@conference {344, title = {W14-02: Systematizing Definitions in Ontologies}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, address = {Corvallis, Oregon, USA}, abstract = {

In this talk, I will present a methodological proposal to systematize textual and logical definitions in ontologies. I will use as a case study definitions of social entities from the Ontology of Medically Related Social Entities (OMRSE). The goal is to show how this method can simplify and accelerate the definition writing process.

}, keywords = {definition templates, definitions, logical definitions, ontologies, textual definitions}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Sepp{\"a}l{\"a}, Selja} } @conference {343, title = {W14-01: Uncovering Definition Coverage in the OBO Foundry Ontologies}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, address = {Corvallis, Oregon, USA}, abstract = {

Definitions, both logical and textual, are an essential part of ontologies. Textual definitions help human users disambiguate and regularize their understanding and use of ontology terms to achieve intra- and inter-personal consistency and avoid errors, for example, when annotating scientific data, integrating databases with an ontology, or importing terms into other ontologies. Logical definitions are needed, among other things, for checking the consistency of the ontology and carrying out inferences, for example, over data that has been annotated with ontology terms. Despite the best efforts of ontology developers, it is not uncommon to see missing definitions. While the OBO Foundry explicitly states that its member ontologies should have a substantial fraction of their terms defined, these ontologies still often lack one or both kinds of definitions. Statistics on definition coverage in the OBO Foundry ontologies are scarce and it is difficult to tell what effectively constitutes a substantial fraction of terms in an ontology. In the present work, we examine the coverage of textual and logical definitions throughout the OBO Foundry ontologies in order to uncover the big picture and to give more detailed insight into logical definitions in these ontologies. We have found that textual definition coverage is reasonably good over the OBO Foundry ontologies (66\%), but that the core ontologies exhibit a higher definition coverage (86\%) than the non-core ones (64\%). Logical definitions follow a similar trend, but with lower values {\textemdash} overall, the OBO Foundry has a 30\% coverage, while core ontologies are better covered (53\%) than non-core ones (28\%).

}, keywords = {definition coverage, logical definitions, OBO Foundry, ontology, textual definitions}, url = {http://ceur-ws.org/Vol-1747/IP16_ICBO2016.pdf}, author = {Schlegel, Daniel R. and Selja Sepp{\"a}l{\"a} and Elkin, Peter L.} } @conference {364, title = {W12-06: Evolution of Floral Form: The potential of ontologies across diverse plant lineages}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

TBA

}, keywords = {evo-devo, flower development, fusion, morphological evolution, petaloidy, Zingiberales}, author = {Chelsea Specht} } @conference {W14-03, title = {W12-04: The Plant Phenology Ontology for Phenological Data Integration}, 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 = {

Plant phenology the timing of life-cycle events, such as flowering or leafing-out has cascading effects on multiple levels of biological organization, from individuals to ecosystems. Despite the importance of understanding phenology for managing biodiversity and ecosystem services, we are not currently able to address continent-scale phenological responses to anticipated climatic changes. This is not because we lack relevant data. Rather, the problem is that the disparate organizations producing large-scale phenology data are using non-standardized terminologies and metrics during data collection and data processing. Here, we preview the Plant Phenology Ontology, which will provide the standardized vocabulary necessary for annotation of phenological data. We are aggregating, annotating, and analyzing the most significant phenological data sets in the USA and Europe for broad temporal, geographic, and taxonomic analyses of how phenology is changing in relation to climate change.

}, url = {http://icbo2016.cgrb.oregonstate.edu/sites/default/files/W14-03_ICBO2016.pdf}, author = {Brian J. Stucky and John Deck and Ellen Denny and Robert P. Guralnick and Ramona L. Walls and Jennifer Yost} } @conference {363, title = {W12-02: TraitBank: semantic integration of biodiversity data from diverse sources}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Easy access to large amounts of biodiversity data has the potential to transform research across the life sciences. However, most of the data generated so far are not easily integrated or repurposed due to a lack of standardization in how scientists talk about the characteristics of organisms, how they describe the context of their observations, and how they document the methods with which the data were collected. TraitBank (eol.org/traitbank) addresses this impediment by linking information aggregated from diverse sources to community-developed ontologies and controlled vocabularies. These post hoc annotations help to organize distributed, heterogeneous knowledge into a lightweight, scalable semantic framework supporting retrieval and reuse for a variety of applications, ranging from large-scale synthetic analyses of biodiversity to linked data products and hands-on data science in the classroom. The TraitBank data store currently holds over 11 million measurements and facts for more than 1.7 million taxa including animals, plants, fungi, and microbes. These data are mobilized from major biodiversity information systems (e.g., International Union for Conservation of Nature, Ocean Biogeographic Information System, Paleobiology Database), open literature repositories (e.g., Dryad, Ecological Archives, Pangaea), label data from natural history collections, and legacy/unpublished data sets. TraitBank subject coverage \ is very broad ranging from distribution, ecology, and life history to morphology and physiology. Data can be downloaded via CSV files or a JSON-LD service. Reuse and redistribution with attribution to the original data sources is encouraged. TraitBank complements taxon or subject-specific knowledge management systems by filling gaps (both in taxonomic and trait space), by recruiting new types of data (e.g., from text-mining, citizen-science, and specimen data digitization efforts) and by integrating knowledge across the entire tree of life and multiple scientific domains. The emerging semantic framework will facilitate data discovery, support queries across data sets, and advance data integration and exchange among projects, thus making more biodiversity data available for use in scientific and policy-oriented applications.

}, author = {Katja Schulz and Jennifer Hammock} } @conference {362, title = {W12-01: Some Challenges in Working with Biodiversity Ontologies}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, address = {Corvallis, OR}, abstract = {

We have faced a number of ontology-related challenges in developing our botanical knowledge portal, which uses Semantic Mediawiki (SMW) to store and display structured data extracted from the Flora of North America (FNA), and to integrate this data with an open biodiversity knowledge graph. We will describe some of the challenges that we have overcome, and some that we continue to struggle with. These include issues with representing and integrating data about phenotypes, habitats, phenology, and establishment means (native vs. introduced). We will also describe the structure of our biodiversity knowledge graph, and invite collaboration in its continued construction.

}, author = {Joel Sachs and Hong Cui and James Macklin} } @conference {W05-06, title = {W05-06: Hierarchical Sentiment Analysis on HPV Vaccines Related Tweets Using Machine Learning Based Approaches}, 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 order to figure out the reasons behind the low HPV vaccine coverage and come up with corresponding strategies to improve vaccine uptake, understand public opinions on HPV vaccines would be of great help. As a precious and rich data source to analyze public opinions, Twitter is now attracting more attention from medical informatics researchers. In order to use machine learning based methods to automatically track and analyze rapidly-growing HPV vaccine related public opinions on Twitter, we first collected and manually annotated 6,000 related tweets as a gold standard. In order to model the possible sentiments especially the negative sentiments over HPV vaccine on Twitter, a preliminary ontology for hierarchical sentiment classification was built as the annotation scheme. A Kappa annotation agreement at 0.851 was reached. Different features (word n-grams, POS and word clusters) were extracted. Experiments were conducted to test the performance of the different combinations features sets on different levels of classification tasks. Macro F scores at 0.8043 and 0.7552 were reached for top-level classification and finest level classification respectively. The limitations and challenges were also discussed. Our results and analysis indicate that it is feasible to do hierarchical classification tasks on HPV vaccine related tweets using machine learning approaches.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Jingcheng Du and Jun Xu and Hsingyi Song and Xiangyu Liu and Cui Tao} } @conference {W05-04, title = {W05-04: LAERTES: An open scalable architecture for linking pharmacovigilance evidence sources with clinical data}, 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 = {Integrating multiple sources of pharmacovigilance evidence has the potential to advance the science of safety signal detection and evaluation. Consistent with these results, there has been a recent call for more research on how to integrate multiple disparate evidence sources while making the evidence computable from a knowledge representation perspective (i.e., semantic enrichment). Existing frameworks integrating various sources provide some of the input needed for combinatorial signal detection. However, none have been specifically designed to support both regulatory and clinical use cases, nor developed using an open architecture allowing interested scientists to easily add new sources. This paper discusses the architecture and functionality of a system called Large-scale Adverse Effects Related to Treatment Evidence Standardization (LAERTES). LAERTES provides an open and scalable architecture for linking evidence sources relevant to investigating the association of drugs with health outcomes of interest (HOIs). Standard terminologies/ontologies are used to represent different entities. For example, drugs and HOIs are represented respectively using RxNorm and SNOMED-CT. At the time of this writing, six evidence sources have been loaded into LAERTES. Also, a prototype evidence exploration user interface and set of Web API services are available. This system operates within a larger software environment provided by the OHDSI clinical research framework.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Richard D. Boyce and Erica A. Voss and Vojtech Huser and Lee Evans and Christian Reich and Jon D. Duke and Nicholas P. Tatonetti and Michel Dumontier and Manfred Hauben and Magnus Wallberg and Lili Peng and Sara Dempster and Yongqun He and Anthony G. Sena and Patrick B. Ryan} } @conference {W04-01, title = {W04-01: Ontology of the Organigram}, 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 = {Basic Formal Ontology (BFO) is a domain-neutral top-level ontology designed to serve as the starting point for development of domain ontologies designed to support the consistent annotation not only of scientific research data but also of data arising through clinical practice, hospital administration, and regulatory oversight. In each of these areas data are generated relating to what are called deontic entities {\textendash} obligations, duties, contracts, permissions, consents, licenses, and so forth. I will sketch how we can understand entities of these sorts within the BFO framework.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {B. Smith} } @conference {IT702, title = {IT702: To MIREOT or not to MIREOT? A case study of the impact of using MIREOT in the Experimental Factor Ontology (EFO)}, 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 = {

MIREOT is a mechanism for the selective re-use of individual ontology classes in other ontologies. Designed to minimise effort and to support orthogonality, it is now in widespread use. The consequences for ontology integrity and automated reasoning of using the MIREOT mechanism have so far not been fully assessed. In this paper, we perform an analysis of the Experimental Factor Ontology (EFO), an ontology which uses the MIREOT process to gather classes from a large range of other ontologies. Our study examines the effect of combining EFO with the ontologies it references by actually importing them into the EFO. We then evaluate the consistency and status of the combined ontologies. Through our investigation, we reveal that EFO in combination with all its referenced ontologies is logically inconsistent. Furthermore, when EFO is individually combined with many of the ontologies it references, we find a large number of unsatisfiable classes. These results demonstrate a potential problem within a major ontological ecosystem, and reveals possible disadvantages to the use of the MIREOT system for developing ontologies.

}, url = {http://ceur-ws.org/Vol-1747/IT702_ICBO2016.pdf}, author = {Luke Slater and Georgios Gkoutos and Paul Schofield and Robert Hoehndorf} } @conference {IT605, title = {IT605: SEPIO: A Semantic Model for the Integration and Analysis of Scientific Evidence}, 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 Scientific Evidence and Provenance Information Ontology (SEPIO) was developed to support the description of evidence and provenance information for scientific claims. The core model represents the relationships between claims, their lines of evidence, and the data items that comprise this evidence, as well as the methods, tools, and agents involved in the creation of these artifacts. SEPIO was initially developed to support the data integration and analysis efforts of the Monarch Initiative, where it provides a unified and computable representation of evidence and provenance metadata for genotype-phenotype associations aggregated across diverse model organism and clinical genetics databases. However, additional requirements were collected from diverse community partners in an effort to provide a shared community standard, with a core model that is domain independent and extensible to represent any type of claim and its associated evidence. In this report we describe the structure and principles behind the SEPIO model, and review its applications in support of data integration, curation, knowledge discovery, and manual and computational evaluation of scientific claims. The SEPIO ontology can be found at http://github.com/monarch-initiative/SEPIO-ontology/blob/master/src/ontology/sepio.owl.

}, url = {http://ceur-ws.org/Vol-1747/IT605_ICBO2016.pdf}, author = {Matthew Brush and Kent Shefchek and Melissa Haendel} } @conference {IT506, title = {IT506: An Ontological Framework for Representing Topological Information in Human Anatomy}, 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 = {

Medical ontologies have been a focus of constant attention in recent years as one of the fundamental techniques and knowledge bases for clinical decision support applications. In this paper, we discuss the description framework of our anatomy ontology with a focus on representing topological information, which is required for anatomical reasoning in clinical decision support applications. Our framework has major advantages over preceding studies with respect to: (1) representations of branching sequence; (2) combined representation of relevant knowledge with the use of {\`O}general structural component{\'O}; and (3) cooperation with the disease and abnormality ontologies.

}, url = {http://ceur-ws.org/Vol-1747/IT506_ICBO2016.pdf}, author = {Takeshi Imai and Emiko Shinohara and Masayuki Kajino and Ryota Sakurai and Kazuhiko Ohe and Kouji Kozaki and Riichiro Mizoguchi} } @conference {IT505, title = {IT505: Towards a Standard Ontology Metadata Model}, 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 = {

Bio-ontologies are becoming increasingly important in semantic alignment for data integration, information exchange, and semantic interoperability. Due to the large number of emerging bio-ontologies, it is challenging for ontology for their applications. Therefore, it is important to have a consistent terminology metadata model and a resource for discovering appropriate ontologies or other resource for use in annotating data. This paper aims to seek a common, shareable, and comprehensive method to create, disseminate, and consume metadata about terminology resources.

}, url = {http://ceur-ws.org/Vol-1747/IT505_ICBO2016.pdf}, author = {Hua Min and Stuart Turner and Sherri de Coronado and Brian Davis and Trish Whetzel and Robert R. Freimuth and Harold R. Solbrig and Richard Kiefer and Michael Riben and Grace A. Stafford and Lawrence Wright and Riki Ohira} } @conference {IT504, title = {IT504: OOSTT: a Resource for Analyzing the Organizational Structures of Trauma Centers and Trauma Systems}, 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 = {

Organizational structures of healthcare organiza-tions has increasingly become a focus of medical research. In the CAF{\'E} project we aim to provide a web-service enabling ontol-ogy-driven comparison of the organizational characteristics of trauma centers and trauma systems. Trauma remains one of the biggest challenges to healthcare systems worldwide. Research has demonstrated that coordinated efforts like trauma systems and trauma centers are key components of addressing this chal-lenge. Evaluation and comparison of these organizations is es-sential. However, this research challenge is frequently com-pounded by the lack of a shared terminology and the lack of ef-fective information technology solutions for assessing and com-paring these organizations. In this paper we present the Ontol-ogy of Organizational Structures of Trauma systems and Trauma centers (OOSTT) that provides the ontological founda-tion to CAF{\'E}{\textquoteright}s web-based questionnaire infrastructure. We present the usage of the ontology in relation to the questionnaire and provide the methods that were used to create the ontology.

}, url = {http://ceur-ws.org/Vol-1747/IT504_ICBO2016.pdf}, author = {Joseph Utecht and John Judkins and Terra Colvin Jr. and J. Neil Otte and Nicholas Rogers and Robert Rose and Maria Alvi and Amanda Hicks and Jane Ball and Stephen M. Bowman and Robert T. Maxson and Rosemary Nabaweesi and Rohit Pradhan and Nels D. Sanddal and M. Eduard Tudoreanu and Robert Winchell and Mathias Brochhausen} } @conference {IT503, title = {IT503: Malaria study data integration and information retrieval based on OBO Foundry 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 = {

The International Centers of Excellence in Malaria Research (ICEMR) projects involve studies to understand the epidemiology and transmission patterns of malaria in different geographic regions. Two major challenges of integrating data across these projects are: (1) standardization of highly heterogeneous epidemiologic data collected by various ICEMR projects; (2) provision of user-friendly search strategies to identify and retrieve information of interest from the very complex ICEMR data. We pursued an ontology-based strategy to address these challenges. We utilized and contributed to the Open Biological and Biomedical Ontologies to generate a consistent semantic representation of three different ICEMR data dictionaries that included ontology term mappings to data fields and allowed values. This semantic representation of ICEMR data served to guide data loading into a relational database and presentation of the data on web pages in the form of search filters that reveal relationships specified in the ontology and the structure of the underlying data. This effort resulted in the ability to use a common logic for storing and display of data on study participants, their clinical visits, and epidemiological information on their living conditions (dwelling) and geographic location. Users of the Plasmodium Genomics Resource, PlasmoDB, accessing the ICEMR data will be able to search for participants based on environmental factors such as type of dwelling, location or mosquito biting rate, characteristics such as age at enrollment, relevant genotypes or gender and visit data such as laboratory findings, diagnoses, malaria medications, symptoms, and other factors.

}, url = {http://ceur-ws.org/Vol-1747/IT503_ICBO2016.pdf}, author = {Jie Zheng and Jashon Cade and Brian Brunk and David Roos and Chris Stoeckert and San James and Emmanuel Arinaitwe and Bryan Greenhouse and Grant Dorsey and Steven Sullivan and Jane Carlton and Gabriel Carrasco-Escobar and Dionicia Gamboa and Paula Maguina-Mercedes and Joseph Vinetz} } @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 {IT405, title = {IT405: Building Concordant Ontologies for Drug Discovery}, 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 this study we demonstrate how we interconnect three different ontologies, the BioAssay Ontology (BAO), LINCS Information FramEwork ontology (LIFEo), and the Drug Target Ontology (DTO). The three ontologies are built and maintained for three different projects: BAO for the BioAssay Ontology Project, LIFEo for the Library of Integrated Network-Based Cellular Signatures (LINCS) project, and DTO for the Illuminating the Druggable Genome (IDG) project. DTO is a new ontology that aims to formally describe drug target knowledge relevant to drug discovery. LIFEo is an application ontology to describe information in the LIFE software system. BAO is a highly accessed NCBO ontology; it has been extended formally to describe several LINCS assays. The three ontologies use the same principle architecture that allows for re-use and easy integration of ontology modules and instance data. Using the formal definitions in DTO, LIFEo, and BAO and data from various resources one can quickly identify disease-relevant and tissue- specific genes, proteins, and prospective small molecules. We show a simple use case example demonstrating knowledge-based linking of life science data with the potential to empower drug discovery.

}, url = {http://ceur-ws.org/Vol-1747/IT405_ICBO2016.pdf}, author = {Hande K{\"u}{\c c}{\"u}k-Mcginty and Saurabh Metha and Yu Lin and Nooshin Nabizadeh and Vasileios Stathias and Dusica Vidovic and Amar Koleti and Christopher Mader and Jianbin Duan and Ubbo Visser and Stephan Schurer} } @conference {IT403, title = {IT403: OntONeo: The Obstetric and Neonatal Ontology}, 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 = {

This paper presents the Obstetric and Neonatal Ontology (OntONeo). This ontology has been created to provide a consensus representation of salient electronic health record (EHR) data and to serve interoperability of the associated data and information systems. More generally, it will serve interoperability of clinical and translational data, for example deriving from genomics disciplines and from clinical trials. Interoperability of EHR data is important to ensuring continuity of care during the prenatal and postnatal periods for both mother and child. As a strategy to advance such interoperability we use an approach based on ontological realism and on the ontology development principles of the Open Biomedical Ontologies Foundry, including reuse of reference ontologies wherever possible. We describe the structure and coverage domain of OntONeo and the process of creating and maintaining the ontology.

}, url = {http://ceur-ws.org/Vol-1747/IT403_ICBO2016.pdf}, author = {Fernanda Farinelli and Mauricio Almeida and Peter Elkin and Barry Smith} } @conference {IT206, title = {IT206: The UNEP Ontologies and the OBO Foundry}, 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://ceur-ws.org/Vol-1747/IT206_ICBO2016.pdf}, author = {Barry Smith and Mark Jensen} } @conference {373, title = {IP34: Plant Reactome: A Resource for Comparative Plant Pathway Analysis}, booktitle = {ICBO and BioCreative 2016}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, address = {Corvallis, OR}, abstract = {

The Plant Reactome database (http://plantreactome.gramene.org/) hosts metabolic, genetic and signaling pathways for several model and crop plant species. The Reactome data model organizes gene products, small molecules and macromolecular interactions into reactions and pathways in the context of their subcellular location to build a systemslevel framework of a plant cell. The Plant Reactome features Oryza sativa (rice) as a reference species, built by importing the RiceCyc metabolic network and curating new metabolic, signaling and genetic pathways. The Plant Reactome database now contains 241 rice reference pathways and orthology-based pathway projections for 58 plant species. Plant Reactome allows users to i) compare pathways across various plant species; ii) query and visualize curated baseline and differential expression data available in the EMBL-EBI{\textquoteright}s Expression Atlas in the context of pathways in the Plant Reactome; and iii) analyze genome-scale expression data and conduct pathway enrichment analysis to enable researchers to identify pathways affected by the stresses or treatments studied in their data sets. Plant Reactome links out to numerous external reference resources, including the gene pages of Gramene, Phytozome, SoyBase, Legume Information System, PeanutBase, Uniprot, as well as ChEBI for small molecules, PubMed for literature supported evidences, and GO for molecular function and biological processes. Users can access/download our data in various formats from our website and via APIs. The presentation will discuss tools for pathway enrichment analysis and homologue pathway comparison, development of the Plant Reactome portal, curation of reference rice pathways, and phylogeny-based analyses of projected pathway annotations. The project is supported by the Gramene database award (NSF IOS-1127112)and the Human Reactome award (NIH: P41 HG003751, ENFIN LSHG-CT-2005-518254, Ontario Research Fund, and EBI Industry Programme).

}, author = {Sushma Naithani and Justin Preece and Parul Gupta and Peter D{\textquoteright}Eustachio and Justin Elser and Antonio Mundao and Joel Weiser and Sheldon McKay and Lincoln Stein and Doreen Ware and Pankaj Jaiswal} } @conference {IP28, title = {IP28: uc_FIDO: unambiguous characterization of food interactions with drugs ontology}, 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 = {

uc_FIDO is an ontology that unambiguously characterizes food interactions with drugs in the human body. This ontology is part of a group of food ontologies describing food and the human experience at the International Center for Food Ontology Operability, Data and Semantics (IC-FOODS) at UC Davis. The first of its kind, uc_FIDO characterizes relations between food, medicine, and human health. uc_FIDO brings together several existing ontologies related to anatomy, metabolic pathways, biological processes, drug ingredients and food structures. Through these ontologies, uc_FIDO annotates relationships between food and drug bioactives, human physiological conditions, and biological reaction pathways. Relationships that link together fully characterize various food interactions with drugs and their effects. The current dearth of ontologies for characterizing foods limits advancement of informatics solutions for improving health. As ontologies of foods are developed, it becomes necessary to describe ingredients, bioactive molecules, potential toxins, and other molecules in food interacting with drugs and the human body.

}, url = {http://ceur-ws.org/Vol-1747/IP28_ICBO2016.pdf}, author = {Constantine Spyrou and Matthew Lange} } @conference {IP27, title = {IP27: Dealing with elements of medical encounters: an approach based on ontological realism}, 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 = {

Electronic health records (EHRs) serve as repositories of documented data collected in a health care encounter. An EHR records information about who receives, who provides the health care and about the place where the encounter happens. We also observe additional elements relating to social relations in which the healthcare consumer is involved. To provide a consensus representation of common data and to enhance interoperability between different EHR repositories we have created a solution grounded in formal ontology. Here, we present how an ontology for the obstetric and neonatal domain deals with these general elements documented in health care encounters. Our goal is to promote the interoperability of information among EHRs created in different specialties. To develop our ontology, we used two main approaches: one based on ontological realism, the other based on the principles of the OBO Foundry, including reuse of reference ontologies.

}, url = {http://ceur-ws.org/Vol-1747/IP27_ICBO2016.pdf}, author = {Fernanda Farinelli and Mauricio Almeida and Peter Elkin and Barry Smith} } @conference {IP26, title = {IP26: Performance Evaluation Clinical Task Ontology(PECTO)}, 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 = {

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}, url = {http://ceur-ws.org/Vol-1747/IP26_ICBO2016.pdf}, author = {Jose F Florez-Arango and Santiago Pati{\~n}o-Giraldo and Jack W Smith and Sriram Iyengar} } @conference {IP19, title = {IP19: Opportunities and challenges presented by Wikidata in the context of biocuration}, 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 = {

Wikidata is a world readable and writable knowledge base maintained by the Wikimedia Foundation. It offers the opportunity to collaboratively construct a fully open access knowledge graph spanning biology, medicine, and all other domains of knowledge. To meet this potential, social and technical challenges must be overcome most of which are familiar to the biocuration community. These include community ontology building, high precision information extraction, provenance, and license management. By working together with Wikidata now, we can help shape it into a trustworthy, unencumbered central node in the Semantic Web of biomedical data.

}, url = {http://ceur-ws.org/Vol-1747/BT105_ICBO2016.pdf}, author = {Benjamin Good and Timothy Putman and Andrew Su and Andra Waagmeester and Sebastian Burgstaller-Muehlbacher and Elvira Mitraka} } @conference {IP18, title = {IP18: The ImmPort Antibody Ontology}, 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 = {

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}, url = {http://ceur-ws.org/Vol-1747/IP18_ICBO2016.pdf}, author = {William Duncan and Travis Allen and Jonathan Bona and Olivia Helfer and Barry Smith and Alan Ruttenberg and Alexander D. Diehl} } @conference {IP16, title = {IP16: Definition Coverage in the OBO Foundry Ontologies: The Big Picture}, 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 = {

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}, url = {http://ceur-ws.org/Vol-1747/IP16_ICBO2016.pdf}, author = {Daniel Schlegel and Selja Sepp{\"a}l{\"a} and Peter Elkin} } @conference {IP14, title = {IP14: Towards designing an ontology encompassing the environment-agriculture-food-diet-health knowledge spectrum for food system sustainability and resilience.}, 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 = {

Feeding 9 billion people is not solely a matter of food, health, nutrition, and the environment. Promoting human health by increasing the sustainability and resilience of food systems requires integrating information from a broad range of disciplines from human nutrition/health systems and agricultural/natural systems to social, financial, physical and political systems. Ontologies serve to specify common terminologies for critical concepts and relationships within these systems, however very few ontologies have been developed with this interdisciplinary focus. Biological ontologies, whether focused on human physiology, soil quality, or nutritional value are only part of the story when it comes to determining linkages throughout the food system that help determine human health and well-being. We seek to build an ontology of food and food systems that encompasses the relevant sustainability issues in their entirety. We have already built an ontology of sustainable sourcing of agricultural raw materials issues and indicators, but aim to expand our ontology to include attributes of resilience, and other issues along the environment-agriculture-food-diet-health knowledge spectrum. Additionally, we aim to create this ontology with the intention of quick usability for the food system decision-maker.

}, url = {http://ceur-ws.org/Vol-1747/IP14_ICBO2016.pdf}, author = {Ruthie Musker and Matthew Lange and Allan Hollander and Patrick Huber and Nathaniel Springer and Courtney Riggle and James Quinn and Thomas Tomich} } @conference {IP10, title = {IP10: Analysis of SNOMED {\textquoteleft}bleeding{\textquoteright} concepts \& terms}, 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 = {

We present an analysis of SNOMED CT {\^O}bleeding{\~O} concepts {\DH} those concepts with descriptions that include {\^O}hematoma{\~O}, {\^O}hemorrhage{\~O}, or {\^O}bleeding{\~O}; or that are descended from {\^O}Bleeding (finding){\~O} in the Is-a hierarchy; or that have Hematomas or Hemorrhages as their associated morphology {\DH} to assess how consistently they are used in the ontology.

}, url = {http://ceur-ws.org/Vol-1747/IP10_ICBO2016.pdf}, author = {Jonathan Bona and Selja Sepp{\"a}l{\"a} and Werner Ceusters} } @conference {IP07, title = {IP07: The Cell Line Ontology integration and analysis of the knowledge of LINCS cell lines}, 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 = {

Cell lines are crucial to study molecular signatures and pathways, and are widely used in the NIH Common Fund LINCS project. The Cell Line Ontology (CLO) is a community-based ontology representing and classifying cell lines from different resources. To better serve the LINCS research community, from the LINCS Data Portal and ChEMBL, we identified 1,097 LINCS cell lines, among which 717 cell lines were associated with 121 cancer types, and 352 cell line terms did not exist in CLO. To harmonize LINCS cell line representation and CLO, CLO design patterns were slightly updated to add new information of the LINCS cell lines including different database cross-reference IDs. A new shortcut relation was generated to directly link a cell line to the disease of the patient from whom the cell line was originated. After new LINCS cell lines and related information were added to CLO, a CLO subset/view (LINCS-CLOview) of LINCS cell lines was generated and analyzed to identify scientific insights into these LINCS cell lines. This study provides a first time use case on how CLO can be updated and applied to support cell line research from a specific research community or project initiative.

}, url = {http://ceur-ws.org/Vol-1747/IP07_ICBO2016.pdf}, author = {Edison Ong and Jiangan Xie and Zhaohui Ni and Qingping Liu and Yu Lin and Vasileios Stathias and Caty Chung and Stephan Schurer and Yongqun He} } @conference {IP02, title = {IP02: Adding evidence type representation to DIDEO}, 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 this poster we present novel development and extension of the Drug-drug Interaction and Drug-drug Interaction Evidence Ontology (DIDEO). We demonstrate how reasoning over this extension of DIDEO can a) automatically create a multi-level hierarchy of evidence types from descriptions of the underlying scientific observations and b) automatically subsume individual evidence items under the correct evidence type. Thus DIDEO will enable evidence items added manually by curators to be automatically categorized into a drug-drug interaction framework with precision and minimal effort from curators. As with all previous DIDEO development this extension is consistent with OBO Foundry principles.

}, url = {http://ceur-ws.org/Vol-1747/IP02_ICBO2016.pdf}, author = {Mathias Brochhausen and Philip E. Empey and Jodi Schneider and William R. Hogan and Richard D. Boyce} } @conference {D202, title = {D201: Ontobull and BFOConvert: Web-based programs to support automatic ontology conversion}, 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 = {

When a widely reused ontology appears in a new version which is not compatible with older versions, the ontologies reusing it need to be updated accordingly. Ontobull (http://ontobull.hegroup.org) has been developed to automatically update ontologies with new term IRI(s) and associated metadata to take account of such version changes. To use the Ontobull web interface a user is required to (i) upload one or more ontology OWL source files; (ii) input an ontology term IRI mapping; and (where needed) (iii) provide update settings for ontology headers and XML namespace IDs. Using this information, the backend Ontobull Java program automatically updates the OWL ontology files with desired term IRIs and ontology metadata. The Ontobull subprogram BFOConvert supports the conversion of an ontology that imports a previous version of BFO. A use case is pro- vided to demonstrate the features of Ontobull and BFOConvert.

}, url = {http://ceur-ws.org/Vol-1747/D201_ICBO2016.pdf}, author = {Edison Ong and Zuoshuang Xiang and Jie Zheng and Barry Smith and Yongqun He} } @conference {D205, title = {D104: Updates to the AberOWL ontology repository}, 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 = {

A large number of ontologies have been developed in the biological and biomedical domains, which are mostly expressed in the Web Ontology Language (OWL). These ontologies form a logical foundation for our knowledge in these domains, and they are in widespread use to annotate biomedical and biological datasets. The use of the semantics provided by ontologies requires the use of automated reasoning {\textendash} inferring new knowledge by evaluating the asserted axioms. AberOWL is an ontology repository which utilises an OWL 2 EL reasoner to provide semantic access to classified ontologies. Since our original presentation of the AberOWL framework, we have developed several additional tools and features which enrich its ability to integrate and explore data, make use of the semantic and inferred content of ontologies. Here we present an overview of AberOWL and the enhancements and new features which have been developed since its conception. AberOWL is freely available at http://aber-owl.net.

}, url = {http://ceur-ws.org/Vol-1747/D104_ICBO2016.pdf}, author = {M{\'A} Rodr{\'\i}guez-Garc{\'\i}a and Luke Slater and Imane Boudellioua and Paul Schofield and Georgios Gkoutos and Robert Hoehndorf} } @conference {BT104, title = {BT105: Opportunities and challenges presented by Wikidata in the context of biocuration}, 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 = {

Wikidata is a world readable and writable knowledge base maintained by the Wikimedia Foundation. It offers the opportunity to collaboratively construct a fully open access knowledge graph spanning biology, medicine, and all other domains of knowledge. To meet this potential, social and technical challenges must be overcome most of which are familiar to the biocuration community. These include community ontology building, high precision information extraction, provenance, and license management. By working together with Wikidata now, we can help shape it into a trustworthy, unencumbered central node in the Semantic Web of biomedical data.

}, url = {http://ceur-ws.org/Vol-1747/BT105_ICBO2016.pdf}, author = {Benjamin Good and Sebastian Burgstaller-Muehlbacher and Elvira Mitraka and Timothy Putman and Andrew Su and Andra Waagmeester} } @conference {BT102, title = {BT103: Collaborative Workspaces for Pathway Curation}, 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 = {

We present a web based visual biocuration workspace, focusing on curating detailed mechanistic pathways. It was designed as a flexible platform where multiple humans, NLP and AI agents can collaborate in real-time on a common model using an event driven API. We will use this platform for exploring disruptive technologies that can scale up biocuration such as NLP, human-computer collaboration, crowd-sourcing, alternative publishing and gamification. As a first step, we are designing a pilot to include an author-curation step into the scientific publishing, where the authors of an article create formal pathway fragments representing their discovery- heavily assisted by computer agents. We envision that this {\textquotedblleft}micro-curation{\textquotedblright} use-case will create an excellent opportunity to integrate multiple NLP approaches and semi-automated curation.

}, url = {http://ceur-ws.org/Vol-1747/BT103_ICBO2016.pdf}, author = {Funda Durupinar-Babur and MC Siper and Ugur Dogrusoz and Istemi Bahceci and Ozgun Babur and Emek Demir} } @conference {BP02, title = {BP02: Disease Named Entity Recognition Using NCBI Corpus}, 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 = {

Named Entity Recognition (NER) in biomedical literature is a very active research area. NER is a crucial component of biomedical text mining because it allows for information retrieval, reasoning and knowledge discovery. Much research has been carried out in this area using semantic type categories, such as fiDNAfl, fiRNAfl, fiproteinsfl and figenesfl. However, disease NER has not received its needed attention yet, specifically human disease NER. Traditional machine learning approaches lack the precision for disease NER, due to their dependence on token level features, sentence level features and the integration of features, such as orthographic, contextual and linguistic features. In this paper a method for disease NER is proposed which utilizes sentence and token level features based on Conditional Random Fields using the NCBI disease corpus. Our system utilizes rich features including orthographic, contextual, affixes, bigrams, part of speech and stem based features. Using these feature sets our approach has achieved a maximum F-score of 94\% for the training set by applying 10 fold cross validation for semantic labeling of the NCBI disease corpus. For testing and development corpus the model has achieved an F-score of 88\% and 85\% respectively.

}, url = {http://ceur-ws.org/Vol-1747/BP02_ICBO2016.pdf}, author = {Thomas Hahn and Hidayat Ur Rahman and Richard Segall} }