@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 {W05-05, title = {W05-05: Co-occurrence Analysis of Adverse Events for Typhoid Fever Vaccines}, 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 = {Salmonella enterica serotype Typhi is considered as one of the high-priority potential bioterrorism agents by the Center for Disease Control and Prevention (CDC). Vaccines against Typhi can help with the prophylaxis against typhoid fever. However, little effort has been conducted for post-market safety monitoring of typhoid fever vaccines. In this paper, we proposed a novel network-based computational approach to investigate the co-occurrence relationships among adverse events reported after typhoid fever vaccine (TYP). We focused on association data that were recorded in the Vaccine Adverse Event Reporting System (VAERS) between 1990 and 2014. First, we extracted and summarized adverse event (AE) information from TYP related reports in the VAERS database using Resource Description Framework (RDF). Then, we applied a series of network approaches to the AE co-occurrence network to identify potential associations among these AEs. Specifically, we (1) constructed an AE co-occurrence network after the typhoid fever vaccines; (2) calculated network properties of AE co-occurrence network; (3) identified condensed subnetworks in AE co-occurrence network; and (4) compared MedDRA terms associated with AEs in each subnetwork. We observed that (1) AE co-occurrence network shares the same scale-free network property as other biological networks and social networks; (2) AEs clustered in one subnetwork are usually enriched in certain MedDRA terms.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Yuji Zhang and Jingcheng Du 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 {W05-03, title = {W05-03: Modulated Evaluation Metrics for Drug-Based 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 = {Our review of randomly selected biomedical ontologies from the National Center for Biomedical Ontology Bioportal showed that majority of the ontologies hosted did not have any documented evidence of formal ontology evaluation. The review points to the need to address this gap, if the ontology research community were to continually produce and maintain high-quality datasets for a wide-range of applications and research endeavors. As a result, this research presents a method to accurately evaluate specific domain ontologies using a semiotic framework that is delineated by the following parts {\textendash} syntactic, semantic, pragmatic, and social. Thusly, we propose the following, 1) whether a semiotic-based approach for ontology evaluation can provide meaningful assessment for biomedical ontologies and 2) if this approach can provide a more accurate assessment of the overall quality of an ontology. We applied this evaluation framework on drug-based ontologies and tailored the metric suite based on features of the drug ontologies. The results of our effort produced a customized metric for drug based ontologies, and also revelations specific to drug ontologies that may offer prescriptions for improvement {\textendash} better selection, consistency, and expressiveness of terms and labels. While ontology evaluation may be a neglected sub-field in ontology research, this study can offer a feasible direction for further research for biomedical ontologies.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Muhammad Amith and Cui Tao} } @conference {W05-01, title = {W05-01: On Prescribing Drug Prescriptions}, 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 = {Ryeyan Taseen and Jean-Francois Ethier and Adrien Barton} } @conference {358, title = {W01-03: Computer Vision for Next Generation Phenomics and Tree of Life}, 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 = {

To build the Tree of Life, scientists collect data on all heritable features {\textendash} both genotypes (e.g., DNA sequences) and phenotypes (e.g., anatomy, behavior, physiology) for all living and extinct species.\  The collection of phenomic data for tree-building has lagged far behind the collection of genomic data. Advances in computer vision have the potential to change this situation. In this talk, I will present a computer vision system developed in our lab for extending phenomic matrices, and in this way\ building the Tree of Life.\ Rows of a phenomic matrix represent images of specimens belonging to various species of interest, and columns represent scores of their phenomic characters. Given a\ phenomic matrix where only few rows are manually annotated with character scores, our vision system extends the matrix row-wise by populating missing character scores of the remaining species in the matrix. The talk will present our experimental results on scoring phenomic characters in images of bat skulls, nematocysts, and leaves, available in the Morphobank and Bisque data repositories.

}, keywords = {Bisque data repository, data visualization, Morphobank, Phenotypes, Tree of Life}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Sinisa Todorovic} } @conference {IT603, title = {IT603: Improving the Semantics of Drug Prescriptions with a Realist 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 = {

Electronic prescriptions are supported as a means to reduce adverse drug events, but the ambiguities and overspecificities of prescription semantics along with their lack of standardization reduce adoption, limit interoperability and are potential sources of error. Ontologies in the OBO Foundry, founded on realist methodology, have been successful in fostering the logical, scientifically accurate data standards that the domain of drug prescriptions is currently in need of. This paper illustrates some problems regarding the structuration of current electronic prescriptions, and demonstrates how the Prescription of Drugs Ontology (PDRO) addresses these issues with improved semantics founded on OBO and realist principles. PDRO reuses classes and object properties from IAO, OBI, OGMS, OMRSE and DRON, introducing new entities within its scope and proposing entities within those of its imported domains that may be useful to other health care and information artifact-related ontologies in the OBO Foundry. PDRO aims at improving the semantics of drug prescriptions and prospectively enabling the interoperability of prescription data.

}, url = {http://ceur-ws.org/Vol-1747/IT603_ICBO2016.pdf}, author = {Jean-Francois Ethier and Ryeyan Taseen and Luc Lavoie and Adrien Barton} } @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 {IT502, title = {IT502: Visualizing the {\textquotedblleft}Big Picture{\textquotedblright} of Change in NCIt{\textquoteright}s Biological Processes}, 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 National Cancer Institute thesaurus (NCIt) is a large and complex ontology. NCIt is frequently updated; a new release is made available approximately every month. Tracking structural changes in NCIt is important for the editors of its content. In this paper we describe a methodology and tool using diff partial-area taxonomies to visually summarize structural changes between two NCIt releases. Diff partial-area taxonomies provide a comprehensible view of the overall impact of the changes. This methodology is illustrated using the Biological Process hierarchy. Specifically, we illustrate how diff partial-area taxonomies reflect change that occurred due to major restructuring of this hierarchy between September 2004 and December 2004. During this time the hierarchy nearly doubled in size and a large portion of the classes were extensively modified. Several kinds of change patterns are identified and discussed.

}, url = {http://ceur-ws.org/Vol-1747/IT502_ICBO2016.pdf}, author = {Yehoshua Perl and Christopher Ochs and Sherri de Coronado and Nicole Thomas} } @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 {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 = {

n/a

}, 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 {IP25, title = {IP25: The Zebrafish Experimental Conditions Ontology Systemizing Experimental Descriptions in ZFIN}, 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 Zebrafish Experimental Conditions Ontology (ZECO) defines the major experimental conditions used in research studies that employ the zebrafish, Danio rerio. We are systematically building the ontology to encompass both standard control conditions and experimental conditions and it is designed to allow better data curation and more precise information retrieval.

}, url = {http://ceur-ws.org/Vol-1747/IP25_ICBO2016.pdf}, author = {Yvonne Bradford and Ceri Van Slyke and Sabrina Toro and Sridhar Ramachandran} } @conference {IP23, title = {IP23: Towards an Ontology of Schizophrenia}, 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 paper presents an Ontology of Schizophrenia (OS) that is designed to provide a formal representation of schizophrenia-related entities crucial to the treatment and study of schizophrenia. OS is developed in accordance with the OBO (Open Biomedical Ontology) Foundry principles and constructed in compliance with Basic Formal Ontology (BFO) as its upper-level ontology. It uses mid-level and domain ontologies built in conformity with BFO such as the Ontology for General Medical Science (OGMS) and the Mental Functioning Ontology (MFO). OS is developed using Prot{\'e}g{\'e} 4.3 and is implemented in OWL2. Having imported BFO2.0 OWL and the development version of OGMS, OS adds approximately 30 schizophrenia-related terms and attempts to provide both formal and textual definitions for each. The terms in OS are in addition annotated with ontology metadata such as labels, textual definitions, definition sources, term editors and editor notes.

}, url = {http://ceur-ws.org/Vol-1747/IP23_ICBO2016.pdf}, author = {Fumiaki Toyoshima} } @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 {IP13, title = {IP13: uc_Eating: Ontology for unambiguous characterization of eating and food habits}, 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 uc_Eating ontology is a standardized unambiguous characterization system for modeling human food habits and eating processes. The uc_Eating ontology along with the physiological, environmental, behavioral, and food ontologies it maps to, provide an infrastructure for annotating the relationships between food, food consumption, eating behaviors, and environments creating a foundation for computable knowledge bases around food and beverage consumption scenarios, their observation, interrogation, and manipulation at biological, behavioral, and environmental levels.

}, url = {http://ceur-ws.org/Vol-1747/IP13_ICBO2016.pdf}, author = {Kimiya Taji and Matthew Lange} } @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 {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} } @conference {BP03, title = {BP03: Label Embedding Approach for Transfer Learning}, 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 = {

Automatically tagging textual mentions with the concepts, types and entities that they represent are important tasks for which supervised learning has been found to be very effective. In this paper, we consider the problem of exploiting multiple sources of training data with variant ontologies. We present a new transfer learning approach based on embedding multiple label sets in a shared space, and using it to augment the training data.

}, url = {http://ceur-ws.org/Vol-1747/BP03_ICBO2016.pdf}, author = {Rasha Obeidat and Xiaoli Fern and Prasad Tadepalli} } @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} }