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NifStd and NeuroLex : Development of a Comprehensive Neuroscience Ontology

NifStd and NeuroLex : Development of a Comprehensive Neuroscience Ontology. Fahim IMAM, Stephen LARSON, Georgio ASCOLI, Gordon SHEPHERD, Anita BANDROWSKI, Jeffery S. GRETHE, Amarnath GUPTA, Maryann E. MARTONE University of California, San Diego, CA

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NifStd and NeuroLex : Development of a Comprehensive Neuroscience Ontology

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  1. NifStd and NeuroLex: Development of a Comprehensive Neuroscience Ontology Fahim IMAM, Stephen LARSON, Georgio ASCOLI, Gordon SHEPHERD, Anita BANDROWSKI, Jeffery S. GRETHE, Amarnath GUPTA, Maryann E. MARTONE University of California, San Diego, CA George Mason University, Fairfax, VA Yale University, New Haven, CT ICBO Workshop 2011 July 26, 2011 Funded in part by the NIH Neuroscience Blueprint HHSN271200800035C via NIDA NEUROSCIENCE INFORMATION FRAMEWORK

  2. NIF: Discover and utilize web-based Neuroscience resources UCSD, Yale, Cal Tech, George Mason, Harvard MGH • A portal for finding and using neuroscience resources • A consistent framework for describing resources • Provides simultaneous search of multiple types of information, organized by category • NIFSTD Ontology, a critical component • Enables concept-based search Easier Supported by NIH Blueprint The Neuroscience Information Framework (NIF), http://neuinfo.org

  3. NIF Standard Ontologies (NifStd) Bill Bug et al. • Modules cover orthogonal domain • e.g. , Brain Regions, Cells, Molecules, Subcellular parts, Diseases, Nervous system functions, etc. • Set of modular ontologies • Covering neuroscience relevant terminologies • Comprehensive ~60, 000 distinct concepts + synonyms • Expressed in OWL-DL language • Supported by common DL Resoners • Closely follows OBO community best practices • Avoids duplication of efforts • Standardized to the same upper level ontologies • e.g., Basic Formal Ontology (BFO), OBO Relations Ontology (OBO-RO), Phonotypical Qualities Ontology (PATO) • Relies on existing community ontologies e.g., CHEBI, GO, PRO, OBI etc.

  4. NIFSTD External Community Sources

  5. Importing or Adapting a new ontology or vocabulary source

  6. NIFSTD Design Principles • Single Inheritance for Named Classes • Follows simple inheritance principle for named classes • An assertednamed class can have only one named class as its superclass • Promotes the named classes to be univocal and to avoid ambiguities • Classes with multiple named superclasses • Can be inferred using automated reasoners • Saves a great deal of manual labor and minimizes human errors • Alan Rector’s Normalization principles.

  7. Design Principles • Unique Identifiers and Annotation Properties. • NIFSTD entities are identified by a unique identifier and accompanied by a variety of annotation properties • Derived from Dublin Core Metadata (DC) and Simple Knowledge Organization System (SKOS) model. • Synonyms, acronyms, definition, defining source etc. • Reuse the same URI through MIREOTed classes from external source, • Allows to avoid extra mapping annotations, e.g., class identifiers remain unaltered.

  8. Design Principles • Annotation properties associated with versioning different levels of contents • creation date and modification dates • file level versioning for each of the modules • annotations for retiring antiquated concept definitions • hasFormerParentClass and isReplacesByClass etc. • tracking former ontology graph position and replacement concepts.

  9. Design Principles • Object Properties and Bridge Modules. • Mostly drawn from OBO Relations Ontology (OBO-RO) • Intra-module relations are kept within the same module • ONLY universal restrictions are considered • e.g., partonomy relations within different brain regions • The cross-module relations are specified in separate bridging modules • Modules that only contain logical restrictions on a set of classes assigned between multiple modules. • Allows main domain modules—e.g., anatomy, cell type, etc. to remain independent of one another

  10. Design Principles Two example bridging modules in NIFSTD • Helps keeping the modularity principles intact • facilitate extensions for broader communities without NIF-centric views • These bridging modules can easily be excluded in order to focus on core modules

  11. Typical Knowledge Model A typical knowledge model in NIFSTD. Both cross-modular and intra-modular classes are associated through object properties mostly drawn from the OBO Relations ontology (RO).

  12. An Analogy Easier Difficult

  13. Typical Use of Ontology in NIF • Basic feature of an ontology • Organizing the concepts involved in a domain into a hierarchy and • Precisely specifying how the classes are ‘related’ with each other (i.e., logical axioms) • Explicit knowledge are asserted but implicit logical consequences can be inferred • A powerful feature of an ontology

  14. Ontology – Asserted Hierarchy

  15. NIF Concept-Based Search Types of GABAergicneurons • Search Google: GABAergic neuron • Search NIF: GABAergic neuron • NIF automatically searches for types of GABAergic neurons

  16. NifStd Current Version • Key feature: Includes useful defined concepts to infer useful classification NIF Standard Ontologies

  17. NifStd and NeuroLex Wiki Stephen D. Larson et al. Semantic wiki platform Provides simple forms for structured knowledge Can add concepts, properties Generate hierarchies without having to learn complicated ontology tools Good teaching tool for principles behind ontologies Community can contribute NIF Standard Ontologies

  18. NeuroLexvs.NIFSTD At a glance guide to the differences between NeuroLex and NIFSTD Larson et. al

  19. Top Down Vs. Bottom up • Top-down ontology construction • A select few authors have write privileges • Maximizes consistency of terms with each other • Making changes requires approval and re-publishing • Works best when domain to be organized has: small corpus, formal categories, stable entities, restricted entities, clear edges. • Works best with participants who are: expert catalogers, coordinated users, expert users, people with authoritative source of judgment NIFSTD • Bottom-up ontology construction • Multiple participants can edit the ontology instantly • Control of content is done after edits are made based on the merit of the content • Semantics are limited to what is convenient for the domain • Not a replacement for top-down construction; sometimes necessary to increase flexibility • Necessary when domain has: large corpus, no formal categories, no clear edges • Necessary when participants are: uncoordinated users, amateur users, naïve catalogers • Neuroscience is a domain that is less formal and neuroscientists are more uncoordinated NEUROLEX Larson et. al

  20. NeuroLex Wiki Contributions http://neurolex.org/wiki/Special:ContributionScores

  21. NifStd/NeurolexCuration Workflow ‘has soma location’ in NeuroLex == ‘Neuron X’ has_part some (‘Soma’ and (part_of some ‘Brain region Y’)) in NIFSTD

  22. Access to NIFSTD Contents • NIFSTD is available as • OWL Format http://ontology.neuinfo.org • RDF and SPARQL Endpoint http://ontology.neuinfo.org/sparql-endpoint.html • Specific contents through web services • http://ontology.neuinfo.org/ontoquest-service.html • Available through NCBO Bioportal • Provides annotation and mapping services • http://bioportal.bioontology.org/ NIF Standard Ontologies

  23. Working to Incorporate Community • NeuroPsyGrid • http://www.neuropsygrid.org • NDAR Autism Ontology • http://ndar.nih.gov • Disease Phenotype Ontology • http://openccdb.org/wiki/index.php/Disease_Ontology • Cognitive Paradigm Ontology (CogPO) • http://wiki.cogpo.org • Neural ElectroMagnetic Ontologies (NEMO) • http://nemo.nic.uoregon.edu

  24. Summary and Conclusions NIF with NIFSTD provides an example of how ontologies can be practically applied to enhance search and data integration across diverse resources We believe, we have defined a process to form complex semantics to various neuroscience concepts through NIFSTD and through NeuroLex collaborative environment. NIF encourages the use of community ontologies Moving towards building rich knowledgebase for Neuroscience that integrates with larger life science communities.

  25. Point of Discussion Gaining OBO Foundry community consensus for a production system is difficult as we often need to move quickly along with the project We rather favor a system whereby we start with minimal complexity as required and add more as the ontologies evolve over time towards perfection What should be the most effective way to collaborate and gain community consensus?

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