datasets with the goal of plant improvement. Traditional plant breeding methods for crop improvement may be combined with next-generation analysis methods and automated scoring of traits and phenotypes to develop improved varieties. The Planteome project (www.planteome.org) develops and hosts a suite of reference ontologies for plants associated with a growing corpus of genomics data. Data annotations linking phenotypes and germplasm to genomics resources are achieved by data transformation and mapping species-specific controlled vocabularies to the reference ontologies. Analysis and annotation tools are being developed to facilitate studies of plant traits, phenotypes, diseases, gene function and expression and genetic diversity data across a wide range of plant species. The project database and the online resources provide researchers tools to search and browse and access remotely via APIs for semantic integration in annotation tools and data repositories providing resources for plant biology, breeding, genomics and genetics.

}, url = {http://ceur-ws.org/Vol-1747/IT406-IP35_ICBO2016.pdf}, author = {Laurel Cooper and Austin Meier and Justin Elser and Justin Preece and Xu Xu and Ryan Kitchen and Botong Qu and Eugene Zhang and Sinisa Todorovic and Pankaj Jaiswal and Marie-Ang{\'e}lique Laporte and Elizabeth Arnaud and Seth Carbon and Chris Mungall and Barry Smith and Georgios Gkoutos and John Doonan} } @conference {IT404, title = {IT404: Ten simple rules for biomedical ontology development}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Biomedical ontology development is often a time and resource consuming endeavor. To maximize efficiency of the process, we present a set of 10 simple rules covering basic technical requirements such as scoping and versioning, while considering additional elements such as licensing and community engagement. When applied, the rules will help avoid common pitfalls and jump-start ontology building.

}, url = {http://ceur-ws.org/Vol-1747/IT404_ICBO2016.pdf}, author = {Melanie Courtot and James Malone and Chris Mungall} } @conference {IT402, title = {IT402: Enhancing the Human Phenotype Ontology for Use by the Layperson}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

In rare or undiagnosed diseases, physicians rely upon genotype and phenotype information in order to compare abnormalities to other known cases and to inform diagnoses. Patients are often the best sources of information about their symptoms and phenotypes. The Human Phenotype Ontology (HPO) contains over 12,000 terms describing abnormal human phenotypes. However, the labels and synonyms in the HPO primarily use medical terminology, which can be difficult for patients and their families to understand. In order to make the HPO more accessible to non-medical experts, we systematically added new synonyms using non-expert terminology (i.e., layperson terms) to the existing HPO classes or tagged existing synonyms as layperson. As a result, the HPO contains over 6,000 classes with layperson synonyms.

}, url = {http://ceur-ws.org/Vol-1747/IT402_ICBO2016.pdf}, author = {Nicole Vasilevsky and Mark Engelstad and Erin Foster and Chris Mungall and Peter Robinson and Sebastian K{\"o}hler and Melissa Haendel} } @conference {IT204, title = {IT204: A sustainable approach to knowledge representation in the domain of sustainability: bridging SKOS and OWL}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {n/a}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Chris Mungall and Mark Jensen and Marie-Ang{\'e}lique Laporte and Pier Buttigieg} } @conference {IP17, title = {IP17: Comparison of ontology mapping techniques to map plant trait ontologies}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Crop specific ontologies for phenotype annotations in breeding have proliferated over the last 10 years. Across-crop data interoperability involves linking those ontologies together. For this purpose, the Planteome project is mapping the Crop Ontology traits (www.cropontology.org) to the reference ontology for plant traits, Trait Ontology (TO). Manual mapping is time-consuming and not sustainable in the long-run as ontologies keep on evolving and multiplicating. We are thus working on developing reliable automated mapping techniques to assist curators in performing semantic integration. Our study shows the benefit of the ontology matching technique based on formal definitions and shared ontology design patterns, compared to standard automatic ontology matching algorithm, such as AML (AgreementMakerLight).

}, url = {http://ceur-ws.org/Vol-1747/IP17_ICBO2016.pdf}, author = {Marie-Ang{\'e}lique Laporte and L{\'e}o Valette and Laurel Cooper and Chris Mungall and Austin Meier and Pankaj Jaiswal and Elizabeth Arnaud} }