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OntologiesOntologies

Prof. Steven A. Demurjian, Sr.Computer Science & Engineering Department

The University of Connecticut371 Fairfield Road, Box U-255

Storrs, CT 06269-2155

steve@engr.uconn.eduhttp://www.engr.uconn.edu/

~steve(860) 486 - 4818

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MotivationMotivation Ontologies – Biomedical and ClinicalOntologies – Biomedical and Clinical

What are they? How are they Used?

What is Issue Facing Ontologies in Future?What is Issue Facing Ontologies in Future? Each HIT System has its Own Ontology HIE Requires

Integration of Patient Data Dealing with Semantic Differences (one EMR has

weight in lbs, one in kg) Reconciling Ontologies

– Each HIT System with Ontology for Same Info

– Ontology + Data Impacts Integration

– How do we Resolve Dramatic Differences?

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Placing Ontologies into PerspectivePlacing Ontologies into Perspective Historical Evolution of WWWHistorical Evolution of WWW OntologyOntology

Definition and Description RDF and OWL

Present Biomedical OntologyPresent Biomedical Ontology Applications of Biomedical OntologiesApplications of Biomedical Ontologies

Clinical Trials OASIS: Integration Technique Clinical Decision Support System

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Current Information Systems on WWWCurrent Information Systems on WWW First Generation: First Generation:

Raw data which was pretty much hand-coded by the user was published online

For example, Static web pages Second Generation: Second Generation:

Dynamic content generation driven by MDA and databases

Machines generate the respective HTML Third Generation: Semantic Web: Third Generation: Semantic Web:

Generating machine processable information where the content is machine understandable, enabling intelligent services such as information brokers, search agents, information filters to process domain related information.

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What are Ontologies?What are Ontologies? Definition (from Philosophy) :Definition (from Philosophy) :

Ontology is study of being or existence and forms the basic subject matter of metaphysics. It seeks to describe the basic categories and relationships of being or existence to define entities and types of entities within its framework.

Definition (from Computer Science):Definition (from Computer Science): In Computer science , Ontology means

“specification of a conceptualization”.It means “A data model that represents a set of concepts within a domain and the relationships between those concepts”.

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Advantages of OntologyAdvantages of Ontology Semantic way of representing knowledge of the Semantic way of representing knowledge of the

domaindomain Intelligent system can provide reasoning Systems to Intelligent system can provide reasoning Systems to

make inferences within the Ontologymake inferences within the Ontology Two main ObjectivesTwo main Objectives

Share the common structure of information Reuse the similar ontology in another domain

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Development of OntologyDevelopment of Ontology Determine the domain and Scope (Range) of the

knowledge Look for an existing ontology in the similar domain

Reuse without change (will it be possible?) Basis to evolve to domain-specific solution

Listing all of Terminologies or Concepts of domain List all of classes and instances to be created in the

ontology Create the properties which will relate these concepts

in the ontology

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Example of OntologyExample of Ontology

Wine Australian Yellow Tail

Color Flavor Maker

Grape

Yellow Delicate AustraliaGerman

Class Individual

Properties

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Parkinson’s Disease Management OntologyParkinson’s Disease Management Ontology

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Parkinson’s Treatment OntologyParkinson’s Treatment Ontology

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Parkinson’s Treatment OntologyParkinson’s Treatment Ontology

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Neurological-Disease OntologyNeurological-Disease Ontology

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Excerpt of Medical Condition OntologyExcerpt of Medical Condition Ontology

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Patient OntologyPatient Ontology

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Skelton OntologySkelton Ontology

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How do Ontologies Related to other Models?How do Ontologies Related to other Models? UML Model

PatientEthnicity: StringprefLang: String

race:StringEmail: Stringgender: String

getAllergies()get_clinical_notes()get_demographics()get_medications()

get_immunizations()

Observation

Id:IntegerstatusCode: String

name: Stringvalue: String

Substance

Id:Integername: String

statusCode: StringeffectiveTime:DaterepeatNumber: Int

Name

family-name: Stringgiven-name: String

prefix: Stringsuffix: String

Person

Id: Integername: name

address: Addressbday: Stringtel: String

Address

street: Stringlocality: Stringregion: Stringcountry: String

deaNumber: StringnpiNumber:StringEthnicity: String

race:StringEmail: Stringgender: String

Provider

hasMedicalObservations

takesPrescribedMedication

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How do Ontologies Related to other Models?How do Ontologies Related to other Models? Entity Relationship Diagram

Patient

idEthnicity

prefLang

race

name

address

bdaytel

Observation

id

statusCode

effectiveTime

value

Substance

idname

effectiveTime

statusCoderepeatNumber

Figure 3.3: Sample EHR Model in ERD.

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How do Ontologies Related to other Models?How do Ontologies Related to other Models? XML Schema<xs:element name=“<xs:element name=“PatientPatient">">  <xs:complexType>  <xs:complexType>    <xs:sequence>    <xs:sequence>      <xs:element name=“id" type="xs:integer"/>      <xs:element name=“id" type="xs:integer"/>      <xs:element name=“ethnicity" type="xs:string"/>      <xs:element name=“ethnicity" type="xs:string"/>      <xs:element name=“race" type="xs:string"/>      <xs:element name=“race" type="xs:string"/>

……….……….            <xs:element name=“tel" type=“xs:string"/><xs:element name=“tel" type=“xs:string"/>    </xs:sequence>    </xs:sequence>  </xs:complexType>  </xs:complexType></xs:element></xs:element>

<xs:element name=“<xs:element name=“SubstanceSubstance">">  <xs:complexType>  <xs:complexType>    <xs:sequence>    <xs:sequence>      <xs:element name=“id" type="xs:integer"/>      <xs:element name=“id" type="xs:integer"/>      <xs:element name=“name" type="xs:string"/>      <xs:element name=“name" type="xs:string"/>      <xs:element name=“statusCode" type="xs:string"/>      <xs:element name=“statusCode" type="xs:string"/>

……….……….            <xs:element name=“repeatNumber" type=“xs:integer"/><xs:element name=“repeatNumber" type=“xs:integer"/>    </xs:sequence>    </xs:sequence>  </xs:complexType>  </xs:complexType></xs:element></xs:element>

<xs:element name=“<xs:element name=“ObservationObservation">">  <xs:complexType>  <xs:complexType>    <xs:sequence>    <xs:sequence>      <xs:element name=“id" type="xs:integer"/>      <xs:element name=“id" type="xs:integer"/>      <xs:element name=“name" type="xs:string"/>      <xs:element name=“name" type="xs:string"/>      <xs:element name=“value" type="xs:string"/>      <xs:element name=“value" type="xs:string"/>      <xs:element name=“statusCode" type=“xs:string"/>      <xs:element name=“statusCode" type=“xs:string"/>    </xs:sequence>    </xs:sequence>  </xs:complexType>  </xs:complexType></xs:element></xs:element>

<xs:element name=“<xs:element name=“takesPrescribedMedicationtakesPrescribedMedication">">    <xs:sequence>    <xs:sequence>      <xs:element ref =“Patient"/>      <xs:element ref =“Patient"/>      <xs:element ref =“Substance"/>      <xs:element ref =“Substance"/>    </xs:sequence>    </xs:sequence></xs:element></xs:element><xs:element name=“<xs:element name=“hasMedicalObservationhasMedicalObservation">">    <xs:sequence>    <xs:sequence>      <xs:element ref =“Patient"/>      <xs:element ref =“Patient"/>      <xs:element ref =“Observation"/>      <xs:element ref =“Observation"/>    </xs:sequence>    </xs:sequence></xs:element></xs:element>

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How do we Model Ontologies?How do we Model Ontologies? Researchers proposed Semantic Web Stack

illustrating hierarchy of languages, where each layer exploits and uses capabilities of the layers below

OWL and RDF belong the family of knowledge representation language. RDF: Resource Description Framework

http://www.w3.org/RDF/ OWL: Web Ontology Language

http://www.w3.org/TR/owl-features/ RDF reminds of Semantic Networks which were

popular in 1970’s

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Introduction to RDF / OWL

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RDF: Resource Description Framework RDF represents the knowledge in triples format:

Subject – Predicate – Object For example,

Students – registerTo – Classes(Subject) (Predicate) (Object)

One triple is RDF is referred as a statement RDF is grammar based language has syntax similar to

XML RDFS (RDF Schema) has syntax similar to RDF and

provide schema grammar to RDF. For example, rdfs:Class, rdfs:subClassOf etc

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RDF: Resource Description Framework RDF syntax of the above example:

All the concepts described in the RDF are identified using an URI (ex. http://www.example.com/examle#Students).

RDF can be viewed as standardized framework for providing metadata to domain concepts.

<rdfs:Class rdf:about="http://www.example.com/examle#Students"rdfs:label="Students">

</rdfs:Class>

<rdfs:Class rdf:about="http://www.example.com/examle#Classes"rdfs:label=“Classes">

</rdfs:Class>

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OWL: Web Ontology LanguageOWL: Web Ontology Language OWL is placed on the top of the semantic web stack, OWL is placed on the top of the semantic web stack,

utilizing all the powerful features offered by the layers utilizing all the powerful features offered by the layers below (RDF, RDFS, XML)below (RDF, RDFS, XML)

OWL design has been influenced by description logic OWL design has been influenced by description logic & knowledge representational paradigms & knowledge representational paradigms SHIQ, Semantic Networks, Frames, SHOE,

DAML, OIL, DAML+OIL. OWL provides richer semantic capabilities than its OWL provides richer semantic capabilities than its

predecessor RDFpredecessor RDF For example, in the previous example, the

predicate registerTo is of type rdf:Property.

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OWL: Web Ontology Language OWL differentiates between properties by defining

owl:ObjectProperty – for connecting two concepts (registerTo) and

owl:DatatypeProperty - for connecting a concept to a datatype (utilized from XML)

These two properties inherit from RDF property OWL also defines owl:AnnotationProperty for

embedding metadata onto classes, rules and axioms The following slide illustrates the use of OWL, RDF

and RDFS ( taken from cardiac ontology build in OWL using protégé tool)

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<owl:Class rdf:ID="Veins"> <rdfs:subClassOf> <owl:Class rdf:ID="Heart"/> </rdfs:subClassOf> </owl:Class><Veins rdf:ID="Pulmonary_Vein"/>

Heart

Vein

Pulmonary Vein

Pulmonary Vein is sub-class of Vein which is sub-class of Heart.

The next slide illustrates the OWL properties and expressive power of OWL to restrict the domain and range values accepted by these properties.

BioMedical Informatics

OWL: Web Ontology Language

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<owl:ObjectProperty rdf:ID="Complications"> <rdfs:domain rdf:resource="#Cardiology_Diseases"/> <rdfs:range> <owl:Class> <owl:unionOf rdf:parseType="Collection"> <owl:Class rdf:about="#Cardiology_Complications"/> <owl:Class rdf:about="#Cardiology_Diseases"/> <owl:Class rdf:about="#Cardiology_Causes"/> </owl:unionOf> </owl:Class> </rdfs:range> </owl:ObjectProperty>

The object property “Complications” can take domain values from class “Cardiology_Diseases” and range values from combination of classes

OWL combined with RDF/RDFS provides an environment for developing domain ontologies by organizing and describing the domain conceptsBioMedical Informatics

OWL: Web Ontology Language

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Disease OntologyDisease Ontology

Sub-Classes of

Cardiology Diseases

Instances of Mitral_Valve_Disorders

Hierarchical organization of Cardiology Diseases

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Disease OntologyDisease Ontology

Property Defined

Representation of “Mitral_Valve_Prolapse” knowledge using properties and instances

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Implemented Ontology in Implemented Ontology in OWLOWL Format Format

…………..

<Congenital_Heart_Disease rdf:ID="Atrial_septal_defect"> <Complications> <Cardiac_Arrhythmias rdf:ID="Arrhythmia"> <Has_Intervention rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >defibrillation</Has_Intervention> <Have_Symptoms> <Cardiology_Symptoms rdf:ID="Dyspnea"/> </Have_Symptoms> <Has_Diagnosis_Test> <Cardiology_Diagnosis_Test rdf:ID="Coronary_Angiography"> <Has_Synonyms rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >coronary catheterization </Has_Synonyms> ………………..

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Bio-Medical OntologiesBio-Medical Ontologies Review a Wide Range of Available Ontologies and Review a Wide Range of Available Ontologies and

Standards:Standards: OpenCyc WordNet Galen UMLS SNOMED – CT FMA Gene Ontology

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Sample EHR Model in UML via HL7 CDASample EHR Model in UML via HL7 CDA

Visit

Id: IntegerpId: Integer

visitDate:Date

PatientProviderpId: IntegerproviderId:

Integer

patientListencounters

Name

family-name: Stringgiven-name: String

prefix: Stringsuffix: String

Person

Id: Integername: name

address: Addressbday: Stringtel: String

Address

street: Stringlocality: Stringregion: Stringcountry: String

** CD, CE, CS, IVL_TS, ANY – HL7 CDA datatypes

deaNumber: StringnpiNumber:StringEthnicity: String

race:StringEmail: Stringgender: String

Provider

hasVitalshasImmunizationRecords

perfomedProcedureshasMedicalObservations

Patient

Ethnicity: StringprefLang: String

race:StringEmail: Stringgender: String

getAllergies()get_clinical_notes()get_demographics()get_medications()

get_immunizations()

Substance Administration

Id:Integername: String

statusCode: CSeffectiveTime:IVL_TSdoseQuantity: IVL_PQ

routeCode:CErepeatNumber:ANY

Procedure

Id:Integercode: CD

statusCode: CSeffectiveTime: IVL_TSapproachSiteCode:CD

targetSiteCode: CDmethodCode: CE

ObservationId:Integer

statusCode: StringeffectiveTime: IVL_TS

code: CDvalue: ANY

targetSiteCode:CD

ImmunizationId:Integercode: CD

statusCode: CSeffectiveTime: IVL_TS

product: CDrouteCode: CD

Vitals

Id: IntegereffectiveTime:

IVL_TS

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OWL Equivalent for ObservationOWL Equivalent for Observation<owl:Class rdf:Id=“IVL_TS”/><owl:Class rdf:Id=“IVL_TS”/>

<owl:DatatypeProperty rdf:Id=“Low”/><owl:DatatypeProperty rdf:Id=“Low”/>

<owl:DatatypeProperty rdf:Id=“High”/><owl:DatatypeProperty rdf:Id=“High”/>

<owl:DatatypeProperty rdf:Id=“width”/><owl:DatatypeProperty rdf:Id=“width”/>

<owl:DatatypeProperty rdf:Id=“center”/><owl:DatatypeProperty rdf:Id=“center”/>

<owl:DatatypeProperty rdf:Id=“lowClosed”/><owl:DatatypeProperty rdf:Id=“lowClosed”/>

<owl:DatatypeProperty rdf:Id=“highClosed”/><owl:DatatypeProperty rdf:Id=“highClosed”/>

</owl:Class></owl:Class>

<owl:Class rdf:Id=“Observation”/><owl:Class rdf:Id=“Observation”/>

<owl:DatatypeProperty rdf:Id=“id”/><owl:DatatypeProperty rdf:Id=“id”/>

<owl:DatatypeProperty rdf:Id=“hasStatusCode”/><owl:DatatypeProperty rdf:Id=“hasStatusCode”/>

<owl:Attribute rdf:Id=“hasEffectiveTime”/><owl:Attribute rdf:Id=“hasEffectiveTime”/>

<owl:Attribute rdf:Id=“hasCode”/><owl:Attribute rdf:Id=“hasCode”/>

<owl:Attribute rdf:Id=“hasValue”/><owl:Attribute rdf:Id=“hasValue”/>

<owl:Attribute rdf:Id=“hasTargetSite”/><owl:Attribute rdf:Id=“hasTargetSite”/>

</owl:Class></owl:Class>

<owl:Class rdf:Id=“CD”/><owl:Class rdf:Id=“CD”/>

<owl:Attribute rdf:Id=“text”/><owl:Attribute rdf:Id=“text”/>

<owl:DatatypeProperty rdf:Id=“code”/><owl:DatatypeProperty rdf:Id=“code”/>

<owl:DatatypeProperty rdf:Id=“codeSystem”/><owl:DatatypeProperty rdf:Id=“codeSystem”/>

<owl:DatatypeProperty rdf:Id=“codeSystemName”/><owl:DatatypeProperty rdf:Id=“codeSystemName”/>

<owl:DatatypeProperty rdf:Id=“codeSysteVersion”/><owl:DatatypeProperty rdf:Id=“codeSysteVersion”/>

<owl:DatatypeProperty rdf:Id=“displayName”/><owl:DatatypeProperty rdf:Id=“displayName”/>

</owl:Class></owl:Class>

<owl:Attribute rdf:Id=“hasEffectiveTime”/><owl:Attribute rdf:Id=“hasEffectiveTime”/><owl:Domain rdf:Id=“Observation”/><owl:Domain rdf:Id=“Observation”/><owl:Range rdf:Id=“IVL_TS”/><owl:Range rdf:Id=“IVL_TS”/>

<owl:Attribute/><owl:Attribute/><owl:Attribute rdf:Id=“hasEffectiveTime”/><owl:Attribute rdf:Id=“hasEffectiveTime”/>

<owl:Domain rdf:Id=“Observation”/><owl:Domain rdf:Id=“Observation”/><owl:Range rdf:Id=“IVL_TS”/><owl:Range rdf:Id=“IVL_TS”/>

<owl:Attribute/><owl:Attribute/>

<owl:Attribute rdf:Id=“hasCode”/><owl:Attribute rdf:Id=“hasCode”/><owl:Domain rdf:Id=“Observation”/><owl:Domain rdf:Id=“Observation”/><owl:Range rdf:Id=“CD”/><owl:Range rdf:Id=“CD”/>

<owl:Attribute/><owl:Attribute/>

<owl:Attribute rdf:Id=“hasValue”/><owl:Attribute rdf:Id=“hasValue”/><owl:Domain rdf:Id=“Observation”/><owl:Domain rdf:Id=“Observation”/><owl:Range rdf:Id=“ANY”/><owl:Range rdf:Id=“ANY”/>

<owl:Attribute/><owl:Attribute/>

<owl:Attribute rdf:Id=“hasTargetSiteCode”/><owl:Attribute rdf:Id=“hasTargetSiteCode”/><owl:Domain rdf:Id=“Observation”/><owl:Domain rdf:Id=“Observation”/><owl:Range rdf:Id=“CD”/><owl:Range rdf:Id=“CD”/>

<owl:Attribute/><owl:Attribute/>

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Sample OWL Ontology ModelSample OWL Ontology Model

Class AssociationAttribute Datatype Attribute

….….

….

(a) Diagnosis Ontology Model (c) Anatomy Ontology Model

(b) Test Ontology Model

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Ontology Example: Open Cyc Open Cyc is an Upper level ontology developed by Open Cyc is an Upper level ontology developed by

Cycorp Inc. Cycorp Inc. Open Cyc has 60,000 hand coded assertions that Open Cyc has 60,000 hand coded assertions that

capture “common sense language”, so that AI capture “common sense language”, so that AI algorithms can perform human like reasoning and algorithms can perform human like reasoning and contains 6,000 conceptscontains 6,000 concepts

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Example of Open CycExample of Open Cyc

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Ontology Example: Word Net WordNet is an electronic lexical database developed at

Princeton University that serves as a resource for applications in natural language processing and information retrieval.

cancer, malignant neoplastic disease: any malignant growth or tumor caused by abnormal and uncontrolled cell division; it may spread to other parts of the body through the lymphatic system or the blood stream Cancer, Crab: (astrology) a person who is born while the sun is in CancerCancer: a small zodiacal constellation in the northern hemisphere; between Leo and GeminiCancer, Cancer the Crab, Crab: the fourth sign of the zodiac; the sun is in this sign from about June 21 to July 22Cancer, genus Cancer: type genus of the family Cancridae

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Unifies Medical Language System Unifies Medical Language System UMLS was developed for National Library of

Medicine

Disease is semantic type with around 392 relations (109 semantic relations and 22 other relations). Pneumonia categorized under one semantic type Disease, but has hundreds of relations.

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Example Ontology: SNOMED-CT SNOMED stands for Systemized Nomenclature Of

Medicine Clinical Terms. SNOMED-CT is the result of merging two ontologies: SNOMED-RT and Clinical Terms.

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Example Ontology: Clinical TrialsExample Ontology: Clinical Trials Low participation in Clinical Trials is the major

problem in Clinical and translational research area. Matching the patient records to clinical trials is

presently a manual procedure and its tedious. Need a Semantic Bridge between Clinical Ontologies

(SNOMED CT, etc ..) and raw patient data for retrieving matching patient records, clinical

guidelines and clinical decision support systems ( CDSS).

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Technical ChallengesTechnical Challenges Challenges to be faced during real time scenario:Challenges to be faced during real time scenario:

Knowledge Engineering. Scalability Noisy or Incomplete Data

Knowledge EngineeringKnowledge Engineering Clinical Ontology has the concept “Drug”, which

described active composition of the various drugs However, patient record contains name of vendor-

specific drugs list Clinical Ontology describe the cause of the disorder.

The patient records only specify the presence or absence of the disorder and where was the clinical test conducted.

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Architecture of SolutionArchitecture of Solution

Patient

Data

ABox

SNOMED-CT

TBox

Query

Ontology

Reasoner

Clinical Trials

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Implementation ApproachImplementation Approach Mapping Patient Data Terminology to SNOMED-CTMapping Patient Data Terminology to SNOMED-CT

Using UMLS as intermediate target. NLP mapping techniques Manual Mapping

Map the raw patient data to SNOMED-CT Map the raw patient data to SNOMED-CT terminology.terminology. Example: Cerner Drug: Lactulose Syrup 20G/30ml SNOMED-CT: administeredSubstance

Allow user to specify which terms in the definition to Allow user to specify which terms in the definition to be matched. be matched.

Last Bullet Means Ontology Matching NOT Fully Last Bullet Means Ontology Matching NOT Fully Automated!Automated!

This is a Real Problem for Interoperating Data!This is a Real Problem for Interoperating Data!

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Contrast in RepresentationContrast in Representation

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How are Observations Reconciled?How are Observations Reconciled?

Э associatedObservation MRSA

Э associatedObservation Pneumococcal Penumonia П Э hasSpecimanSource Blood Ц Sputum

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Example Ontology:Example Ontology:Clinical Decision Support SystemClinical Decision Support System

Clinical Decision Support Systems (CDSS) are Clinical Decision Support Systems (CDSS) are Interactive computer programs Designed to assist physicians and other health

professionals with decision making tasks Components of CDSS:Components of CDSS:

Knowledge Base Rule Based Engine Case Base Business Models

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Example of Usage of RulesExample of Usage of Rules

IF “ RULE 1” &“RULE 2” &“RULE 3” ….. “Rule n”

THEN “INTERVENTION 1 or Rule M”

IF p.getGender() = “male”& p.getAge()=34 & p.getBP() <140 & p.getInsulinLevel()<20

THEN “ Asthma Intervention Level 2”

Class Patinet HasGender “male” П hasAge “34” П hasBP MoreThan 140 П

hasInsulinLevel MoreThan 20

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Ontology IntegrationOntology Integration All ontologies developed have a common aim, All ontologies developed have a common aim,

describing the domain knowledgedescribing the domain knowledge Integration of ontologies is becoming very critical Integration of ontologies is becoming very critical

Applications tend to use multiple ontologies Concepts in the various ontologies overlap or

same concept is described in multiple ways. For example, the concept “Blood” is described as For example, the concept “Blood” is described as

differently differently “Fluid” in one ontology “Substance” in another ontology “semi-solid” in a third ontology

Need to Reconcile these Differences When Need to Reconcile these Differences When Attempting to “Combine” data that Originates from Attempting to “Combine” data that Originates from Different OntologiesDifferent Ontologies

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Example of Conflicting OntologiesExample of Conflicting Ontologies• Ontology 1:Ontology 1:

Disease References Symptoms which References Treatments

Hierarchy of:

• Ontology 2:Ontology 2: Symptoms References

Diseases which References Treatments

Hierarchy of:

Previously Discussed Issues: Previously Discussed Issues: How do you Integrate Ontologies Across HIT to Support HIE How do you Integrate Ontologies Across HIT to Support HIE

and Virtual Chart?and Virtual Chart? How do you Merge Data Intensive Conflicting Ontologies?How do you Merge Data Intensive Conflicting Ontologies? How do you query from Inside Out?How do you query from Inside Out?

• DiseaseDisease• Respiratory DiseaseRespiratory Disease• Cardio DiseaseCardio Disease• Nervous DiseaseNervous Disease

• SymptomsSymptoms• General SymptomsGeneral Symptoms• Behavioral SymptomsBehavioral Symptoms

• TreatmentTreatment• General TreatmentGeneral Treatment• Surgical TreatmentsSurgical Treatments

• SymptomsSymptoms• General SymptomsGeneral Symptoms• Behavioral SymptomsBehavioral Symptoms

• DiseaseDisease• Respiratory DiseaseRespiratory Disease• Cardio DiseaseCardio Disease• Nervous DiseaseNervous Disease

• TreatmentTreatment• General TreatmentGeneral Treatment• Surgical TreatmentsSurgical Treatments

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Ontology IntegrationOntology Integration Semantics vs Structural Integration ? Difficulties of integration arise with similar, same and Difficulties of integration arise with similar, same and

complementary ontology integration. complementary ontology integration.

Ontology B

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OASISOASIS Ontology Mapping and Integration FrameworkOntology Mapping and Integration Framework

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Summary - OntologiesSummary - Ontologies OntologyOntology

Definition and Descriptions Many Examples in Practice OWL and RDF

Biomedical OntologyBiomedical Ontology Open Cyc WordNet SNOMED - CT

Application of Biomedical OntologyApplication of Biomedical Ontology Clinical Trials OASIS: Integration Technique Clinical Decision Support System

Integration of OntologiesIntegration of Ontologies