@conference {ICBO_2018_20,
	title = {ICBO_2018_20: KNowledge Acquisition and Representation Methodology (KNARM)},
	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 = {<p>Technological advancements in many fields have led to huge increases in data production, including data volume, diversity, and the speed at which new data is becoming available. In accordance with this, there is a lack of conformity in the ways data is interpreted. In-depth analyses making use of various data types and data sources, and extracting knowledge has become one of the many challenges with this big data. This is especially the case in life-sciences where simplification and flattening of diverse data types often leads to incorrect predictions. Effective applications of big data approaches in the life sciences require better, knowledge-based, semantic models that are suitable as a framework for big data integration, while avoiding overly extreme simplification, such as reducing various biological data types to the gene level. A major challenge in developing such semantic knowledge models, or ontologies, is the knowledge acquisition bottleneck. Automated methods are still very limited and significant human expertise is required. In this research, we describe a methodology to systematize this knowledge acquisition and representation challenge, termed KNowledge Acquisition and Representation Methodology (KNARM). We also examplify how KNARM was applied on three ontologies: BioAssay Ontology (BAO), LINCS FramEwork Ontology (LIFE) ,and Drug Target Ontology (DTO) built for three different projects: BioAssay Ontology, Library of Integrated Network-Based Cellular Signatures (LINCS), and Illuminating the Druggable Genome (IDG), and how the methodology help the ontologies work together in complex queries.</p>
},
	keywords = {big data, ontology building methodology, semi-automated ontology building},
	author = {Hande K{\"u}{\c c}{\"u}k-Mcginty and Stephan Schurer and Ubbo Visser}
}
@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}
}
