Maryann Martone & Fahim Imam NIF Webinar [www.neuinfo.org/webinars/archive.shtm] February 9, 2010

1. Defined versus Asserted
Classes: Working with the OWL
Ontologies
NIF Webinar
February 9th 2010

2. Outline
• NIFSTD ontologies in brief
• Multiple vs Single hierarchy of classes/ Asserted
vs Inferred classes/Primitive and Defined classes
• Simple inference example
• NIF’s Neuron by neurotransmitter classification
• NIF’s Neuron by Brain region classification
• Bridge files and modularity
• Searching Neurons through NIF’s GWT search
interface

3. NIFSTD Modules
Fig.1: The semantic domains
(in oval) covered in the
NIFSTD with some of the sub-
domains (in rectangle). Each
of the domains are covered
by a separate OWL module
Overview. Constructed based on the best practices closely followed by the Open
Biomedical Ontologies (OBO) community
• Built in a modular fashion, covering orthogonal neuroscience domain
• e.g. anatomy, cell types, techniques etc.
• promotes easy extendibility
• Avoids duplication of efforts by conforming to standards that promote reuse
• Modules are standardized to the same upper level ontologies
• The Basic Formal Ontology (BFO), OBO Relations Ontology (OBO-RO),
and the Ontology of Phenotypical Qualities (PATO)

4. Ontology
• Adopted to CS by AI community as “explicit
specification of conceptualization” (T. Gruber)
– Organizing the concepts involved in a domain to a
hierarchy and
– Precisely specifying how the concepts are inter-
related with each other
• Explicit knowledge are asserted but implicit
consequences should rely on reasoners

5. OWL-DL
• NIFTSD ontologies are represented in OWL-DL language
– Standard language defined by (W3C)
– Largely influenced by Description Logics
• Decidable fragment of First Order Logic
– Useful reasoning services from common reasoner such as Pallet,
Racer Pro, Fact++ etc.
• Automatic Subsumption/ Classification
• Consistency checking
• Using a reasoner to classify the class hierarchy is a powerful
feature of building an ontology using the OWL-DL

6. Asserted vs. Inferred classes
• NIFSTD chose single inheritance principle
– Class hierarchies are constructed as a simple tree
– Asserted hierarchy (manually created hierarchy) should have only one super
class. It keeps the classes univocal and avoids ambiguity
– By ‘asserted hierarchy ’ we would mean a hierarchy that represents a
universal facts in the BFO sense
– OBO foundry recommendation
• We are aware that there are cases where multiple parents are required.
– Example: the universal fact about ‘Purkinje cell’ can be that it is a kind of
‘Neuron’. However, the same cell can have more specific views such as it’s a
‘GABAergic neuron’ or it’s kind of a ‘Cerebellum neuron’.
• Single inheritance is often misunderstood to mean that you can only have
a single parent
– Multiple parents can actually be derived/ inferred in a logical way
– Rely on automated reasoning to compute and maintain multiple inheritence

7. Asserted vs. Inferred classes
• Reasoners can keep the hierarchies in a
maintainable and logically correct state
• Provides a logical and intuitive reason as to how a
class X may exist in multiple/different hierarchies
• Saves a great deal of manual labor
• Minimizes human errors as well
• Keeps the ontology in a maintainable and
modular state
• Promotes the reuse of the ontology by other
ontologies and applications

8. Primitive and Defined Classes
– Primitive classes
• Has a set of necessary conditions
– Defined classes
• Has a set of necessary and sufficient restrictions; defined
by equivalent statement in OWL.
– Automated classification is possible on defined
classes through reasoners

9. 9
PersonhasChildPersonParent .
))](),(()()(:[ yPersonyxhasChildyxPersonxParentFOL
FemalehasGenderPersonWoman .
FemalehasGender
PersonhasChildParentMother
.
.
PersonhasChildPersonParent .
Defined Classes
hasChild (Person, Person)
hasGender (Person, Gender)
Relations/ Properties:
DL Reasoning Example

10. 10
DL Reasoning Example

11. NIF’s Neuron Classifications
• List of NIF neurons in NeuroLex (wiki version of NIFSTD)
• http://neurolex.org/wiki/Category:Neuron
• We wanted to classify the neurons based on their
Neurotransmitter and also based on their soma location in
different brain regions
– Neuron by Neurotransmitter
• http://neurolex.org/wiki/Neuron_by_neurotransmitter
– Neuron by region
• http://neurolex.org/wiki/Neuron_by_region

12. Bridge files
NIF-
Molecule
NIF-
Anatomy
NIF-Cell
NIF-
Subcellular
NIFSTD
NIF-Neuron-NT-Bridge.owl
NIF-Neuron-BrainRegion-Bridge.owl
• Cross-module relations among classes are assigned in a separate bridging
module.
• Allows different users to assert their own restrictions in a different
bridge file without worrying about NIF-specific view of the restriction on
core modules.

13. Neuron by Neurotransmitter
Classification
• Based on NeuroLex wiki contributions by NIF cell working group, a
bridge file has been constructed between NIF-Cell and NIF-
Molecule
– Assigned relation between a neuron and its neurotransmitter
– Defined classes to generate an inferred classifications of Neurons by
their neurotransmitters (e.g., GABAergic neurons, Glutamatergic
neurons etc.)
– Currently using a ‘macro’ relation called ‘has_neurotransmitter’.
• This relation will be further defined in terms of other obo relations to
associate other intermediate concepts
• Ex: x has_neurotransmitter y <=> x has_disposition some (realized_as some
(GO:synaptic_transmission and has_participant some (y and has_role
neurotransmitter_role))); [As proposed by Chris Mungall]
– Bridge file location:
http://ontology.neuinfo.org/NIF/BiomaterialEntities/NIF-Neuron-NT-
Bridge.owl

14. Neuron by Brain Region Classification
• We’ve created another bridge file based on NeuroLex
contributions
– Assigns relations between a neuron and its soma location in
different brain regions
– Defined Neurons based on their brain region, e.g., Hippocampal
neuron, Cerebellum neuron, Neocortical neuron etc.
– We have a ‘macro’ relation ‘has_soma_location’ and
corresponding actual relation:
• x has_soma_location y <=> ‘neuron_type_x’ has_part some ('somatic
portion' and (part_of some brain_region_y));
• Location of the Bridge file:
http://ontology.neuinfo.org/NIF/BiomaterialEntities/NIF-
Neuron-BrainRegion-Bridge.owl

15. Example Neurons with Necessary
Restrictions

16. Defined Neuron Classes Example

17. Demos in Protégé

18. Neurons through NIF GWT
http://nif-apps-stage.neuinfo.org/nif/nifgwt.html

19. Acknowledgement
• NIF-Cell working group: Giorgio Ascoli ,
Gordon Shepherd, Sridevi Polavar, Stephen
Larson, MaryAnn Martone