Defined versus Asserted Classes: Working with the OWL Ontologies


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Maryann Martone & Fahim Imam
NIF Webinar []
February 9, 2010

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Defined versus Asserted Classes: Working with the OWL Ontologies

  1. 1. Defined versus AssertedClasses: Working with the OWLOntologiesNIF WebinarFebruary 9th 2010
  2. 2. Outline• NIFSTD ontologies in brief• Multiple vs Single hierarchy of classes/ Assertedvs 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 searchinterface
  3. 3. NIFSTD ModulesFig.1: The semantic domains(in oval) covered in theNIFSTD with some of the sub-domains (in rectangle). Eachof the domains are coveredby a separate OWL moduleOverview. Constructed based on the best practices closely followed by the OpenBiomedical 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. 4. Ontology• Adopted to CS by AI community as “explicitspecification of conceptualization” (T. Gruber)– Organizing the concepts involved in a domain to ahierarchy and– Precisely specifying how the concepts are inter-related with each other• Explicit knowledge are asserted but implicitconsequences should rely on reasoners
  5. 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 powerfulfeature of building an ontology using the OWL-DL
  6. 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 superclass. It keeps the classes univocal and avoids ambiguity– By ‘asserted hierarchy ’ we would mean a hierarchy that represents auniversal 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 havea single parent– Multiple parents can actually be derived/ inferred in a logical way– Rely on automated reasoning to compute and maintain multiple inheritence
  7. 7. Asserted vs. Inferred classes• Reasoners can keep the hierarchies in amaintainable and logically correct state• Provides a logical and intuitive reason as to how aclass 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 andmodular state• Promotes the reuse of the ontology by otherontologies and applications
  8. 8. Primitive and Defined Classes– Primitive classes• Has a set of necessary conditions– Defined classes• Has a set of necessary and sufficient restrictions; definedby equivalent statement in OWL.– Automated classification is possible on definedclasses through reasoners
  9. 9. 9PersonhasChildPersonParent .))](),(()()(:[ yPersonyxhasChildyxPersonxParentFOLFemalehasGenderPersonWoman .FemalehasGenderPersonhasChildParentMother..PersonhasChildPersonParent .Defined ClasseshasChild (Person, Person)hasGender (Person, Gender)Relations/ Properties:DL Reasoning Example
  10. 10. 10DL Reasoning Example
  11. 11. NIF’s Neuron Classifications• List of NIF neurons in NeuroLex (wiki version of NIFSTD)•• We wanted to classify the neurons based on theirNeurotransmitter and also based on their soma location indifferent brain regions– Neuron by Neurotransmitter•– Neuron by region•
  12. 12. Bridge filesNIF-MoleculeNIF-AnatomyNIF-CellNIF-SubcellularNIFSTDNIF-Neuron-NT-Bridge.owlNIF-Neuron-BrainRegion-Bridge.owl• Cross-module relations among classes are assigned in a separate bridgingmodule.• Allows different users to assert their own restrictions in a differentbridge file without worrying about NIF-specific view of the restriction oncore modules.
  13. 13. Neuron by NeurotransmitterClassification• Based on NeuroLex wiki contributions by NIF cell working group, abridge 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 bytheir neurotransmitters (e.g., GABAergic neurons, Glutamatergicneurons etc.)– Currently using a ‘macro’ relation called ‘has_neurotransmitter’.• This relation will be further defined in terms of other obo relations toassociate 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_roleneurotransmitter_role))); [As proposed by Chris Mungall]– Bridge file location:
  14. 14. Neuron by Brain Region Classification• We’ve created another bridge file based on NeuroLexcontributions– Assigns relations between a neuron and its soma location indifferent brain regions– Defined Neurons based on their brain region, e.g., Hippocampalneuron, Cerebellum neuron, Neocortical neuron etc.– We have a ‘macro’ relation ‘has_soma_location’ andcorresponding actual relation:• x has_soma_location y <=> ‘neuron_type_x’ has_part some (somaticportion and (part_of some brain_region_y));• Location of the Bridge file:
  15. 15. Example Neurons with NecessaryRestrictions
  16. 16. Defined Neuron Classes Example
  17. 17. Demos in Protégé
  18. 18. Neurons through NIF GWT
  19. 19. Acknowledgement• NIF-Cell working group: Giorgio Ascoli ,Gordon Shepherd, Sridevi Polavar, StephenLarson, MaryAnn Martone