@conference {D105, title = {D205: Easy Extraction of Terms and Definitions with OWL2TL}, 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 = {

"Facilitating good communication between semantic web specialists and domain experts is necessary to efficient ontology development. This development may be hindered by the fact that domain experts tend to be unfamiliar with tools used to create and edit OWL files. This is true in particular when changes to definitions need to be reviewed as often as multiple times a day. We developed ""OWL to Term List"" (OWL2TL) with the goal of allowing domain experts to view the terms and definitions of an OWL file organized in a list that is updated each time the OWL file is updated. The tool is available online and currently generates a list of terms, along with additional annotation properties that are chosen by the user, in a format that allows easy copying into a spreadsheet."

}, url = {http://ceur-ws.org/Vol-1747/D205_ICBO2016.pdf}, author = {John Judkins and Joseph Utecht and Mathias Brochhausen} } @conference {D204, title = {D203: Humane OWL: RDF and OWL for Humans}, 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 = {

Humane OWL (HOWL) is a syntax for RDF and OWL designed for manual editing. By allowing human-readable labels to be used in place of IRIs, and providing convenient syntax for OWL annotations and expressions, HOWL files can be used like source code with tools such as GitHub, then translated into any other RDF or OWL format for use with other tools.

}, url = {http://ceur-ws.org/Vol-1747/D203_ICBO2016.pdf}, author = {James A. Overton} } @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 {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 {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 {D103-W14-04, title = {D103-W12-05-IP36: The Phenoscape Knowledgebase: tools and APIs for computing across phenotypes from evolutionary diversity and model organisms}, 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 Phenoscape Knowledgebase (KB) is an ontologydriven database that combines existing phenotype annotations from model organism databases with new phenotype annotations from the evolutionary literature. Phenoscape curators have created phenotype annotations for more than 5,000 species and higher taxa, by defining computable phenotype concepts for more than 20,000 character states from over 160 published phylogenetic studies. These phenotype concepts are in the form of Entity{\textendash}Quality (EQ) [1] compositions which incorporate terms from the Uberon anatomy ontology, the Biospatial Ontology (BSPO), and the Phenotype and Trait Ontology (PATO). Taxonomic concepts are drawn from the Vertebrate Taxonomy Ontology (VTO). This knowledge of comparative biodiversity is linked to potentially relevant developmental genetic mechanisms by importing associations of genes to phenotypic effects and gene expression locations from zebrafish (ZFIN [2]), mouse (MGI [3]), Xenopus (Xenbase [4]), and human (Human Phenotype Ontology project [5]). Thus far, the Phenoscape KB has been used to identify candidate genes for evolutionary phenotypes [6], to match profiles of ancestral evolutionary variation with gene phenotype profiles [7], and to combine data across many evolutionary studies by inferring indirectly asserted values within synthetic supermatrices [8]. Here we describe the software architecture of the Phenoscape KB, including data ingestion, integration of OWL reasoning, web service interface, and application features (Fig. 1).

}, url = {http://ceur-ws.org/Vol-1747/D103-W14-04_ICBO2016.pdf}, author = {James Balhoff} } @conference {D102, title = {D102: SPARQL2OWL: towards bridging the semantic gap between RDF and OWL}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Several large databases in biology are now making their information available through the Resource Description Framework (RDF). RDF can be used for large datasets and provides a graph-based semantics. The Web Ontology Language (OWL), another Semantic Web standard, provides a more formal, model- theoretic semantics. While some approaches combine RDF and OWL, for example for querying, knowledge in RDF and OWL is often expressed differently. Here, we propose a method to generate OWL ontologies from SPARQL queries using n-ary relational patterns. Combined with background knowledge from ontologies, the generated OWL ontologies can be used for expressive queries and quality control of RDF data. We implement our method in a a prototype tool available at https://github.com/ bio- ontology- research- group/SPARQL2OWL.

}, url = {http://ceur-ws.org/Vol-1747/D102_ICBO2016.pdf}, author = {Mona Alsharani and Hussein Almashouq and Robert Hoehndorf} } @conference {D201, title = {D101: Plant Image Segmentation and Annotation with Ontologies in BisQue}, 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/D101_ICBO2016.pdf}, author = {Justin Preece and Justin Elser and Pankaj Jaiswal and Kris Kvilekval and Dmitry Fedorov and B.S. Manjunath and Ryan Kitchen and Xu Xu and Dmitrios Trigkakis and Sinisa Todorovic and Seth Carbon} }