Invited talk at an ontology meeting at harwell 2003

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Communities building ontologies:
Tensions and Reality
Robert StevensUniversity of Manchester
Robert.stevens@cs.man.ac.uk

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Introduction
• CS & domain experts
• Why do CS people go on about semantics?
• CS version of building ontologies
• Some reality
• Resolving some tensions

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Two Communities
Computer Scientists
Building ontologies
KR
Reasoning
Better Ontologies
Biologists
Ontology content
Domain Knowledge

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So what Counts as an ontology?
[Deborah McGuinness, Stanford]
Catalog/
ID
Thesauri
Terms/
glossary
Informal
Is-a
Formal
Is-a
Formal
instance
Frames
(properties)
General
Logical
constraints
Value
restrictions
Disjointness,
Inverse, partof
Gene Ontology
Mouse Anatomy
EcoCyc
PharmGKB
TAMBIS
Arom

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Semantics
– An operational semantics for a language is
defined by what a sentence in that language will
do.
– Denotational semantics is a precise mathematical
definition of the objects and relations of
language in which each sentence of the language
names, or denotes, a mathematical object, such as
a function.
– Natural semantics are the loose ordinary language
sense, in which the semantics of a statement is
its "meaning".
– The term logicist semantics refers to formal
models that attempt to represent the natural
semantics of some external domain.

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Malate Dehydrogenase Activity
• malate dehydrogenase activity
• GO:0016615
• definition: Catalysis of the reversible
conversion of pyruvate or oxaloacetate to
malate using NAD or NADPH.

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What are We Saying?
Person
WomanMan
is-ais-a
•Are all instances of Man instances of Person?
•Can an instance of Person be both a Man
and an instance of Woman?
•Can there be any more kinds of Person?

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What are we saying?
• What kinds of class can fill “has chromosome”?
• How many “Y chromosome” are present?
• Does their have to be a “Y chromosome”?
• What properties are sufficient to be a Man and which are simply
necessary?
Y chromosomeMan
has chromosome
Y chromosomeMan
has chromosome
X chromosome
has chromosome
autosome
has chromosome
1
1
44

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What Does the Computer Know?
#1234
#5678#9101
#1121#1121
•Knows that all instances of #5678 are members of
#1234
•Knows that #5678 & #9101 are disjoint
•Knows that #5678 & #9101 are the only kinds of
#1234

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malate dehydrogenase
class malate dehydrogenase defined
subClassOf enzymatic_function
restriction onProperty has_reagent_on_side_A has-class malate
restriction onProperty has_reagent_on_side_B has-class oxaloacetate
restriction onProperty has_reagent_on_side_A has-class NADP anion
restriction onProperty has_reagent_on_side_B has-class NADPH
restriction onProperty catalyses has-class
((reducing and (restriction onProperty acts_on has-class NADP))
and (oxidising and (restriction onProperty acts_on has-class malate)
and (restriction onProperty acts_on_donar_group has-class CH-OH group)))
restriction onProperty catalyses has-class
((reducing and (restriction onProperty acts_on has-class oxaloacetate))
and (oxidising and (restriction onProperty acts_on has-class NADPH)))
restriction onProperty catalyses to-class
(((reducing and (restriction onProperty acts_on has-class NADP)
and (oxidising and (restriction onProperty acts_on has-class malate)
and (restriction onProperty acts_on_donar_group has-class CH-OH group)))
or ((reducing and (restriction onProperty acts_on has-class oxaloacetate))
and (oxidising and (restriction onProperty acts_on has-class NADPH))))

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Building Ontologies
• No field of Ontological Engineering equivalent to Knowledge or
Software Engineering;
• Developing standard methodologies for building ontologies;
• Such a methodology would include:
– a set of stages that occur when building ontologies;
– guidelines and principles to assist in the different stages;
– an ontology life-cycle which indicates the relationships among
stages.
• Gruber's guidelines for constructing ontologies are well known.

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The Development Lifecycle
• Two kinds of complementary methodologies emerged:
– Stage-based, e.g. TOVE [Uschold96]
– Iterative evolving prototypes, e.g. MethOntology [Gomez Perez94].
• Most have TWO stages:
– Informal stage
• ontology is sketched out using either natural language
descriptions or some diagram technique
– Formal stage
• ontology is encoded in a formal knowledge representation
language, that is machine computable
• An ontology should ideally be communicated to people and
unambiguously interpreted by software
– the informal representation helps the former
– the formal representation helps the latter.

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A Provisional Methodology
• A skeletal methodology and life-cycle for building ontologies;
• Inspired by the software engineering V-process model;
• The overall process moves through a life-cycle.
The left side
charts the
processes in
building an
ontology
The right side
charts the
guidelines, principles
and evaluation used
to ‘quality assure’
the ontology

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Methodology
Conceptualisation
Integrating existing
ontologies
Encoding
Representation
Identify purpose and scope
Knowledge acquisition
Evaluation: coverage,
verification, granularity
Conceptualisation
Principles: commitment,
conciseness, clarity,
extensibility, coherency
Encoding/Representation
principles: encoding bias,
consistency, house styles
and standards, reasoning
system exploitation
Ontology in Use
User Model
Conceptualisation Model
Implementation Model
Ontology Learning
Maintenance

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An Ontology Building Life-cycle
Identify purpose and scope
Knowledge acquisition
Evaluation
Language and
representation
Available
development
tools
Conceptualisation
Integrating
existing
ontologiesEncoding
Building
Ontology Learning
Consistency
Checking

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Some Reality & Tensions
• Conceptualisation & encoding conflated
• Encoding rarely driven by system requirements
• Very difficult to re-use other ontologies
• Not really sequential stages, but overlapping
• Little support for communal development and comment

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More Reality & Tensions
• The community must need the ontology
• The community must build the ontology
• There is no one true ontology
• Don’t wait for completeness
• Don’t wait for correctness
• Simple representation gives rapid start
• DL too difficult to use

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More Tips from GO
• Policy for updates of concept labels, Ids and definitions
• Separate concept labels and Ids
• Track obselete terms: “replaces” and “replaced by”
• Record provenance of term’s origins
• Be open

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Some Solutions
• Simple representation often unsustainable
• DL gives better sustainability
• Need domain experts to build, but ontologist to refine
• DL representation bad for users: Need familiar representation
• Reconciling simplicity and sustainability
• Applying CS knowledge appropriately

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Migration path
OWL ontologyDAG like ontology
i. Semantic diff
ii. Delivery in familiar format
iii. Consistent and taxonomically complete
iv. Property based and reasonable
i. Communal knowledge acquisition
ii. Provenance of acquisition
iii. Communal commentary
iv. Consensus
i. Report changes
ii. Semantic diff
Communal ontology development

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Things, Symbols & Concepts
• Humans require words (or at least symbols) to communicate
efficiently. The mapping of words to things is only indirectly possible.
We do it by creating symbols that stand for things.
• The relation between symbols and things has been described in the
form of the meaning triangle:
“Jaguar“
Concept
[Ogden, Richards, 1923]