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Developing an application ontology for biomedical resource annotation and retrieval: challenges and lessons learned C. Torniai, M. Brush, N. Vasilevsky,

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Presentation on theme: "Developing an application ontology for biomedical resource annotation and retrieval: challenges and lessons learned C. Torniai, M. Brush, N. Vasilevsky,"— Presentation transcript:

1 Developing an application ontology for biomedical resource annotation and retrieval: challenges and lessons learned C. Torniai, M. Brush, N. Vasilevsky, E. Segerdell, M. Wilson, T. Johnson, K. Corday, C. Shaffer and M. Haendel

2  eagle-i project  Aims  Ontology role  eagle-i ontology  Requirements  Implementation  Implementation choices  Challenges Outline c o n s o r t i u m

3 eagle-i NIH funded pilot project working to make scientific resources more visible via a federated network of nine institutional repositories  Index invisible resources reagents, protocols, techniques, instruments, expertise, organisms, software, training, human studies, biological specimens, etc.  Ontology-driven approach to research resource annotation and discovery  Facilitate development of shared semantic entities that can be referenced in publications, databases, experiments, etc. c o n s o r t i u m

4 1)Represent collected resource information 2)Use the set of ontologies to control the data collection and search applications user-interface (UI) and logic 1)Build a set of ontologies that are reusable and interoperable with other ontologies and existing efforts for representing biomedical entities Ontology development drivers

5 c o n s o r t i u m Ontology role in eagle-i architecture eagle-i ontologies Federated Network Repositories (RDF) NIF, PubMed Entrez Gene Search Application Data Collection Application Resource information collection

6 Ontology/MethodScope/Purpose Basic Formal Ontology (BFO)Upper ontology Information Artifact Ontology (IAO) Ontology metadata Relation Ontology (RO)Common properties Minimum Information to Represent External Ontology Terms (MIREOT) Reuse classes and properties from external ontologies Implementation c o n s o r t i u m

7 Ontology layers Goal: to decouple research resources representation from information used for application appearance and behavior Application specific module – Classes, annotation properties and individuals required to drive the UIs eagle-i core ontology – Classes and properties used to represent information about biomedical research resources MIREOT files – Externally sourced classes and properties

8 c o n s o r t i u m eagle-i core and MIREOTed sources eagle-i core ontology: 1283 classes, 56 object properties, and 61 data properties. External OntologiesPurpose/subsetsClasses Ontology of Biomedical Investigations (OBI) research material entities, processes, devices, roles 509 NCBI TaxonomyOrganisms taxa192 VIVO ontology people, organization, publications 20 Ontology of Clinical Research (OCRe) human study designs and facets 19 Biomdedical Resource Ontology (BRO) instruments13

9 Application-specific module Contains properties and classes required to drive the UIs of the data collection and search applications – UI Annotation Definition file – Definition of UI annotation properties and sets of values for these properties – UI Annotations file – Holds annotations made on eagle-I core and MIREOTed classes and properties

10 c o n s o r t i u m Examples of annotation values and use LabelDescriptionExample Primary Resource Type Denotes classes for which instances are collected ‘instrument’, ‘biospecimen’, ‘protocol’ Data Model Exclude Denotes classes or properties that are not included in the model used for the data tool or the search tool UIs BFO classes such ‘continuant’ or ‘occurrent’ or RO relations such ‘precedes’ Embedded Class Denotes a class for which instances can only be created in the context of an embedding class ‘antibody immunogen’ created within ‘antibody’, ‘construct insert’ created within ‘plasmid’

11 Additional application-specific properties Property Label DescriptionExampleProperty Type eagle-i domain constraint Used to specify the domain of an imported property. Each annotation will contain the URI of one class Value set to “OBI_ ” (‘organization’) for RO property ‘location_of’’ Data Property eagle-i range constraint Used to specify the range of an imported property. Each annotation will contain the URI of one class Value set to “ERO_ ” (‘instrument’) for RO property ‘located_in’ Data Property eagle-i preferred label Defines the value of preferred label to display in the data collection tool and search UIs Capitalized ‘Organization’ for OBI_ (‘organization’) Annotation Property

12 Classes annotated with ‘primary resource type ’ Construct insert is an example of a resource annotated as an ‘embedded class’, ‘eagle-i preferred definition’ is used for tooltips ‘eagle-i preferred label’ is used for the display name Property annotated as ‘’primary property’ Technique is annotated as ‘referenced taxonomy’ Data Collection Application

13 c o n s o r t i u m  Reuse of existent ontologies  Ontology Layers  Application-specific module  Community coordination and alignment  Best practices and tools Challenges and benefits

14 c o n s o r t i u m  BFO and the relation ontology (RO)  OBO Foundry orthogonality principle Advantages – Integration with other ontologies – Ease the design process – Data integration and publication (Linked Open Data) Challenges – Need to exclude some classes (continuant, occurrent) from UI visualization after the inferred module has computed – Domain and Range in RO not specified or not specific enough for an application – Not all relevant ontologies are built using BFO and RO Reuse of existent ontologies

15 c o n s o r t i u m Advantages – Effective means to drive an application UI while maintaining interoperability with external ontologies and data sources – Facilitate parallel concurrent development Challenges – Keeping the annotations current with the core module – Risk of excessive proliferation of annotation properties as quick way to simplify application development complexity Ontology layers

16 c o n s o r t i u m Requirements for bridging the gap between an application and domain-specific ontologies – Application-specific labels and definitions – Exclusion of sets of classes and properties from the model used by the application – Restriction of domain and range for some imported properties – Definition of display order of object and data properties at class level Application-specific module

17 c o n s o r t i u m Commitment to collaboration with similar efforts aimed at resource modeling – Aligned high level models with NIF, RDS, VIVO – Service, instrument (device) implemented in OBI and reused by NIF and eagle-i – Coordinated representation of reagents, biospecimens, and genotype information (in progress) Challenges – Process is time consuming and it requires extra implementation efforts Implement and import back from reference ontologies – Application ontologies have peculiar requirements Example: Service hierarchy in eagle-i based on type of process rather than input and output of the process (OBI) Community coordination

18 c o n s o r t i u m  Reusing/referencing existent ontologies – Ontofox, OWL module extractor, NCBO extractor service  Have tools integrated in ontology editors (Protégé) – Effective methods for managing and syncing MIREOTed terms  Have several “community views” or ‘slims’ that could be directly imported with different level of complexity Best practices and tools

19 c o n s o r t i u m  Developing an ontology-driven application has been an important benchmark for usage of biomedical ontologies  We have designed a layered set of ontologies, consisting of a broadly applicable core ontology and application- specific module – Requirements and principles to inform a general design pattern  Future steps  Refining, documenting and sharing requirements and lessons learned  Engage in efforts addressing the issues we have experienced Conclusion

20 c o n s o r t i u m Thank you eagle-i core module: eagle-i search: Carlo Torniai Acknowledgments: Ted Bashor, Rob Frost, Larry Stone and Daniela Bourges Project funded through NIH/NCRR ARRA award #U24RR029825


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