@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 {W14-02, title = {W12-03: The Biological Collections Ontology for linking traditional and contemporary biodiversity 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 = {

Biodiversity data comes from many sources, ranging from museum specimens to field surveys to genomic sequences. Domain specific standards provide vocabularies for many types of these data, but they do not fully support integrating data across methods, scales, and domains. The Biological Collections Ontology (BCO) was designed to bridge the terminology gap between traditional museum-based specimen collections and more contemporary environmental sampling methods, such as metagenomic sequencing, by providing a logically defined set of terms for biodiversity that map to standards such as the Darwin Core and Minimum Information for any Sequence. The BCO is expanding to encompass observational biodiversity data such as field surveys and taxonomic inventories. A key design principle of the BCO is to clearly distinguish the different types of processes involved in biodiversity data collection along with the inputs and outputs of those processes. The BCO has applications to plant biodiversity studies for linking herbarium specimens to sequence data, connecting trait data to specimens, and describing survey data.

}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Ramona Walls and Rob Guralnick} } @conference {W11-06, title = {W11-06: FoodON Use cases: Caution! Food Allergies Ahead}, 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 = {Millions of people worldwide live with food allergies, including all those at risk for life-threatening anaphylaxis. The lack of a standardized food vocabulary impacts food source risk assessment, food hazard control, consistent food allergy policy implementation and food-allergy research. The Canadian Healthy Infant Longitudinal Study (CHILD) examines causal factors of asthma and allergy during childhood development. The development of FoodON will benefit food allergy research by standardizing food descriptors across child cohorts, enable the correlation of food antigens with biological causation of immune response, and streamline guidelines for parents.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Emma Griffiths} } @conference {W11-04, title = {W11-04: Food composition: Understanding what gives food its colour, taste and aroma}, 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 = {Data on the composition of foods are essential for a diversity of purposes. It provides detailed sets of information on the nutritionally important components such as proteins, carbohydrates, vitamins and minerals. In this talk, we will describe our approach to employ ontologies to identify food components in recipes described in natural language. By combining several public data sources, we link the food components to chemical compounds and their physiological and pathological effects.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Miguel Garcia} } @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 {IT604, title = {IT604: Qualitative causal analyses of biosimulation models}, 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 describe an approach for performing qualita-tive, systems-level causal analyses on biosimulation models that leverages semantics-based modeling formats, formal ontology, and automated inference. The approach allows users to quickly investigate how a qualitative perturbation to an element within a model{\~O}s network (an increment or decrement) propagates throughout the modeled system. To support such analyses, we must interpret and annotate the semantics of the models, includ-ing both the physical properties modeled and the dependencies that relate them. We build from prior work understanding the semantics of biological properties, but here, we focus on the se-mantics for dependencies, which provide the critical knowledge necessary for causal analysis of biosimulation models. We de-scribe augmentations to the Ontology of Physics for Biology, via OWL axioms and SWRL rules, and demonstrate that a reasoner can then infer how an annotated model{\~O}s physical properties influence each other in a qualitative sense. Our goal is to provide researchers with a tool that helps bring the systems-level network dynamics of biosimulation models into perspective, thus facilitat-ing model development, testing, and application.

}, url = {http://ceur-ws.org/Vol-1747/IT604_ICBO2016.pdf}, author = {Maxwell Neal and John Gennari and Daniel Cook} } @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 {IT401, title = {IT401: An analysis of differences in biological pathway resources}, 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 = {

Integratingcontentfrommultiplebiologicalpathwayresourcesisnecessarytofullyexploitpathwayknowledgefortheofbiologyandmedicine.Dif-ferencesincontent,representation,coverage,andmoreoccurbetweendatabases,andarechallengestoresourcemerging.Weintroduceatypologyofrepresentationaldifferencesbetweenpathwayresourcesandgiveexam-plesusingseveraldatabases:BioCyc,KEGG,PANTHERpathways,andReactome.WealsodetectandquantifyannotationmismatchesbetweenHumanCycandReactome.Thetypologyofmismatchescanbeusedtoguideentityandrelationshipalignmentbetweenthesedatabases,helpingusidentifyandunderstandinourknowledge,andallowingtheresearchcommunitytoderivegreaterfromtheexistingpathwaydata.

}, url = {http://ceur-ws.org/Vol-1747/IT401_ICBO2016.pdf}, author = {Lucy Wang and John Gennari and Neil Abernethy} } @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 {IP32, title = {IP32: Enhancing SciENcv through semantic research profile integration with the VIVO-ISF 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/IP32_ICBO2016.pdf}, author = {Marijane White and Matthew Brush and Shahim Essaid and Robin Champieux and Adrienne Zell and Melissa Haendel and Colin Grove and Syeda Momina Tabish and David Eichmann} } @conference {IP30, title = {IP30: Supporting database annotations and beyond with the Evidence \& Conclusion Ontology (ECO)}, 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 Evidence \& Conclusion Ontology (ECO) is a community standard for summarizing evidence in scientific research in a controlled, structured way. Annotations at the world{\textquoteright}s most frequented biological databases (e.g. model organisms, UniProt, Gene Ontology) are supported using ECO terms. ECO describes evidence derived from experimental and computational methods, author statements curated from the literature, inferences drawn by curators, and other types of evidence. Here, we describe recent ECO developments and collaborations, most notably: (i) a new ECO website containing user documentation, up-to-date news, and visualization tools; (ii) improvements to the ontology structure; (iii) implementing logic via an ongoing collaboration with the Ontology for Biomedical Investigations (OBI); (iv) addition of numerous experimental evidence types; and (v) addition of new evidence classes describing computationally derived evidence. Due to its utility, popularity, and simplicity, ECO is now expanding into realms beyond the protein annotation community, for example the biodiversity and phenotype communities. As ECO continues to grow as a resource, we are seeking new users and new use cases, with the hope that ECO will continue to be a broadly used and easy-to-implement community standard for representing evidence in diverse biological applications. Feel free to visit two ECO-sponsored workshops at ICBO 2016 to learn more: 1. {\`O}An introduction to the Evidence and Conclusion Ontology and representing evidence in scientific research{\'O} and 2. {\`O}OBI-ECO Interactions \& Evidence{\'O}.

}, url = {http://ceur-ws.org/Vol-1747/IP30_ICBO2016.pdf}, author = {Marcus Chibucos and Suvarna Nadendla and James Munro and Elvira Mitraka and Dustin Olley and Nicole Vasilevsky and Matthew Brush and Michelle Giglio} } @conference {IP24, title = {IP24: An OBI ontology Datum Proof Sheet}, 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 = {

There are numerous past and current examples of ontology-driven projects that provide auto-generated user interfaces for managing entities and relations, each presenting its own varied and complex data model. Our Datum Proof Sheet application aims to simplify the application development landscape by building community consensus about the way basic categorical, textual and numeric datum fields should be described within the OBOFoundry community of ontologies. The proof sheet shows selected datums (grouped under the context of an OBI {\`O}data representational model{\'O} item) as form inputs on an HTML page, enabling an application ontology{\~O}s contents to be presented to end users (ranging in our case from epidemiologists to software developers) for review without necessarily having a working application to showcase them in. The basic relations and cases necessary for presenting datums in a user interface are mostly satisfied by OBI{\~O}s design, but we introduce a few extra elements to bring more clarity to datum specifications, and to provide user interface term labels and definitions that may differ from those that ontologists prefer in the {\`O}backend{\'O}.

}, url = {http://ceur-ws.org/Vol-1747/IP24_ICBO2016.pdf}, author = {Damion Dooley and Emma Griffiths and Fiona Brinkman and William Hsiao} } @conference {IP22, title = {IP22: SourceData: Making Data discoverable}, 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/BT101_ICBO2016.pdf}, author = {Nancy George and Sara El-Gebali and Thomas Lemberger} } @conference {IP21, title = {IP21: FoodON: A Global Farm-to-Fork Food 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 = {

Several resources and standards for indexing food descriptors currently exist, but their content and interrelations are not semantically and logically coherent. Simultaneously, the need to represent knowledge about food is central to many fields including biomedicine and sustainable development. FoodON is a new ontology built to interoperate with the OBO Library and to represent entities which bear a . It encompasses materials in natural ecosystems and food webs as well as human-centric categorization and handling of food. The latter will be the initial focus of the ontology, and we aim to develop semantics for food safety, food security, the agricultural and animal husbandry practices linked to food production, culinary, nutritional and chemical ingredients and processes. The scope of FoodON is ambitious and will require input from multiple domains. FoodON will import or map to material in existing ontologies and standards and will create content to cover gaps in the representation of food-related products and processes. As a robust food ontology can only be created by consensus and wide adoption, we are currently forming an international consortium to build partnerships, solicit domain expertise, and gather use cases to guide the ontologys development. The products of this work are being applied to research and clinical datasets such as those associated with the Canadian Healthy Infant Longitudinal Development (CHILD) study which examines the causal factors of asthma and allergy development in children, and the Integrated Rapid Infectious Disease Analysis (IRIDA) platform for genomic epidemiology and foodborne outbreak investigation.

}, url = {http://ceur-ws.org/Vol-1747/IP21_ICBO2016.pdf}, author = {Emma Griffiths and Damion Dooley and Pier Luigi Buttigieg and Robert Hoehndorf and Fiona Brinkman and William Hsiao} } @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 {IP12, title = {IP12: NAPRALERT, from an historical information silo to a linked resource able to address the new challenges in Natural Products Chemistry and Pharmacognosy.}, 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 = {

NAPRALERT (https://www.napralert.org) is a database on natural products, including data on ethnobotany, chemistry, pharmacology, toxicology, and clinical trials from literature dating back to the 19th century. Established in 1975 by Norman R. Farnsworth, it became a web accessible resource in 2005 but soon became stagnant while literature grew exponentially. After a complete rewrite of the platform, the focus is now on connecting this resource to the rest of the existing databases and expanding its usability. The creation of a Pharmacognosy/Natural Product ontology will foster better understanding of this domain, its linking potential with other resources and the ability to automatize literature annotation and entry efficiently.

}, url = {http://ceur-ws.org/Vol-1747/IP12_ICBO2016.pdf}, author = {Jonathan Bisson and James McAlpine and James Graham and Guido Pauli} } @conference {IP09, title = {IP09: How to Summarize Big Knowledge Subjects}, 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 = {

One manifestation of the {\textquotedblleft}Big Knowledge{\textquoteright}{\textquoteright} challenge is providing automated tools for summarization of ontology content to facilitate user comprehension. An aggregation approach for the automatic identification and display of major subjects covered by an ontology{\textquoteright}s content is presented. The results show that our methodology is viable in capturing the {\textquotedblleft}big picture{\textquotedblright} of ontology content.

}, url = {http://ceur-ws.org/Vol-1747/IP09_ICBO2016.pdf}, author = {Ling Zheng and Yehoshua Perl and James Geller and Gai Elhanan} } @conference {IP06, title = {IP06: Building a molecular glyco-phenotype ontology to decipher undiagnosed diseases}, 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 = {

Hundreds of rare diseases are due to mutation on genes related to glycans synthesis, degradation or recognition. These glycan-related defects are well described in the literature but largely absent in ontologies and databases of chemical entities and phenotypes, limiting the application of computational methods and ontology-driven tools for characterization and discovery of glycan related diseases. We are curating articles and textbooks in glycobiology related to genetic diseases to inform the content and the structure of an ontology of Molecular Glyco-Phenotypes (MGPO). MGPO will be applied toward use cases including disease diagnosis and disease gene candidate prioritization, using semantic similarity and pattern matching at the glycan level with glycomics data from patient of the Undiagnosed Diseases Network.

}, url = {http://ceur-ws.org/Vol-1747/IP06_ICBO2016.pdf}, author = {Jean-Philippe Gourdine and Thomas Metz and David Koeller and Matthew Brush and Melissa Haendel} } @conference {D203, title = {D202: Reusing the NCBO BioPortal technology for agronomy to build AgroPortal}, 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 = {

Many vocabularies and ontologies are produced to represent and annotate agronomic data. By reusing the NCBO BioPortal technology, we have already designed and implemented an advanced prototype ontology repository for the agronomy domain. We plan to turn that prototype into a real service to the community. The AgroPortal project aims at reusing the scientific outcomes and experience of the biomedical domain in the context of plant, agronomic, food, environment (perhaps animal) sciences. We offer an ontology portal which features ontology hosting, search, versioning, visualization, comment, recommendation, enables semantic annotation, as well as storing and exploiting ontology alignments. All of these within a fully semantic web compliant infrastructure. The AgroPortal specifically pays attention to respect the requirements of the agronomic community in terms of ontology formats (e.g., SKOS, trait dictionaries) or supported features. In this paper, we present our prototype as well as preliminary outputs of four driving agronomic use cases. With the experience acquired in the biomedical domain and building atop of an already existing technology, we think that AgroPortal offers a robust and stable reference repository that will become highly valuable for the agronomic domain.

}, url = {http://ceur-ws.org/Vol-1747/D202_ICBO2016.pdf}, author = {Clement Jonquet and Anne Toulet and Elizabeth Arnaud and Sophie Aubin and ED Yeumo and Vincent Emonet and John Graybeal and Mark A. Musen and Cyril Pommier and Pierre Larmande} } @conference {D101-BP04, title = {D106-BP04: Enhancing Information Accessibility of Publications with Text Mining and 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 = {

We present an ongoing effort on utilizing text mining methods and existing biological ontologies to help readers to access the information contained in the scientific articles. Our approach includes using multiple strategies for biological entity detection and using association analysis on extracted analysis. The entity extraction processes utilizes regular expression rules, ontologies, and keyword dictionary to get a comprehensive list of biological entities. In addition to extract list of entities, we also apply natural language processing and association analysis techniques to generate inferences among entities and comparing to known relations documented in the existing ontologies.

}, url = {http://ceur-ws.org/Vol-1747/D106-BP04_ICBO2016.pdf}, author = {Weijia Xu and Amit Gupta and Pankaj Jaiswal and Crispin Taylor and Patti Lockhart} } @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 {BT202, title = {BT202: Social Media Mining for Pharmacovigilance}, 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 = {Graciela Gonzalez} } @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 {342, title = {BT101: SourceData: Making Data discoverable}, 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 = {

In molecular and cell biology, most of the data presented in published papers are not available in formats that allow for direct analysis and systematic mining. The goal of the SourceData project (http://sourcedata.embo.org) is to make published data easier to find, to connect papers containing related information and to promote the reuse and novel analysis of published data. The main concept underlying the project is that the structure of a dataset provides information about the design of the study in question and can be exploited in powerful data-oriented search strategies. SourceData has therefore developed tools to generate machine-readable descriptive metadata from figures in published manuscripts. Experimentally tested hypotheses are represented as directed relationships between standardized biological entities. Once processed, a comprehensive {\textquoteleft}scientific knowledge graph{\textquoteright} can be generated from this data (see demo video1 at https://vimeo.com/sourcedata/kg), making the body of data efficiently searchable. Importantly, this graph is objectively grounded in published data and not on the potentially subjective interpretation of the results.\ 

}, url = {http://ceur-ws.org/Vol-1747/BT101_ICBO2016.pdf}, author = {Nancy George and Sara El-Gebali and Thomas Lemberger} } @conference {BIT105, title = {BIT105: A Web Application for Extracting Key Domain Information for Scientific Publications using 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 = {

We present demos of an ongoing project, domain informational vocabulary extraction (DIVE), which aims to enrich digital publications through entity and key informational words detection and by adding additional annotations. The system implements multiple strategies for biological entity detection, including using regular expression rules, ontologies, and a keyword dictionary. These extracted entities are then stored in a database and made accessible through an interactive web application for curation and evaluation by authors. Through the web interface, the user can make additional annotations and corrections to the current results. The updates can then be used to improve the entity detection in subsequent processed articles. Although the system is being developed in the context of annotating journal articles, it can be also be beneficial to domain curators and researchers at large.

}, url = {http://ceur-ws.org/Vol-1747/BIT105_ICBO2016.pdf}, author = {Weijia Xu and Amit Gupta and Pankaj Jaiswal and Crispin Taylor and Patti Lockhart} }