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Copyright © 1997 Pangea Systems, Inc. All rights reserved.
Ontologies:
Definitions, Components,
Subtypes
Outline
Copyright © 1998 Pangea Systems, Inc. All rights reserved.
 Motivations
for ontologies in bioinformatics
 Definition
of ontology
 Principles
and pitfalls of ontology design
 GKB
Editor ontology development tool
Definition of an Ontology
Copyright © 1998 Pangea Systems, Inc. All rights reserved.
 Conceptualization
of a domain of interest
 Concepts, relations, attributes, constraints,
objects, values
 An ontology is a specification of a
conceptualization
 Formal notation
 Documentation
A
variety of forms, but includes:
 A vocabulary of terms
 Some specification of the meaning of the terms
 Ontologies
are defined for reuse
Roles of Ontologies in Bioinformatics
 Success
of many biological DBs depends on
 High fidelity ontologies
 Clearly communicating their ontologies

Prevent errors on data entry and interpretation
Copyright © 1998 Pangea Systems, Inc. All rights reserved.
 Common
framework for multidatabase queries
 Controlled
vocabularies for genome annotation
 Riley ontology, GO
 EC numbers
Roles of Ontologies in Bioinformatics
 Information-extraction
applications
Copyright © 1998 Pangea Systems, Inc. All rights reserved.
 Reuse
is a core aspect of ontologies
 Reuse of existing ontologies faster than
designing new ones
 Reuse decreases semantic heterogeneity of
DBs
 Schema-driven
Software
 Knowledge-acquisition tools
 Query tools
Definitions
Model:
 Primitive data structuring mechanism in which
an ontology is expressed
 Relational data model, object-oriented data
model, frame data model
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 Data
 Ontology:

Domain specific conceptualization expressed
within some data model
Components of an Ontology
 Concepts
AKA: Class, Set, Type, Predicate
 Gene, Reaction, Macromolecule

Copyright © 1998 Pangea Systems, Inc. All rights reserved.
 Taxonomy
of concepts
 Generalization ordering among concepts
 Concept A is a parent of concept B iff every
instance of B is also an instance of A
 Superset / subset
 “A kind of” vs “a part of”
Copyright © 1998 Pangea Systems, Inc. All rights reserved.
Taxonomy of Concepts
Components of an Ontology
 Objects
AKA: Instances, members of the set
 trpA Gene, Reaction 1.1.2.4
 Strictly speaking, an ontology with instances is
a knowledge base
 Relations and Attributes
 AKA: Slots, properties
 Product of Gene, Map-Position of Gene
 Reactants of Reaction, Keq of Reaction
 Values
 The Product of the trpA Gene is tryptophansynthetase
 trpA.Product = tryptophan-synthetase
Copyright © 1998 Pangea Systems, Inc. All rights reserved.

Components of an Ontology
 Constraints
and other meta information about
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relations
 Slot Product:
 Value type: Poypeptide or RNA
 Domain: Genes
Slot Map-Position:
 Value type: Number
 Domain: Genes
 Cardinality: At-Most 1
 Range: 0 <= X <= 100
 General Axioms


Nucleic acids < 20 residues are oligonucleiotides
More on Concepts
 Primitive:
properties are necessary
 Globular protein must have hydrophobic core,
but a protein with a hydrophobic core need not
be a globular protein
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 Defined:
properties are necessary + sufficient
 Eukaryotic cells must have a nucleus. Every cell
that contains a nucleus must be Eukaryotic.
Ontology Subtypes Expressiveness
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 Controlled
vocabulary
 List of terms
 Taxonomy
 Terms in a generalization hierarchy
 DB schemas (relational and object-oriented)
 More implementation specific
 No instance information
 Limited constraints
 Frame knowledge bases
 Description Logics
Ontology Subtypes
 Database
schema
 Concepts, relations, constraints
 Perhaps no taxonomy
 At most hundreds of concepts
Copyright © 1998 Pangea Systems, Inc. All rights reserved.
 Taxonomy
Concepts, taxonomy, perhaps a few relations
 Thousands of concepts

 Knowledge
base
 Concepts, relations, constraints, objects, values
 Hundreds to hundreds of thousands of
concepts and objects
Ontology Subtypes
(a.k.a. upper, core or reference)
 common high level concepts
 “Physical”, “Abstract”, “Structure”, “Substance”
 useful for ontology re-use
 important when generating or analysing natural
language expressions
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 Generic
 Domain-oriented
domain specific (e.g. E.coli)
 domain generalisations (e.g. gene function)
 Task-oriented
 task specific (e.g. annotation analysis)
 task generalisations (e.g. problem solving)
