Fahim Imam Workshop on using Multiple Ontologies, International Conference on Biomedical Ontologies (ICBO 2011), Buffalo, NY July 26, 2011

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
 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
UCSD, Yale, Cal Tech, George Mason, Harvard MGH
Supported by NIH Blueprint
Easier
The Neuroscience Information Framework (NIF), http://neuinfo.org

3. NIF STANDARD ONTOLOGIES (NIFSTD)
• 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.
3
• Modules cover orthogonal domain
e.g. , Brain
Regions, Cells, Molecules, Subcellul
ar parts, Diseases, Nervous system
functions, etc.
Bill Bug et al.

4. 4
NIFSTD EXTERNAL COMMUNITY SOURCES
Domain External Source Import/ Adapt Module
Organism taxonomy NCBI Taxonomy, GBIF, ITIS, IMSR, Jackson Labs mouse catalog Adapt NIF-Organism
Molecules IUPHAR ion channels and receptors, Sequence Ontology (SO),
ChEBI, and Protein Ontology (PRO); pending: NCBI Entrez
Protein, NCBI RefSeq, NCBI Homologene, NIDA drug lists
Adapt
IUPHAR,
ChEBI;Import
PRO, SO
NIF-Molecule
NIF-Chemical
Sub-cellular Sub-cellular Anatomy Ontology (SAO). Extracted cell parts and
subcellular structures. Imported GO Cellular Component
Import NIF-Subcellular
Cell CCDB, NeuronDB, NeuroMorpho.org. Terminologies; pending:
OBO Cell Ontology
Adapt NIF-Cell
Gross Anatomy NeuroNames extended by including terms from BIRN, SumsDB,
BrainMap.org, etc; multi-scale representation of Nervous
System Macroscopic anatomy
Adapt NIF-
GrossAnatomy
Nervous system
function
Sensory, Behavior, Cognition terms from NIF, BIRN,
BrainMap.org, MeSH, and UMLS
Adapt NIF-Function
Nervous system
dysfunction
Nervous system disease from MeSH, NINDS terminology;
Disease Ontology (DO)
Adapt/Import NIF- Dysfunction
Phenotypic qualities PATO is Imported as part of the OBO foundry core Import NIF-Quality
Investigation: reagents Overlaps with molecules above, especially RefSeq for mRNA Import NIF-Investigation
Investigation:
instruments, protocols
Based on Ontology for Biomedical Investigation (OBI) to include
entities for biomaterial transformations, assays, data
transformations
Adapt NIF-Investigation
Investigation: Resource NIF, OBI, NITRC, Biomedical Resource Ontology (BRO) Adapt NIF-Resource
Biological Process Gene Ontology’s (GO) biological process in whole Import NIF-BioProcess
Cognitive Paradigm Cognitive Paradigm Ontology (CogPO) Import NIF-Investigation

5. IMPORTING OR ADAPTING A NEW ONTOLOGY OR
VOCABULARY SOURCE
Source Import/adapt
a source already in OWL, uses the OBO-
RO and the BFO and is orthogonal to
existing modules
the import simply involves adding an
owl:import statement
existing orthogonal ontology is in OWL
but does not use the same foundational
ontologies as NIFSTD
an ontology-bridging module (explained
later) is constructed declaring the deep
level semantic equivalencies such as
foundational objects and processes.
external source is satisfied by the above
two rules but observed to be too large for
NIF’s scope of interests
a relevant subset is extracted.
MIREOT principles has been adopted
external source has not been represented
in OWL, or does not use the same
foundation as NIFSTD,
the terminology is adapted to
OWL/RDF in the context of the
NIFSTD foundational layer ontologies

6. NIFSTD DESIGN PRINCIPLES
• Single Inheritance for Named Classes
– Follows simple inheritance principle for named
classes
– An asserted named 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
 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
Two example bridging modules in NIFSTD

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
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14. Class name Asserted necessary conditions
Cerebellum Purkinje cell 1. Is a ‘Neuron’
2. Its soma lies within 'Purkinje cell layer of cerebellar cortex’
3. It has ‘Projection neuron role’
4. It uses ‘GABA’ as a neurotransmitter
5. It has ‘Spiny dendrite quality’
Class name Asserted defining (necessary & sufficient) expression
Cerebellum neuron Is a ‘Neuron’ whose soma lies in any part of the
‘Cerebellum’ or ‘Cerebellar cortex’
Principal neuron Is a ‘Neuron’ which has ‘Projection neuron role’, i.e., a
neuron whose axon projects out of the brain region in
which its soma lies
GABAergic neuron Is a ‘Neuron’ that uses ‘GABA’ as a neurotransmitter
ONTOLOGY – ASSERTED HIERARCHY
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15. NIF CONCEPT-BASED SEARCH
• Search Google: GABAergic neuron
• Search NIF: GABAergic neuron
– NIF automatically searches for types of
GABAergic neurons
Types of GABAergic
neurons

16. NIFSTD CURRENT VERSION
• Key feature: Includes useful defined concepts to
infer useful classification
NIF Standard Ontologies 16

17. NIFSTD AND NEUROLEX WIKI
• 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
17
Stephen D. Larson et al.

18. NeuroLex vs.NIFSTD
NeuroLex NIFSTD
A semantic mediawiki based website
containing the content of the NIFSTD
plus additional community contributions
Collection of cohesive, unified modular
ontologies deployed in OWL
Categories Classes
Content is fluid and can be updated at
any time.
Structure is based on OBO foundry
principles
Defines relationships between
categories as simple properties
Defines relationships between classes as
OWL restrictions derived from RO
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
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
Larson et. al
NIFSTD
NEUROLEX

20. http://neurolex.org/wiki/Special:ContributionScores
NEUROLEX WIKI CONTRIBUTIONS

22. NIFSTD/NEUROLEX CURATION WORKFLOW
‘has soma location’ in NeuroLex == ‘Neuron X’ has_part some (‘Soma’ and
(part_of some ‘Brain region Y’)) in NIFSTD

23. ACCESS TO NIFSTD CONTENTS
• NIFSTD is available as
– OWL Format
http://ontology.neuinfo.org
– RDF and SPARQL Endpoint
http://ontology.neuinfo.org/spar
ql-endpoint.html
• Specific contents through web
services
– http://ontology.neuinfo.org/onto
quest-service.html
• Available through NCBO Bioportal
– Provides annotation and mapping
services
– http://bioportal.bioontology.org/
NIF Standard Ontologies 23

24. 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
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25. 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.
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27. 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?