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Description Logic for the support of “Ontologies” for Description Logic for the support of “Ontologies” for Clinical Terminology and Information Systems:Clinical Terminology and Information Systems:

Some ways in which they are and are not usefulSome ways in which they are and are not useful

Alan RectorAlan RectorInformation Management Group / Bio Health Informatics ForumInformation Management Group / Bio Health Informatics Forum

Department of Computer Science, University of ManchesterDepartment of Computer Science, University of Manchester

rector@cs.man.ac.uk rector@cs.man.ac.uk co-ode-admin@cs.man.ac.ukco-ode-admin@cs.man.ac.uk

www.co-ode.orgwww.co-ode.orgprotege.stanford.orgprotege.stanford.orgwww.opengalen.orgwww.opengalen.org

www.clinical-escience.orgwww.clinical-escience.org

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Description Logics, Ontologies and Description Logics, Ontologies and Healthcare Information systemsHealthcare Information systems

• The issue is not the expressivity of DLs per se– For that ask Franz Baader, Uli Sattler, Ian Horrocks, …

• The issue is how DLs support the requirements of Health Information Systems– Communication between

• humans and humans

• humans and machine

• machine and machine

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What roles might “Ontologies” and What roles might “Ontologies” and DLs playDLs play• In

– Electronic Health Records (EHRs, EPRs, …)– Messaging– Decision support– Reference Resources– Knowledge Management

• Supporting tasks for ‘faithful’ information– capture– storage– retrieval– communication– integration– indexing– presentation– inference– annotation

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Healthcare Information RepresentationHealthcare Information RepresentationA Mechanism for collaboration and sharingA Mechanism for collaboration and sharing

• Model of Meaning– How information is to be retrieved and used

• What inferences can be drawn from the information

• Model of use– What information is needed when –

What information needs to be “to hand”

– What information goes together in argument and dialogue

• Model of presentation– How to say it / How to make it understood

• in language, pictures, sound, animation, …

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““Ontologies”, DLs and Health ITOntologies”, DLs and Health IT

• “Ontologies” – A word borrowed from philosophy to describe one part of

information systems

– The catalogue of entities about which we need to record information

• The model of encapsulations– Information ‘chunks’

» IT people often also borrow the word “concept” from cognitive science

– A part of software engineering• Informed by, but different from, ‘ontologies’ as understood by

philosophy

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Healthcare IT: A special caseHealthcare IT: A special caseAn unusual factoringAn unusual factoring

• Separate development by different groups of :– Information Model –

• HL7, CEN WG1, ISO WG1,…– Models for Records and Messages overlap

– Terminology / “Ontology” • And associated “Reference Information Resource

– ICD 9/10 (CM), SNOMED-CT, NANDA, UMLS, – OBO (GO, MGED, SAEL), OMIM, SWISPROT, …

– Decision support model• Guidelines, Drug warnings, …

– Model of inference over individuals

– Knowledge management model• NHS Knowledge, Drug Databases, BMJ evidence, PubMed,

Resource discovery, …

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Metadata

Metadata

interface

interface

interface

Information Model(EHR Model,Archetypes)

Inference Model(Guideline Model)

‘Contingent’ Domain Knowledge

Metadata

Concept System Model(‘Ontology’)

Prototypical & Background Domain knowledge

Individual Patient Records

Metadata

Concept System Model(‘Ontology’)

Prototypical & Background Domain Knowledge

Metadata

Concept System Model(‘Ontology’)

Prototypical & Background Domain Knowledge

Just one part of a Health IT System

It is easy to forget what it is for

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Even of the “reference information Even of the “reference information resource” it is just one partresource” it is just one part

• The “ontology”– All consistent worlds

• Open world

• Language

• Coding and classification

• Prototypical / default information

• “Accidental” information – This world / closed world, e.g. licensing

• Indexing of external resources– “Knowledge management”

• …

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What do we mean by What do we mean by ‘Description logics’ in this context?‘Description logics’ in this context?

• The “DL fragment” of Logic with two variables (L2)?– With “standard reasoning”?– With “non-standard reasoning”?

• Description logics for which complete algorithms are possible?

• Description logics with complete algorithms implemented?– that scale to 10,000 classes? to 100,000 classes?

• Restricted DLs designed for scaling?

• OWL? – Lite? DL? or Full?

• The fragment of OWL-DL for which reasoners exist today? – – For which reasoners are promised?

• e.g. including numbers, strings, and datatypes (concrete domains)

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DLs are an implementation FormalismDLs are an implementation Formalism

• An “assembly language” for which we have“compilers”

• Compared to full FoL may need “alternative axiomitizations”– analogous to coordinate transformations

• For users and applications buildings may need “pre-compilers” / “intermediate representations” to be– a) usable

– b) sufficiently expressive

• Easy to confuse – Ontological principle

– DL implementation tricks

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Focus on two use casesFocus on two use cases

• Cooperation on model of meaning for terminology– Separate from

• Model of use

• Model of presentation (language)

• Model of inference over individuals (decision support)

– Including interface/coordination with EHR Schema

• Cooperation on Indexing of resources– Knowledge

• Close coupled knowledge - reference knowledge resource

• External knowledge resources – the semantic web

– Classifications• ICD, CPT, OPCS, NANDA, BNF & related, …

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Tasks & DesiderataTasks & Desiderata• Authoring and maintenance

– Parsimony – compositionality– Non-ambiguity– Consistent classification – accurate depiction of meaning– Constraints

• “What is it meaningful to say?”

– Modularity– Locality

• All changes made in exactly one place

– Fractal extensibility & multiple granularities– Multiple views, contexts and transformations

• Disambiguation and recognition of equivalence– When two terminology constructs are equivalent/subsuming– When two combined terminology and ehr constructs are equivalent/subsuming

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When are inferences relevantWhen are inferences relevant

• At schema creation / design time– Meta-ontology

• At authoring / configuration time– The ontology proper

– To support collaboration

• At run time– Is “pre-coordination” enough?

– Is “post coordination” necessary?

– Interface with healthcare record model?

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Three levels of ontologyThree levels of ontology• The meta-ontology – schema creation or design time

– e.g. Classic merology and merotopology• The meaning of the relations to be represented in the “ontology”

– Properties of entities • Identity, rigidity, etc. e.g.

– Formal Ontology, Ontoclean,…

• The upper ontology – categories of individuals in general - Author time– Possible precoordinated notions:

Cell, Organism, Pathology, Disease, Metabolism, …

• The domain ontology – categories of individuals relevant to biomedicine – Run time– Possible pre-coordinated notions:

Human organism, Red cell, Inflammation, Pneumonia, Diabetes, Fracture of trochanter of left femur

Ontology Layers: What’s it for?Ontology Layers: What’s it for?

CooperationCooperation on the on the Domain ContentDomain Content Ontologies Ontologies to enable…to enable…

CooperationCooperation on on TopTop Domain OntologiesDomain Ontologies to enable…to enable…

CooperationCooperation on the on the Upper OntologiesUpper Ontologies to enable …. to enable ….

The The Meta OntologyMeta Ontology is to enable… is to enable…

CooperationCooperation on on Information systems & Information systems & resources resources

InformationInformation systems & systems & resources resources

Databases, RDFDatabases, RDFInstance stores, …Instance stores, …(“individuals”)(“individuals”)

Where do DLs fit in?Where do DLs fit in?

Domain Content Ontologies

TopTop Domain Domain OntologiesOntologies

Upper OntologiesUpper Ontologies

DLs?DLs?(“classes”)(“classes”)

Meta OntologyMeta Ontology FoL /FoL /HoLHoL

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Reformulate the question:Reformulate the question:What functions can DLs serve for the Upper, Top What functions can DLs serve for the Upper, Top

Domain and Domain Content Ontologies?Domain and Domain Content Ontologies?

• Preliminary answer for Domain Content “Ontologies”– the ‘units of encapsulated information’ to be held by

information models and used as cognitive units by domain experts (Doctors, nurses & other HCPs)

• Even simple DLs work to improve quality and reduce effort of maintenance

– GALEN

– SNOMED-CT

– GO/GONG

– MGED

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Reformulate the question:Reformulate the question:What functions can DLs serve for the Upper,What functions can DLs serve for the Upper,

Top Domain, and Domain Content Ontologies? Top Domain, and Domain Content Ontologies?

• Parsimony / compositionality – Existential graphs plus some rewriting

• Dictionary + grammar instead of a phrase book

• Non-ambiguity / recognition of equivalence– Within their expressive power

• But both additional human and logical meanings needed

• Consistent classification / accurate retrieval – Empirically difficult (impossible) to achieve manually for non-

trivial systems• Improvement using DLs proportional to connectivity of ontology,

i.e. number of axes, number of parents / entity

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What DLs can do for domain “ontologies”What DLs can do for domain “ontologies”

• Constraints ?– What is NOT meaningful to say

• What is meaningful to say requires non-standard reasoning– Not the complement of what it is not sensible to say

• Modularity?– Only if ontology well normalised

• Locality ?– Only if ontology well normalised &

well supported by tools• Notorious for non-local effects and propagation of errors

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What DLs can do for domain “ontologies”What DLs can do for domain “ontologies”

• Fractal extensibility & multiple granularities

• Multiple views, contexts and transformations ?– Strong logical basis but still a research topic

• Recognition of equivalence– For two terminology constructs ?

• Provided patterns are adhered to rigorously - “canonical forms”– Or with non-standard reasoning

• Provided within DL fragment

– Combined ontology and EHR constructs ?• Only if ontology-EHR relationship is well defined

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Practical examplesPractical examplesStraightforward constructionsStraightforward constructions

• Composition with modifiers– Laterality and position – left / right hand

– Causes of diseases - bacterial / viral pneumonia

– Extent of injury – depth / size of burn

• Composition with modality– Family history of … , Risk of… etc

• Site of disease or injury– With rewriting to deal with part-whole

• Usual pattern:“Disease of Heart” means“Disease of Heart or any of its parts”

– For a suitable definition of “its parts”

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Practical examples: Limitations on AxiomsPractical examples: Limitations on Axioms

• If the whole has a laterality, then all its parts can only have the same laterality– Outside L2 – therefore must be treated as an axiom

schema• If the whole has left laterality, then its parts can only have

the left laterality

• If the whole has right laterality, then its parts can only have right laterality.

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Practical examples: Limitations of AxiomsPractical examples: Limitations of Axioms

• If there is an ulcer of a segment of the GI tract, then it can only be in the mucosa of the wall of the same segment of the GI tract. – Outside L2 – therefore must be treated as an axiom schema

• Stomach ulcers can only be in the mucosa of the wall of th stomach

• Duodenal ulcers can only be in the mucosa of the wall of th duodenum

• …

– For an indefinite number of segments of the GI tract this must be automated as some additional rule mechanism

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Property/Role inclusion can helpProperty/Role inclusion can help

• has_locus o has_lining has_locus– Ulcer has_locus some Stomach

Ulcer has_locus some Mucosa_of_wall_of_stomach

• is_laterality_of o has_part has_laterality– Left is_laterality_of Arm has_part Hand

Left is_laterality_of Hand

• NB Role inclusion originally fashioned for partonomy but superseded by explicit representation disjunction: “Whole or its parts”

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Mutual constraint and contextMutual constraint and context

• Through values in compositional concepts– “Large liver”is the intersection of “having the property

large” and “liver”• Handle context naturally

– The largeness of livers need not be the same as the largeness of animals

• Through explicit context– Body & is_of some Human_organism has_part =2 legs

Body & is_of some (Mammal & not Primate & not Kangaroo_family)

has_part =4

legs.

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Temporal ContextTemporal Context(Usually implicit in EHR structure)(Usually implicit in EHR structure)

• (Patient as_observed_at is time(t)) has_condition some (Diabetes & has_state some Brittle) treated_with some (Insulin & has_type some Human)

• Patient as_observed_at is time(t)) has_condition some (Diabetes & has_state some Brittle & is_treated_by some (Insulin & has_type some Human)

……but how to represent their equivalence?but how to represent their equivalence?

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Two approximations in DLsTwo approximations in DLs

• Role Inclusion– has_condition o is_treated_by treated_with

• Not supported by current implementations although algorithms known

• Role hierarchy + post filtering– treated_with transitive

treated_with treated_by has_condition

• Some unwanted inferences but can be filtered out

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N-Ary relation solutionN-Ary relation solutionRe-represent Re-represent treated_withtreated_with as a class as a class

• (Patient at_time(t)) target_of some (Treatment AND is_with some Insulin is_for some Diabetes) has_condition some Diabetes

• No way of linking the two occurrences of “Diabetes”– Consider “Fractures” or “Tumours” or “ulcers”

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DLs often need to be supplemented by DLs often need to be supplemented by imposed constraints and patternsimposed constraints and patterns

• Only a subset of logical equivalences can be expressed– Pre-standardisation of formulation required

• Some can be done by DL constraints (Domains & Ranges)• Some requires external meta-constraints

– Usually built into tools

• Other examples:– Processes and their outcomes: Ulcer & Ulceration– Syndromes and diseases

• May have to represent all “diseases” of syndromes for consistency

– even though most have only one condition

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Patterns, Transformations & Patterns, Transformations & Interface to EHR ModelInterface to EHR Model

• DLs may provide the patterns to be transformed

• Almost certainly not the language for transformation– Unification?

– Forward chaining rule engine?

– Grammar engine

– Other?

• Possibly the language for EHR-Ontology Interface– How to make sure that things that can be said in both are

either• Consistent

• Said in only one place

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Problems in dealing with AnatomyProblems in dealing with Anatomy

• Cyclical models do not scale– anything containing both is_part_of and has_part

• Key schemas outside L2– A layer of a part of a whole is a part of the corresponding layer of the

whole• But approximation by kind of works for manypractical cases

– A rare case where “lying to the logic” works» If you do it right you must first do it and then undo it.

• But a nonsense for a rigourous version of anatomy

– Axiom schemas using universal constraints scale badly• Much worse than exponential even for trivial cases

– At least en^k perhaps en^n

• Parsimonious version of anatomy not possible in DLs without supplementary rules

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False problems: TimeFalse problems: Time“Clinical content ontology” /terminology need only “Clinical content ontology” /terminology need only

do what is not done by EHR modeldo what is not done by EHR model

• Time is normally outside clinical “ontologies”– Except that time is relevant to meta-definition of

part-of and other relations

• Clinical ontologies are of reality in the context of the view from a specific point in time

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Meta-ontology can make explicit the Meta-ontology can make explicit the implicit semantics of the domain ontologyimplicit semantics of the domain ontology

for combined “ontology” and “EHR”for combined “ontology” and “EHR”

• has_partclinical-DL = has_structural_partt-

– The variant of has_part useful in most clinical applications represented using DLs

• has_structural_part follows merologic axioms – with some limitation for granularity so that not every atom

remains forever a part of the whole

• has_structural_partt- = has_structural_part_at_time(t) & t now

Not expressible in LNot expressible in L22 & hence not in DLs & hence not in DLs

but inferences not needed at author or run timebut inferences not needed at author or run time

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Weaknesses for terminology for Weaknesses for terminology for clinical terminology and ISsclinical terminology and ISs

• Dealing with optionality or “may…”– Infection may_be_treated_with some Penicillin

• Infections treated_with some Penicillin– This infection is treated with penicillin

• Does it mean– “Some infections are treated with some penicillin”?, i.e.

“Not (all infections are not treated with any penicillin)”?• Inside the DL fragment but not implemented

– Infections may_be_treated_with some penicillin• treated_with may_be_treated_with

– just a weaker form?

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““What can I say about …?”What can I say about …?”“What qualifiers are allowed for ..”“What qualifiers are allowed for ..”

“Sanctioning”“Sanctioning”

• Really a meta logical question– At least non standard reasoning

and intended meaning unclear• For what properties are all Cs in domain? Some Cs in the domain?

• For what properties are all Cs not in the complement of the domain? some Cs not in the complement of the domain?

– Easier to ask what is excluded than what is allowed

• What is it is it sensible to say?– For queries, the more we know, the less it is sensible to ask?

– For statements, the more we know, the more it is sensible to ask?

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Scaling: TechnicalScaling: Technical

• All tableaux algorithms for expressive DL algorithms at least NP-Hard– Most worst case exponential

• Mapping the edge: what constructs cause problems– Cyclical structures

• Heart has_part Ventricle; Ventricle is_part_of Heart.– Important. Kluges available. Principled solutions needed

– Axiom schemas with universal axioms• A part of a sublayer can only be a sublayer of the corresponding

part of the layer– Probably en^3

• Need help walking the cliff– Resource constrained reasoning?

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Scaling: OrganisationalScaling: Organisational

• Training in ontology and DL logic not scalable– Domain experts have other things to do

• Formalised patterns and constraints can reduce training– In GALEN-IN-USE from 3 months to 3 days!

• Treat the DL as an assembly language;Provide tools to create tailored high level user-oriented languages plusUser oriented environments that use those languages

• Only possible because of rigorous structure– Plus a flexible means to produce grammars and manage disputes– And users must not encounter the scaling ‘cliff’

• In a Web age, could open the world to “Just in Time” “Domain Ontologies” / Terminologies / “Encapsulations”

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How expressive a DL do we really need?How expressive a DL do we really need?

• GALEN & S-CT both use only– Existential qualifiers– Restricted role inclusion axioms– GALEN

• “Absorbable” General Inclusion Axioms

– Restricted conjunction guaranteeing tree structure• One primitive per conjunct

• Could one add (in order of priority)– Numeric range constraints – Qualified min cardinality restrictions– Conjunction of primitives only– Weakened negation/disjointness and disjunction

• Can fully expressive ontologies be scaled up to work?

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Other things neededOther things needed

• Cyclic expressions– Must be able to say both scalably:

• has_part / is_part_of

• has_cause / is_cause_of

• Solution to “may” / “optionality”– Just saying that the topic and value are in the

domain and range not really enough• Distorts the semantics of the intent

– Shows up in tool implementations

• And can only answer by standard reasoning “may not” rather than “may”

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SummarySummary• Even simple DLs make a difference for “Domain Content

Ontology” – Manage Information encapsulations

• Not really relevant to Meta-ontologies– “ontology proper”?

• Patterns must be maintained rigorously if DL classification is to work

• DLs more appropriate as an “assembly language” than a user language– But make flexible user languages possible

• Many outstanding problems– Hybrid DL/Rule reasoners?– Resource constrained reasoning

• But so far the best proven scalable practical tool we have