ONTO-1 CSE 5810 Ontologies Ontologies Prof. Steven A. Demurjian, Sr. Computer Science & Engineering Department The University of Connecticut 371 Fairfield Road, Box U-255 Storrs, CT 06269-2155 [email protected]http://www.engr.uconn.edu/ ~steve (860) 486 - 4818
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ONTO-1 CSE 5810Ontologies Prof. Steven A. Demurjian, Sr. Computer Science & Engineering Department The University of Connecticut 371 Fairfield Road, Box.
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OntologiesOntologies
Prof. Steven A. Demurjian, Sr.Computer Science & Engineering Department
The University of Connecticut371 Fairfield Road, Box U-255
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
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"/>
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)
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
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?
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
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?
• 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