Defined versus Asserted Classes: Working with the OWL Ontologies
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This document discusses defined versus asserted classes in ontologies and provides examples from the Neuroscience Information Framework (NIF) Standard (NIFSTD) ontologies. It explains that NIFSTD uses single inheritance for asserted class hierarchies but allows multiple inheritance through logical definitions and automated reasoning. It provides examples of defined neuron classes based on neurotransmitter or brain region and discusses how bridge files link different ontology modules while allowing custom restrictions.
![9
PersonhasChildPersonParent .
))](),(()()(:[ yPersonyxhasChildyxPersonxParentFOL
FemalehasGenderPersonWoman .
FemalehasGender
PersonhasChildParentMother
.
.
PersonhasChildPersonParent .
Defined Classes
hasChild (Person, Person)
hasGender (Person, Gender)
Relations/ Properties:
DL Reasoning Example](https://image.slidesharecdn.com/2010-imam-nifstdclasses-130612142904-phpapp01/85/Defined-versus-Asserted-Classes-Working-with-the-OWL-Ontologies-9-320.jpg)
![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](https://image.slidesharecdn.com/2010-imam-nifstdclasses-130612142904-phpapp01/85/Defined-versus-Asserted-Classes-Working-with-the-OWL-Ontologies-13-320.jpg)
Defined versus Asserted Classes: Working with the OWL Ontologies
- 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
- 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