Accepted Papers


Long and Short Research Papers


Yuki Yamagata, Hiroko Kou, Kouji Kozaki, Riichiro Mizoguchi, Takeshi Imai and Kazuhiko Ohe. Ontological Model of Abnormal States and its Application in the Medical Domain
Abstract [] PDF:
Exchanging medical data and information is important in the medical domain. This paper discusses abnormal states in the definition of diseases. First, we introduce our unified representation framework of abnormal states in a property-decomposed manner, which allows interoperability between clinical data and abnormal states in the definition of diseases. Next, we propose our ontological model of abnormal states. In our model, common concepts can be kept distinct from specific ones. By combing causalapplying causal chains of diseases, we can capture the commonalities of abnormal states across clinical divisions. This work will contribute to various applications for understanding knowledge about abnormal states with no ambiguity.

Albert Goldfain, Min Xu, Jonathan Bona and Barry Smith. Ontology Based Annotation of Contextualized Vital Signs
Abstract [] PDF:
Representing the kinetic state of a patient (posture, motion, and activity) during vital sign measurement is an important part of continuous monitoring applications, especially remote monitoring applications. In contextualized vital sign representation, the measurement result is presented in conjunction with salient measurement context metadata. We present an automated annotation system for vital sign measurements that uses ontologies from the Open Biomedical Ontology Foundry (OBO Foundry) to represent the patient’s kinetic state at the time of measurement. The annotation system is applied to data generated by a wearable personal status monitoring (PSM) device. We demonstrate how annotated PSM data can be queried for contextualized vital signs as well as sensor algorithm configuration parameters.
 
Maxwell Neal, Daniel Cook and John Gennari.An OWL knowledge base for classifying and querying collections of physiological models: A prototype human physiome
Abstract [] PDF:
The human physiome is envisioned as the quantitative description of the whole of human physiology. Researchers across the world are actively working toward achieving this grand challenge by populating publicly available repositories of quantitative physiological models. However, no mechanism has been developed that integrates the information from these models into a single knowledge resource that can be said to represent a physiome. Here we present a mechanism for automatically generating a physiome knowledge base (SemPhysKB) from models collected from the CellML repository, the National Simulation Resource repository, and the BioModels database. Applying description logic axioms and automated classification, we demonstrate that the SemPhysKB can be queried to retrieve models, sub-models and mathematical formulations of interest, and provides a set of logically defined physiological reference terms currently unavailable among biomedical ontologies
 
Carlo Torniai, Shahim Essaid, Brian Lowe, Jon Corson-Rikert and Melissa Haendel. Finding common ground: integrating the eagle-i and VIVO ontologies
Abstract [] PDF:
This paper describes the approach and strategies we have been ap-plying during the process of integrating and modularizing the eagle-i and VIVO ontologies into the Integrated Semantic Framework. As this effort is yet on going, we will subsequently provide an evaluation re-port when the merger and modularization are complete. We welcome insight and comments from the ontology community about our pro-gress and approach thus far.
 
Christopher Ochs, Zhe He, Yehoshua Perl, Sivaram Arabandi, Michael Halper and James Geller. Choosing the Granularity of Abstraction Networks for Orientation and Quality Assurance of the Sleep Domain Ontology
Abstract [] PDF:
The NCBO BioPortal is a large repository of over 300 bio-medical ontologies covering a wide variety of domains. An abstraction network is a compact network summarizing the structure and content of a given ontology. Abstraction net-works have been shown to support orientation into and quality assurance of ontologies. Area and partial-area taxonomies are examples of abstraction networks that utilize the relationships of an ontology to group together classes with similar structure and semantics. These taxonomies can be derived in different ways, leading to different granularities of summarization. Such granularity is illustrated by applying various derivations methodologies to the Sleep Domain Ontology (SDO), hosted in BioPortal. The impact of different granularity levels is demonstrated with respect to orientation into the ontology’s structure and content. Support for quality assurance is demonstrated in a companion paper.
 
Jie Zheng, Chris Stoeckert and Elisabetta Manduchi. Development of an Application Ontology for Beta Cell Genomics Based On the Ontology for Biomedical Investigations
Abstract [] PDF:
The development process for the Beta Cell Genomics Ontology
(BCGO) is described. This process should be generally applicable and consists of integration of a subset of reference ontologies. A key ele-ment is use of the Ontology for Biomedical Investigation (OBI) as an ontology framework. Another element is enriching ontologies using existing patterns when needed. The ontology was evaluated in three aspects based on our needs including data annotation, queries and automated classification. The BCGO is available at: https://bcgo-ontology.googlecode.com/svn/trunk/ontology/bcgo.owl.
 
Karen Eilbeck, Jason Jacobs, Sunanda McGarvey, Cynthia Vinion and Catherine Staes. Exploring the use of ontologies and automated reasoning to manage selection of reportable condition lab tests from LOINC
Abstract [] PDF:
Epidemiologists publish criteria for laboratory tests that must be reported to public health agencies in order to initi-ate public health control measures. There are efforts to pub-lish value sets of standard laboratory test names using Logi-cal Observation and Identifier Names and Codes (LOINC®) codes to enable automated systems to use the codes to iden-tify reportable events. Unfortunately, the set of lab tests (and thus codes) vary by state, are difficult to manually curate, and may be missing desired or include undesired tests. Previously, we developed an ontology that classified the terminology used to describe LOINC®-coded tests for Chlamydia. For each test, we created a new ontology term with a logical definition, and used the HermiT reasoner to classify the tests resulting in a fully specified ontology in OWL and OBO format. We could query for logic using terms familiar to epidemiologists, and demonstrated views that allow the user to visualize selected codes in the context of all codes. Methods: To test the extensibility of our original model, we extended the ontology to handle tests for tuberculosis. We reviewed the requirements for tuberculosis laboratory test reporting in Utah and New York City gathered for the CDC’s Reportable Conditions Knowledge Management System (RCKMS) project. The microorganisms included Mycobacterium tuberculosis and a subset of Mycobacterium complex. The testing methods included 9 methods (e.g., tuberculin skin tests, interferon gamma release assay, acid fast bacillus culture/smear, PCR, etc.) of which only 3 had previously been cataloged in our ontology for Chlamydia reporting. We manually queried the LOINC® database to gather all possible tests for tuberculosis. We added 188 new terms to the existing base ontology following a hierarchy appropriate for epidemiological searching. As previously performed, we created a fully specified ontology. We que-ried the ontology and compared the findings with the lists curated manually for the RCKMS project. We reviewed three reporting criteria. Results: All lab tests in the ontology were classified by the logical reasoner and for these three criteria, every test in the manually-curated RCKMS value set was included in the ontology. For two criteria, querying the ontology returned a larger value set that subsumes the manually created set. The ontology-based query identified one lab test that was missed by the manual process. We found, however, that new terms for animal tests and laboratory test orders (rather than re-sults) need to be added to the ontology to improve specifici-ty. For a third criteria, the manual set includes a test that was present in the ontology but did not meet the specific conditions of the query. The extended ontology remains appropriate for Chlamydia tests. Conclusion: The LOINC® database provides structure that is useful to develop an application ontology to support epi-demiologists with the task of managing sets of codes that meet reporting criteria. The automated classification strate-gy we propose is reproducible and extendable to address new diseases and problems found as the ontology is im-proved.
 
Catalina Martínez Costa and Stefan Schulz. Ontology-based reinterpretation of the SNOMED CT context model
Abstract [] PDF:
SNOMED CT includes concepts that encode complex expressions in its context model under Situation with explicit context, which blends characteristics of information models with characteristics of ontologies. In order to improve interoperability of isosemantic expressions that are constituted by different information model / ontology combinations, we propose an ontology-based reinterpretation of four of the most representative patterns found in the SNOMED CT context model. The formalizations provided require the use of negation universal quantification, thus requiring a shift from OWL-EL to OWL-DL. After a thorough analysis of the meaning of the SNOMED CT concepts that instantiate these patterns as well as the ontological errors of the current OWL-EL rendering we transformed a module of SNOMED CT according to these patterns. The classification performance of the resulting ontology was benchmarked for several transformation steps. Classification times remained under 1s for Fact++ and under 3s for HermIT. Although the SNOMED CT module used comprised only those concepts which are needed for expressing the content of the context model (about 5% of the complete context models), the results are en-couraging as they suggest that the a limited inclusion of OWL-DL ex-pressiveness does not lead to inacceptable performance results.
 
William Hogan, Josh Hanna, Eric Joseph and Mathias Brochhausen. Towards a Consistent and Scientifically Accurate Drug Ontology
Abstract [] PDF:
Our use case for comparative effectiveness research requires an ontology of drugs that enables querying National Drug Codes (NDCs) in the United States by the active ingredient, mechanism of action, physiological effect, and therapeutic class of the drug products they represent. We conducted an ontological analysis of drugs from the realist perspective, and evaluated existing drug terminology, ontology, and database artifacts from (1) the technical perspective, as to whether it met our requirements, (2) the perspective of pharmacology and medical science, as to whether it contained incorrect scientific assertions about drugs, (3) the perspective of description logic semantics (if it were available in Web Ontology Language or OWL), and (4) the perspective of our realism-based analysis of the domain. We found that no existing resource was sufficient. Therefore, we built a Drug Ontology in OWL, which we additionally populated with NDCs and other classes from RxNorm using only content created by the National Library of Medicine. We also built an application that uses the ontology to query for NDCs according to the various properties of the drug products they represent, available at: http://ingarden.uams.edu/ingredients. This application uses an OWL-based description logic reasoner to execute end-user queries. The Drug Ontology is available at http://code.google.com/p/dron.
 
Jose L.V. Mejino Jr, Ravensara S Travillian, Timothy C Cox, James F Brinkley and Linda G Shapiro. Human Development Domain of the Ontology of Craniofacial Development and Malformation
Abstract [] PDF:
In this paper we describe an ontological scheme for representing anatomical entities undergoing morphological transformation and changes in phenotype during prenatal development. This is a proposed component of the Anatomical Transformation Abstraction (ATA) of the Foundational Model of Anatomy (FMA) Ontology that was created to provide an ontological framework for capturing knowledge about human development from the zygote to postnatal life. It is designed to initially describe the structural properties of the anatomical entities that participate in human development and then enhance their description with developmental properties, such as temporal attributes and developmental processes. This approach facilitates the correlation and integration of the classical but static representation of embryology with the evolving novel concepts of developmental biology, which primarily deals with the experimental data on the mechanisms of embryogenesis and organogenesis. This is important for describing and understanding the underlying processes involved in structural malformations. In this study we focused on the development of the lips and the palate in conjunction with our work on the pathogenesis and classification of cleft lip and palate (CL/P) in the FaceBase program. Our aim here is to create the Craniofacial Human Development Ontology (CHDO) to support the Ontology of Craniofacial Development and Malformation (OCDM), which provides the infrastructure for integrating multiple and disparate craniofacial data generated by FaceBase researchers.
 
Alexander P. Cox, Mark Jensen, Alan Ruttenberg, Kinga Szigeti and Alexander D. Diehl. Measuring Cognitive Functions: Hurdles in the Development of the NeuroPsychological Testing Ontology
Abstract [] PDF:
The NeuroPsychological Testing Ontology (NPT) provides a set of classes for the representation and annotation of neuropsychological tests and the associated data. These classes are intended to enable the integration of results from a variety of neuropsychological tests that assay similar or overlapping domains of cognitive functioning. Neuro-psychological testing is an important component in developing the clinical picture used in the diagnosis of patients with a range of neuro-logical diseases. A core assumption in designing and implementing these tests is that their results provide more than just a description of a patient’s behavior. We contend that cognitive functioning assays provide information about the state of the patient’s cognitive functions. Impairment of these cognitive functions is typically responsible for the patient’s observed behavior and can be linked to the medical condition that caused the impairment. In this paper, we discuss how to best rep-resent cognitive functioning assays and the resulting measurements of cognitive functions. Many theoretical and practical issues arise in the course of representing cognitive functioning assays, cognitive func-tions, and measurements of cognitive functions. This paper discusses some of these issues and proposes a solution to them that is developed within the context of the Ontology for Biomedical Investigations (OBI). In particular, the handedness assay is used as a model for representing cognitive functioning assays.
 
Snezana Nikolic, Prabhu Shankar, Sivaram Arabandi, Akshaye Dhawan, Rajshekhar Sunderraman, Sham Navathe, Kunal Malhotra and Rani Singh. ONSTR: The Ontology for Newborn Screening Follow-up and Translational Research
Abstract [] PDF:
Translational research in the field of newborn screening system requires integration of data generated during various phases of life long treatment of patients identified and diagnosed through newborn dried blood spot screening (NDBS). In this paper, we describe Ontology for Newborn Screening Follow-up and Translational Research (ONSTR). ONSTR will serve as a core of the data integration framework the Newborn Screening Follow-up Data Integration Collaborative (NBSDC) designed to support Semantic web tools and applications with the goal of helping clinicians involved in translational research. ONSTR is an application ontology for representing data entities, practices and knowledge in the domain of the newborn screening follow-up of patients diagnosed with inheritable and congenital disorders. Here, we describe ONSTR domain, our top-down bottom up methodological approach to ontology modeling and lessons learned. Additionally, we provide an illustration of the ontological model of some aspects of PKU which include 1) etiology of PKU, 2) phenylalanine hydroxylase enzyme dysfunction underlying PKU and 3) through application of ontological reasoning we provide the disambiguation of terms central to PKU appearing in the literature. In modeling the mechanism of PAH enzyme dysfunction, we encountered limitations in using GO, in terms of over-granularity of represented biological processes and the lack of representations of their participants. As a solution to this problem and to accurately represent the process we adopted the role based approach of biochemical reactions along with the terms from existing quality ontologies and ONSTR. This will serve as a prototype for modeling of the etiology of other IMDs and enzymatic processes of impor-tance to clinical and translational research in long-term follow-up domain. These initial steps provide ontological foundation for automated reasoning, integration and annotation of data collected through newborn screening system.
 
Shahim Essaid, Carlo Torniai and Melissa Haendel. Enabling semantic search in a bio-specimen repository
Abstract [] PDF:
We present our effort to enhance a bio-specimen repository search application with semantic search capability. We describe the nature of the original data, the application of text processing tools, and the leveraging of existing terminologies and ontologies to build an application ontology that supports the bio-specimen search system. We also describe few of the difficulties we have encountered and possible ways for addressing them in our future work.
 
Simon Kocbek, Jin-Dong Kim, Jean-Luc Perret and Patricia L. Whetzel. Visualizing ontology mappings to help ontology engineers identify relevant ontologies for their reuse
Abstract [] PDF:
The importance of ontologies in biomedicine is increasing in the areas such as the standardization of terminology, the verification of data consistency, and the integration of heterogeneous biomedical databases. New ontologies are being built and added to repositories such as BioPortal. The ontologies represent a large network of biomedical concepts where a single ontology connects a group of closely related concepts. When ontology engineers build new ontologies they often search for existing ontologies to avoid redundancy of concepts. When selecting existing ontologies, engi-neers consider different factors such as ontology domain, the size of the ontology, and also the relations between ontologies and their concepts. In this paper we present a graph that aims to visualize mappings of all BioPortal ontologies. This graph can help ontology engineers in deciding which ontology to use when selecting existing concepts for building new ontologies.
 
Jonathan M. Mortensen, Paul R. Alexander, Mark A. Musen and Natalya F. Noy. Crowdsourcing Ontology Verification
Abstract [] PDF:
Biomedical ontologies are becoming increasingly large and complex. A single user cannot easily develop or maintain them. Researchers have developed various automated techniques to assist with ontology development and engineering at scale. However, these solutions are not always complete. Microtask crowdsourcing, wherein workers are paid small amounts to complete simple, short tasks, may be one technique to alleviate some of the development difficulties. Previously, we developed a method to verify an ontology hierarchy using microtask crowdsourcing. In this work, we investigated the finer details of the design and configuration of a hierarchy- verification task. For example, when we provided definitions and required qualifications, workers performed with 82% accuracy on the hierarchy-verification task, compared to 50% without. We showed that to achieve reasonable performance on such a task, workers require context via definitions, tasks require qualifications that select a worker with proper domain knowledge, and a question must be phrased with the least cognitive load (i.e., in the simplest way).


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Early Career Track Papers


Melanie Courtot, Jie Zheng, Chris Stoeckert, Ryan Brinkman and Alan Ruttenberg. Diagnostic criteria and clinical guidelines standardization to automate case classification
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Matthew Brush, Chris Mungall, Nicole Washington and Melissa Haendel. What’s in a Genotype?: An Ontological Characterization for Integration of Genetic Variation Data
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William Duncan, Titus Schleyer and Alan Ruttenberg. Representing Intracoranal Tooth Restorations in the Ontology for Oral Health and Disease
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Heiner Oberkampf, Sonja Zillner, Bernhard Bauer and Matthias Hammon. An OGMS-based Model for Clinical Information (MCI)
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Leonid L. Chepelev and Michel Dumontier. The Web as a Distributed Biochemical Reactor: Semantically Enabled Metabolic Fate Prediction
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Software demonstration


Melissa Haendel, Carlo Torniai, Nicole Vasilevsky, Scott Hoffmann and Daniela Bourges-Waldegg. eagle-i: ontology-driven federated search and data entry tools for discovering biomedical research resources.
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Cheng Chen, Josh Hanna, John Talburt, Mathias Brochhausen and William Hogan. A Demonstration of Entity Identity Information Management Applied to Demographic Data in a Referent Tracking System.
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Christopher Ochs, Yehoshua Perl and James Geller. BLUSNO: A System for Orientation, Visualization, and Quality Assurance of SNOMED CT Using Abstraction Networks.
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Tomasz Adamusiak, Naoki Shimoyama, Marek Tutaj and Mary Shimoyama. Next generation ontology browser.
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Ulf Schwarz, Holger Stenzhorn, Nikolina Koleva, Luc Schneider and Emilio M. Sanfilippo.  A method for semi-automatic extension of a middle-layer ontology.
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Matthew Horridge, Jonathan Mortensen, Tania Tudorache, Jennifer Vendetti, Csongor I Nyulas, Mark Musen and Natasha F. Noy. Introducing WebProtege 2 as a Collaborative Platform for Editing Biomedical Ontologies
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Posters


Arash Shaban-Nejad, Christian Jauvin, Maxim Lavigne, Masoumeh Izadi, Luke Mondor, Anya Okhmatovskaia and David L. Buckeridge.
PopHR: An Integrated Semantic Framework for Population Health Surveillance
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Patrick Ray and Alexander Diehl. The Ocular Disease Ontology
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Matthew Brush, Melissa Haendel, Jie Zheng, Yongqun He, Sirarat Sarntivijai, Bjoern Peters, Alexander Diehl and Christian Stoeckert. Alignment of Cultured Cell Modeling Across OBO Foundry Ontologies: Key Outcomes and Insights
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Mark Jensen, Alexander P. Cox, Barry Smith and Alexander D. Diehl. Representing Disease Courses: An application of the Neurological Disease Ontology to Multiple Sclerosis Typology
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Zuoshuang Xiang and Yongqun He. HINO: BFO-aligned ontology representation of human molecular interactions and pathways
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Samuel Croset, John Overington and Dietrich Rebholz-Schuhmann. Brain, Biomedical Knowledge Manipulation
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Samuel Croset, John Overington and Dietrich Rebholz-Schuhmann. The Functional Therapeutic Chemical Classification System
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Alison Callahan and Michel Dumontier. HyQue: A Semantic Web tool for evaluating scientific hypotheses
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Alison Callahan, Jose Cruz-Toledo, Peter Ansell and Michel Dumontier. Bio2RDF Release 2: Improved coverage, Interoperability and Provenance of Linked Data for the Life Sciences
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Mary E Dolan and Karen E Rasmussen. OncoCL: A Cancer Cell Ontology
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James Overton, Randi Vita, Jason Greenbaum, Heiko Dietze, Alessandro Sette and Bjoern Peters. A Taxonomy for Immunologists
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