Stuart Aitken Artificial Intelligence Applications Institute
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
Stuart Aitken
Artificial Intelligence Applications Institute
A Process Ontology for A Process Ontology for Cell BiologyCell Biology
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
OutlineOutline
• Rapid Knowledge Formation (RKF) Project– RKF Project goals and domain– The Cyc knowledge based-system– RKF Tools
• Process Ontology– General approach– Formalisation– Example
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
Rapid Knowledge FormationRapid Knowledge Formation
• The RKF project aims to develop tools which will allow domain experts to enter knowledge directly into the KBS.
• DARPA-funded, two teams:– CYCORP– SRI
• Organised around ‘Challenge Problems’ – Cell Biology
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
RKFRKF
Aim: To enable biologists to construct an ontology/KB from a textbook source
formalise
Ontology
Alberts et al, Essential Cell Biology, 1998
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
Rapid Knowledge FormationRapid Knowledge Formation
Key techniques:• The KBS has knowledge of the KA
process– Knowledge of salience– Knowledge of the requirements of an
adequate formalisation
• There is a dialogue between expert and system, which clarifies the concept being defined.
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
Rapid Knowledge FormationRapid Knowledge Formation
Evaluation:
After a period of tool development,• trials are organised, both• expert performance, and• KE performance is measured,• and assessed independently.
The evaluation is extensive – over a period of 2 weeks
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
The Cyc KBSThe Cyc KBS
• Cyc (Doug Lenat) is a knowledge-based system, under development since ~1984, aiming to represent common sense knowledge.
• Cyc uses a large upper-level ontology
• Uses a logical language based on first-order logic
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
The Cyc KBSThe Cyc KBS
Concepts in the Upper Ontology:– Thing, Agent, Event– TangibleThing, InformationBearingObject– …. Dog, Book– subclass(genls), instance-of(isa)– parts, subevent, role predicates– 1600 concepts in total in the public
release (1998) - small% of Cyc
Classification:– Stuff-like vs Object-like– Individual vs Set
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
The Cyc KBSThe Cyc KBS
• The upper-ontology supports application development:
Upper-level
Intermediate-level
Application-level
Thing
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
The Cyc KBSThe Cyc KBS
Cyc includes:• An inference engine, • GUI, • tools for ontology development.• Until the RKF project, ontology
development was by trained knowledge engineers, working with domain experts.
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
RKFRKF
New tools in Cyc:• Define a new concept, and place it
correctly in the ontology• Refine a concept definition• Define a new predicate• Assert a new fact• Define a new rule• State an analogy• Construct a new process
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
RKFRKF
User interaction:• Selection of items in the interface
– Choice determined ‘intelligently’, KBS has knowledge of salience, and the KA process, this knowledge must be authored
• Browsing of the ontology• Search• Natural language dialogue
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
Process ModelsProcess Models
BindsTogether Move
RNA Transcription
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
Process DescriptorProcess Descriptor
Q: Name the processA: [ RNA Transcription ]Q:Select the type of Process that describes
the category best• event localised• creation or destruction event…• ‘say this:’[ _ _ _ _ _ _ ]Q: Define:• affected object: [ _ _ _ _ _ ]• location: [ _ _ _ _ _ ]• actor: [ _ _ _ _ _ ]
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
Process ModelsProcess Models
Describing Processes:• Complex expressions at the instance level• Simpler to describe in terms of types
Upper-level
Intermediate-level
subevent(Event,Event)doneBy(Event,Agent)
ForAll ?E ?F ?G implies(subevent(?E,?G) and isa(?E,BindsTogether)subevent(?F,?G) and isa(?F,Move))before(startOf(?E),startOf(?F))
Application-level?
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
Script VocabularyScript Vocabulary
The Script theory defines the semantics of Type-Level assertions
(typePlaysRoleInScene RNATranscription DNAMolecule BindsTogether objectActedOn)
• Requires rules for identity– Can require complex reasoning
• Good for user input• Can be extended to cover pre and
postconditions of actions
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
ScriptsScripts
subevents
BindsTogether
e
Move
f
RNA Transcription
Forall subevents f of t, of type Move,and all subevents e of t, of type BindsTogether,(startsAfterStartingof f e) where t is of type RNATranscription
t
startsAfterStartingOfInScript
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
ScriptsScripts
Type playing role
N
BindsTogetherNucleotide
e
Types:
objectActedOn
Instance:
For some n in N, (objectActedOn e n)
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
New Script VocabularyNew Script Vocabulary
• Pre and Post conditions
BindsTogether
N
R
N
RnottouchingDirectly connectedTo
(preconditionOfScene-negated BindsTogether touchingDirectly <Ribonucleotide Nucleotide>)
(postconditionOfScene BindsTogether connectedTo <Ribonucleotide Nucleotide>)
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
New Script VocabularyNew Script Vocabulary
N R
Some ?n in N, some ?r in R(not(touchingDirectly ?n ?r))
Some ?n in N, some ?r in R(connectedTo ?n ?r)
BindsTogetherNucleotide Ribonucleotide
e
Types:
roleroleSet ofInstances:
Precondition: Postcondition:
identity
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
Script VocabularyScript Vocabulary
• The Script vocabulary forms an ‘intermediate level’, which
• lies behind the Process descriptor GUI (i.e. the textboxes)
• Not, in itself, a taxonomy of processes, but allows processes to be described in detail.
• Defining the subclass relation is just one task.
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
Vaccinia Virus Life CycleVaccinia Virus Life Cycle
• The vaccinia virus life cycle was selected as an example of a complex model to formalise as a set of Scripts.
• The model includes actions, decomposition, ordering, objects-playing-roles and pre/postconditions
• It is a good test for the Script vocabulary
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
Vaccinia Virus Life CycleVaccinia Virus Life Cycle
mRNATranscription-Early
ViralGeneTranslation-Early
MovementOfProtein
Temporal:
mRNATranscription-Early
ViralGeneTranslation-Early
MovementOfProtein
mRNATranscription-Early
ViralGeneTranslation-Early
MovementOfProtein
Participants
Conditions:
Outputs:messengerRNA
Inputs:messengerRNA
Pre:spatiallySubsumes Cell VirusCore
Post:spatiallySubsumes CellCytoplasm Vitf2
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
EvaluationEvaluation
• 8 biologists were selected, and trained in the tools, 4 per team
• The knowledge to be formalised was selected (chapter 7 in Alberts)
• The knowledge base was allowed to contain ‘pump-priming’ knowledge
• The biologists entered knowledge , using the tools, then tested it against a set of questions,
• Ontology/KB was revised
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
EvaluationEvaluation
Results (outline)• A huge amount of data was collected,
but analysis is complex (IET Inc)• Domain experts were able to develop
ontologies after ‘light’ training• Knowledge engineers out-perform
domain experts in ontology construction
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Artificial Intelligence Applications InstituteCentre for Intelligent Systems and their Applications
SummarySummary
‘Power Tools’ for ontology development are being implemented and tested in the RKF project.
• A Script/Process vocabulary has been developed and applied to processes in cell biology, covering:– Temporal order– Participants– Pre/postconditions– Repetition