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Ontologies

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Ontologies Prof. Steven A. Demurjian, Sr. Computer Science & Engineering Department The University of Connecticut 371 Fairfield Road, Box U-255 Storrs, CT 06269-2155 – PowerPoint PPT presentation

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Title: Ontologies


1
Ontologies
Prof. Steven A. Demurjian, Sr. Computer Science
Engineering Department The University of
Connecticut 371 Fairfield Road, Box U-255
Storrs, CT 06269-2155
steve_at_engr.uconn.edu http//www.engr.uconn.edu/st
eve (860) 486 - 4818
2
Motivation
  • Ontologies Biomedical and Clinical
  • What are they?
  • How are they Used?
  • 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?

3
Placing Ontologies into Perspective
  • Historical Evolution of WWW
  • Ontology
  • Definition and Description
  • RDF and OWL
  • Present Biomedical Ontology
  • Applications of Biomedical Ontologies
  • Clinical Trials
  • OASIS Integration Technique
  • Clinical Decision Support System

3
4
Current Information Systems on WWW
  • First Generation
  • Raw data which was pretty much hand-coded by the
    user was published online
  • For example, Static web pages
  • Second Generation
  • Dynamic content generation driven by MDA and
    databases
  • Machines generate the respective HTML
  • 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.

5
What are Ontologies?
  • 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)
  • 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.

6
Advantages of Ontology
  • Semantic way of representing knowledge of the
    domain
  • Intelligent system can provide reasoning Systems
    to make inferences within the Ontology
  • Two main Objectives
  • Share the common structure of information
  • Reuse the similar ontology in another domain

7
Development 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

8
Example of Ontology
Wine
Australian Yellow Tail
Class
Individual
Grape
Properties
Maker
Color
Flavor
German
Yellow
Delicate
Australia
9
Parkinsons Disease Management Ontology
10
Parkinsons Treatment Ontology
11
Parkinsons Treatment Ontology
12
Neurological-Disease Ontology
13
Excerpt of Medical Condition Ontology
14
Patient Ontology
15
Skelton Ontology
16
How do Ontologies Related to other Models?
  • UML Model

17
How do Ontologies Related to other Models?
  • Entity Relationship Diagram

18
How do Ontologies Related to other Models?
  • XML Schema

ltxselement namePatient"gt  ltxscomplexTypegt   
ltxssequencegt      ltxselement nameid"
type"xsinteger"/gt      ltxselement
nameethnicity" type"xsstring"/gt     
ltxselement namerace" type"xsstring"/gt .  
     ltxselement nametel" typexsstring"/gt   
lt/xssequencegt  lt/xscomplexTypegtlt/xselementgt
ltxselement nameSubstance"gt 
ltxscomplexTypegt    ltxssequencegt     
ltxselement nameid" type"xsinteger"/gt     
ltxselement namename" type"xsstring"/gt     
ltxselement namestatusCode" type"xsstring"/gt
.       ltxselement namerepeatNumber"
typexsinteger"/gt    lt/xssequencegt 
lt/xscomplexTypegtlt/xselementgt
ltxselement nametakesPrescribedMedication"gt   
ltxssequencegt      ltxselement ref
Patient"/gt      ltxselement ref
Substance"/gt    lt/xssequencegtlt/xselementgt ltx
selement namehasMedicalObservation"gt   
ltxssequencegt      ltxselement ref
Patient"/gt      ltxselement ref
Observation"/gt    lt/xssequencegtlt/xselementgt
ltxselement nameObservation"gt 
ltxscomplexTypegt    ltxssequencegt     
ltxselement nameid" type"xsinteger"/gt     
ltxselement namename" type"xsstring"/gt     
ltxselement namevalue" type"xsstring"/gt     
ltxselement namestatusCode" typexsstring"/gt 
   lt/xssequencegt  lt/xscomplexTypegtlt/xselement
gt
19
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 1970s

20
Introduction to RDF / OWL
21
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,
    rdfsClass, rdfssubClassOf etc

22
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.co
    m/examleStudents).
  • RDF can be viewed as standardized framework for
    providing metadata to domain concepts.

ltrdfsClass rdfabout"http//www.example.com/exam
leStudents" rdfslabel"Students"gt lt/rdfsClassgt
ltrdfsClass rdfabout"http//www.example.com/ex
amleClasses" rdfslabelClasses"gt lt/rdfsClassgt
23
OWL Web Ontology Language
  • OWL is placed on the top of the semantic web
    stack, utilizing all the powerful features
    offered by the layers below (RDF, RDFS, XML)
  • OWL design has been influenced by description
    logic knowledge representational paradigms
  • SHIQ, Semantic Networks, Frames, SHOE, DAML, OIL,
    DAMLOIL.
  • OWL provides richer semantic capabilities than
    its predecessor RDF
  • For example, in the previous example, the
    predicate registerTo is of type rdfProperty.

24
OWL Web Ontology Language
  • OWL differentiates between properties by defining
  • owlObjectProperty for connecting two concepts
    (registerTo) and
  • owlDatatypeProperty - for connecting a concept
    to a datatype (utilized from XML)
  • These two properties inherit from RDF property
  • OWL also defines owlAnnotationProperty 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)

25
OWL Web Ontology Language
  • 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
26
OWL Web Ontology Language
ltowlObjectProperty rdfID"Complications"gt
ltrdfsdomain rdfresource"Cardiology_Diseases"/gt
ltrdfsrangegt ltowlClassgt
ltowlunionOf rdfparseType"Collection"gt
ltowlClass rdfabout"Cardiology_Complications"/
gt ltowlClass rdfabout"Cardiology_Dise
ases"/gt ltowlClass rdfabout"Cardiolog
y_Causes"/gt lt/owlunionOfgt
lt/owlClassgt lt/rdfsrangegt
lt/owlObjectPropertygt
  • 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 concepts

BioMedical Informatics
27
Disease Ontology
Sub-Classes of Cardiology Diseases
Instances of Mitral_Valve_Disorders
Hierarchical organization of Cardiology Diseases
28
Disease Ontology
Property Defined
Representation of Mitral_Valve_Prolapse
knowledge using properties and instances
29
Implemented Ontology in OWL Format
.. ltCongenital_Heart_Disease
rdfID"Atrial_septal_defect"gt
ltComplicationsgt ltCardiac_Arrhythmias
rdfID"Arrhythmia"gt ltHas_Intervention
rdfdatatype"http//www.w3.org/2001/XMLSchemastr
ing" gtdefibrillationlt/Has_Interventiongt
ltHave_Symptomsgt
ltCardiology_Symptoms rdfID"Dyspnea"/gt
lt/Have_Symptomsgt ltHas_Diagnosis_Testgt
ltCardiology_Diagnosis_Test
rdfID"Coronary_Angiography"gt
ltHas_Synonyms rdfdatatype"http//www.w3.org/2001
/XMLSchemastring" gtcoronary
catheterization lt/Has_Synonymsgt ..
30
Bio-Medical Ontologies
  • Review a Wide Range of Available Ontologies and
    Standards
  • OpenCyc
  • WordNet
  • Galen
  • UMLS
  • SNOMED CT
  • FMA
  • Gene Ontology

31
Sample EHR Model in UML via HL7 CDA
32
OWL Equivalent for Observation
ltowlClass rdfIdIVL_TS/gt ltowlDatatypeProperty
rdfIdLow/gt ltowlDatatypeProperty
rdfIdHigh/gt ltowlDatatypeProperty
rdfIdwidth/gt ltowlDatatypeProperty
rdfIdcenter/gt ltowlDatatypeProperty
rdfIdlowClosed/gt ltowlDatatypeProperty
rdfIdhighClosed/gt lt/owlClassgt
ltowlClass rdfIdCD/gt ltowlAttribute
rdfIdtext/gt ltowlDatatypeProperty
rdfIdcode/gt ltowlDatatypeProperty
rdfIdcodeSystem/gt ltowlDatatypeProperty
rdfIdcodeSystemName/gt ltowlDatatypeProperty
rdfIdcodeSysteVersion/gt ltowlDatatypeProperty
rdfIddisplayName/gt lt/owlClassgt
ltowlAttribute rdfIdhasEffectiveTime/gt ltowlD
omain rdfIdObservation/gt ltowlRange
rdfIdIVL_TS/gt ltowlAttribute/gt ltowlAttribute
rdfIdhasEffectiveTime/gt ltowlDomain
rdfIdObservation/gt ltowlRange
rdfIdIVL_TS/gt ltowlAttribute/gt ltowlAttribute
rdfIdhasCode/gt ltowlDomain
rdfIdObservation/gt ltowlRange
rdfIdCD/gt ltowlAttribute/gt ltowlAttribute
rdfIdhasValue/gt ltowlDomain
rdfIdObservation/gt ltowlRange
rdfIdANY/gt ltowlAttribute/gt ltowlAttribute
rdfIdhasTargetSiteCode/gt ltowlDomain
rdfIdObservation/gt ltowlRange
rdfIdCD/gt ltowlAttribute/gt
ltowlClass rdfIdObservation/gt ltowlDatatypePro
perty rdfIdid/gt ltowlDatatypeProperty
rdfIdhasStatusCode/gt ltowlAttribute
rdfIdhasEffectiveTime/gt ltowlAttribute
rdfIdhasCode/gt ltowlAttribute
rdfIdhasValue/gt ltowlAttribute
rdfIdhasTargetSite/gt lt/owlClassgt
33
Sample OWL Ontology Model
34
Ontology Example Open Cyc
  • Open Cyc is an Upper level ontology developed by
    Cycorp Inc.
  • Open Cyc has 60,000 hand coded assertions that
    capture common sense language, so that AI
    algorithms can perform human like reasoning and
    contains 6,000 concepts

35
Example of Open Cyc
36
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 Cancer Cancer a small zodiacal constellation
in the northern hemisphere between Leo and
Gemini Cancer, Cancer the Crab, Crab the fourth
sign of the zodiac the sun is in this sign from
about June 21 to July 22 Cancer, genus Cancer
type genus of the family Cancridae
37
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.
38
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.

39
Example 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).

40
Technical Challenges
  • Challenges to be faced during real time
    scenario
  • Knowledge Engineering.
  • Scalability
  • Noisy or Incomplete Data
  • Knowledge 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.

41
Architecture of Solution
Clinical Trials
42
Implementation Approach
  • Mapping Patient Data Terminology to SNOMED-CT
  • Using UMLS as intermediate target.
  • NLP mapping techniques
  • Manual Mapping
  • Map the raw patient data to SNOMED-CT
    terminology.
  • Example Cerner Drug Lactulose Syrup 20G/30ml
  • SNOMED-CT administeredSubstance
  • Allow user to specify which terms in the
    definition to be matched.
  • Last Bullet Means Ontology Matching NOT Fully
    Automated!
  • This is a Real Problem for Interoperating Data!

43
Contrast in Representation
  • Example
  • SNOMED-CT Disease1
  • hasAgent Virus007
  • Infection due to Bacteria001
  • Infection due to MicroBacteria007
  • Patient Record Disease1 Positive.
  • As there is not much information in the
    patient
  • record the query reasoner cannot find the
    records
  • with partial data.

44
How are Observations Reconciled?
Clinical Trials Description
NCT00084266 Patients with MSRA
NCT00288808 Patients with warfarin
NCT00298870 Patients on steroids
NCT00304382 Patients with Pneumonia,source of Blood or Sputum
? associatedObservation MRSA
? associatedObservation Pneumococcal
Penumonia ? ?
hasSpecimanSource Blood ? Sputum
45
Example OntologyClinical Decision Support System
  • Clinical Decision Support Systems (CDSS) are
  • Interactive computer programs
  • Designed to assist physicians and other health
    professionals with decision making tasks
  • Components of CDSS
  • Knowledge Base
  • Rule Based Engine
  • Case Base
  • Business Models

46
Example 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() lt140 p.getInsulinLevel()lt20 THEN
Asthma Intervention Level 2
Class Patinet HasGender male ? hasAge 34 ?
hasBP MoreThan 140 ? hasInsulinLevel MoreThan 20
47
Ontology Integration
  • All ontologies developed have a common aim,
    describing the domain knowledge
  • 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
    differently
  • Fluid in one ontology
  • Substance in another ontology
  • semi-solid in a third ontology
  • Need to Reconcile these Differences When
    Attempting to Combine data that Originates from
    Different Ontologies

48
Example of Conflicting Ontologies
  • Ontology 1
  • Disease References Symptoms which References
    Treatments
  • Hierarchy of
  • Ontology 2
  • Symptoms References Diseases which References
    Treatments
  • Hierarchy of
  • Disease
  • Respiratory Disease
  • Cardio Disease
  • Nervous Disease
  • Symptoms
  • General Symptoms
  • Behavioral Symptoms
  • Treatment
  • General Treatment
  • Surgical Treatments
  • Symptoms
  • General Symptoms
  • Behavioral Symptoms
  • Disease
  • Respiratory Disease
  • Cardio Disease
  • Nervous Disease
  • Treatment
  • General Treatment
  • Surgical Treatments
  • Previously Discussed Issues
  • How do you Integrate Ontologies Across HIT to
    Support HIE and Virtual Chart?
  • How do you Merge Data Intensive Conflicting
    Ontologies?
  • How do you query from Inside Out?

49
Ontology Integration
  • Semantics vs Structural Integration ?
  • Difficulties of integration arise with similar,
    same and complementary ontology integration.

Ontology B
50
OASIS
  • Ontology Mapping and Integration Framework

51
Summary - Ontologies
  • Ontology
  • Definition and Descriptions
  • Many Examples in Practice
  • OWL and RDF
  • Biomedical Ontology
  • Open Cyc
  • WordNet
  • SNOMED - CT
  • Application of Biomedical Ontology
  • Clinical Trials
  • OASIS Integration Technique
  • Clinical Decision Support System
  • Integration of Ontologies
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