1

Project “Scope”

Semantic interoperability Community of Practice Enablement for:

– Federal Health Architecture (FHA)– National Health Information Network (NHIN)

DRM Semantic Technologies Profile Pilot by

– Ontolog Forum,– Stanford Medical Informatics, – SICoP

2

Outline

• Participants

• Purpose

• Topics

• Demonstrations– Protégé based EON– Time Representations

• Next Steps

3

Participants• ONTOLOG Forum:

– an open, international, citizen-centric, virtual community of practice working on business domain ontologies.

– http://ontolog.cim3.net• Stanford Medical Informatics (SMI):

– an interdisciplinary academic and research group within the Department of Medicine in the Stanford University School of Medicine

– brings together scientists who create and validate models of how knowledge and data are used within biomedicine.

– http://www.smi.stanford.edu/• Semantic Interoperability Community of Practice

(SICoP):– chartered by the CIO Council’s Best Practices Committee for the

purpose of achieving "semantic interoperability" and "semantic data integration"

– focused on the government sector and to make the Semantic Web operational.

– http://web-services.gov

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Purpose

• to see how ontologies can help architects define their architecture   

• to understand how ontologies will help architects drive interoperability in ways that:– avoid remedial work down the road    – avoid “false starts”

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Specific Objectives

• Provide Semantic Technology Profiles (STP) for the Data Reference Model (DRM).– See http://colab.cim3.net/cgi-bin/wiki.pl?

DataReferenceModel• Support the Federal Health Architecture’s (FHA)

Architecture Development Methodology (ADM) and the Architecture Peer Review Group (APRG).– See http://fha.mitre.org/ecommunities/login.jsp

(password required)• Support the National Health Information (NHIN)

Network Request for Information (RFI) Review.– See http://www.hhs.gov/healthit/

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Main Topics (by Slide numbers)

• Ontologies for Semantic Interoperability in Enterprise Architecture (Slide # 8)

• Formal Taxonomies as Ontologies (Slide # 9)

• Ontology-Driven Information Systems (Slide #10)

• Categories of Ontologies (Slides # 11-12)

• FEA Reference Model Ontology (FEA-RMO) (Slide #13)

• Business Reference Model (BRM) Taxonomy (Slide #14)

• Health Domains Taxonomy (Slides #15-16)

• The Role of Ontologies in the Electronic Health Record (Slide #17)

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Ontologies for Semantic Interoperability in Enterprise Architecture

Age of Programs

Age of Proprietary

Data

Age of OpenData

Age of Open

Metadata

Age of SemanticModels

Program-Data

GIGO/minis/micros www / Netscape Web services OWL

Text, Office DocsDatabases

(proprietary schema)

HTML,XML

(open schema)

Namespaces,Taxonomies,

RDF

Ontologies&

Inference

1945 -1970 2000 - 20031994 - 20001970 - 1994 2003 -

ProceduralProgramming

Object-OrientedProgramming

Model-DrivenProgramming

“Data is lesslessimportant

than code”

“Data is asasimportantas code”

“Data is moremoreimportant

than code”

Michael Daconta, Creating Relevance and Reuse with Targeted Semantics,XML 2004 Conference Keynote, November 16, 2004.

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Formal Taxonomies as OntologiesOWL Listing:<?xml version="1.0"?> <rdf:RDF

xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:daml="http://www.daml.org/2001/03/daml+oil#" xmlns="http://www.owl-ontologies.com/unnamed.owl#" xmlns:dc="http://purl.org/dc/elements/1.1/" xml:base="http://www.owl-ontologies.com/unnamed.owl"> <owl:Ontology rdf:about=""/> <owl:Class rdf:ID="Transportation"/> <owl:Class rdf:ID="AirVehicle"> <rdfs:subClassOf rdf:resource="#Transportation"/> </owl:Class> <owl:Class rdf:about="#GroundVehicle"> <rdfs:subClassOf rdf:resource="#Transportation"/> </owl:Class> <owl:Class rdf:about="#Automobile"> <rdfs:subClassOf> <owl:Class rdf:ID="GroundVehicle"/> </rdfs:subClassOf> Etc.

Formal Taxonomies for the U.S. Government, Michael Daconta, Metadata Program Manager, US Department of Homeland Security, XML.Com, http://www.xml.com/pub/a/2005/01/26/formtax.html

Transportation Class Hierarchy

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Ontology-Driven Information Systems

• Methodology Side – the adoption of a highly interdisciplinary approach (means multiple CoPs and effective coordination mechanisms):– Analyze the structure at a high level of generality.– Formulate a clear and rigorous vocabulary.

• Architectural Side – the central role in the main components of an information system:– Information resources.– User interfaces.– Application programs.

Source: Nicola Guarino, Formal Ontology and Information Systems,Proceedings of FOIS ’98, Trento, Italy, 6-8 June 1998.

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Categories of Ontologies

Most General Thing

Process Location

Geographic Area of Interest

Airspace Target Area of Interest

UpperOntology

Mid-LevelOntology

DomainOntology

Most General Thing

Process Location

Geographic Area of Interest

Airspace Target Area of Interest

UpperOntology

Mid-LevelOntology

DomainOntology

Source: Netcentric Semantic Linking (Mapping): An Approach for Enterprise Semantic Interoperability, Mary Pulvermacher, et. Al. MITRE, October 2004.

SUMO

HL7 RIM

FEA-RMO*

EONSNOMED CTLOINC

Examples

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Upper Ontologies

– Several examples of Upper Ontologies illustrate their important functionality in practice; SUMO, DOLCE, Omega, MSO for example.

– The Suggested Upper Merged Ontology (SUMO) and its domain ontologies form the largest formal public ontology in existence today.

– They are being used for research and applications in search, linguistics and reasoning. SUMO is the only formal ontology that has been mapped to all of the WordNet lexicon.

– SUMO is written in the SUO-KIF language. – SUMO is free and owned by the IEEE. – The ontologies that extend SUMO are available under GNU

General Public License. Adam Pease, a member of the Ontolog Forum, is the Technical Editor of SUMO.

– See http://www.ontologyportal.org/.

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FEA Reference Model Ontology FEA-RMO*

• The purpose of FEA-RMO is to:– Define an ontology based on FEA reference models

(PRM, BRM, SRM, TRM, and DRM),– Develop a common vocabulary, or lexicon, from the

FEA reference models,– Support execution, validation, and inference based on

FEA reference models,– Support the GSA role in e-Government as market

maker, and– Support OMB/AIC partnership in AIC Task 1 & AIC

Task 4 by providing lessons learned and an ontology.

Source: GSA FEA Reference Model Ontology: A Domain Specific Parsimonious Ontology, Rick Murphy, Enterprise Architect, Office of the CIO, GSA, January 18, 2005.

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Business Reference Model Taxonomy

• Four Business Areas-one of which is:– Services to Citizens, which has

• 19 Lines of Business-one of which is:– Health, which has

• 5 Topics:– Health Care Services– Illness Prevention– Immunization Management– Public Health Monitoring– Consumer Health and Safety

"The Business Reference Model is a function-driven framework for describing the business operations of the Federal Government independent of the agencies that perform them.“ Federal Enterprise Architecture Program Management Office. 2004.

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Health Domains Taxonomy• Access to Care

– Focuses on the access to appropriate care

• Population Health and Consumer Safety– Assesses health indicators and consumer products as a means to

protect and promote the health of the general population

• Health Care Administration– Assures that federal health care resources are expended effectively to

ensure quality, safety, and efficiency

• Health Care Delivery Services– Provides and supports the delivery of health care to its beneficiaries

• Health Care Research and Practitioner Education– Fosters advancements in health discovery and knowledge

Source: Introduction to the Federal Health Architecture Development Methodology, Briefing to the FHA APRG, February 10, 2005.

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Health Domains Taxonomy

• Health Care Domains (simplified):– Access– Information– Administration– Delivery Services– Research and

Education

• FHA Organization (New):– Regional Initiatives– Clinical Practice– Population Health– Health Interoperability– Federal Health

Architecture

Source: Architectural Peer Review Group(APRG) Initial Meeting, February 10, 2005

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Demonstrators

• Samson W. Tu, Stanford Medical Informatics, Stanford University:– The EON and ATHENA Projects– What the Clinician Sees– Guideline-Based Decision Support Architecture– The Ontology in Protégé

• Peter Yim, Co-Organizer of the Ontolog Forum:– The SUMO Time-Related Concepts in Protégé– Upper Ontology Component Reuse:

• Extend From an Upper Ontology (SUMO), Rather Than Repeating the Definitions Within the Domain Ontology (HL7)

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Demonstration #1 Health Care Domain

• EON: A domain-independent, component-based architecture for automation of protocol-based care.

• Architecture (see next slide):– Problem solving components that have task-specific

functions:• Planning patient’s therapy• Determining patient’s eligibility for protocols

– A temporal data mediator that• Extends the standard relational model with a model of time• Supports valid-time temporal queries and updates

– A shared knowledgebase of protocols and general medical concepts

Source: http://www.smi.stanford.edu/projects/eon/96SCAMCMusen/sld007.htm

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Demonstration: #1 Architecture

ClientsServers

ProtocolEligibilityChecker

TherapyAdvisory

Server

Protégé

TemporalMediator

YentaYentaEligibilityClient

YentaYentaAdvisoryClient

ClientsPatient

Database

ProtégéKnowledge

BaseEON

GuidelineOntology

Medical DomainOntology

PatientData Model

Guidelines

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Pause for Slides & Online Demo

Samson’s 13 slides follow this slide

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Reuse and Semantic Interoperability

• Multiple working groups shouldn't redefine-basic concepts– Undermines semantic interoperability across domains and

systems– Varying quality of individual models– Limits downstream extensibility

• Ontology-based formalizations offer more rigor– Typically leverage work of broader community of interests– Designed for reuse and extensibility– Generally reflect more thorough, higher-quality modeling– Reuse of Upper and Mid-level ontologies improves semantic

alignment of Domain-Level ontologies and resulting implementations

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Examples of Time Formalization

• HL-7*– Time taxonomy fragment– TimingEvent model

• WordNet– Time (Verb)– Time (Noun)

• SUMO– Process Examples

*See Patrick Cassidy notes: http://ontolog.cim3.net/forum/health-ont/2005-02/msg00011.html

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HL-7 Timing Event ModelType Concept ID Mnemonic Description

L: (AC) 10708 AC Before meal (from lat.ante cibus)

L: (ACD) 10712 ACD Before lunch (from lat.ante cibus diurnus)

L: (ACM) 10711 ACM Before breakfast (from lat. Ante cibus matutinus)

L: (ACV) 10713 ACV Before dinner (from lat. Ante cibus vespertinus)

L: (HS) 10707 HS The hour of sleep (e.b., H18-22)

L: (IC) 10710 IC Between meals (from lat. Inter cibus)

L: (ICD) 10718 ICD Between lunch and dinner

L: (ICM) 10717 ICM Between breakfast and lunch

L: (ICV) 10719 ICV Between dinner and the hour or sleep

L: (PC) 10709 PC After meal (from lat. post cibus)

L: (PCD) 10715 PCD After lunch (from lat. post cibus diurnus)

L: (PCM) 10714 PCM After breakfast (from lat. post cibus matutinus)

L: (PCV) 10716 PCV After dinner (from lat. Post cibus vespertinus)

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Time Representation in HL7

The following was selected from the HL-7 taxonomy:• DataTypeDataValue• DataTypeInterval

– DataTypeIntervalOfPhysicalQuantities – DataTypeIntervalOfPointsInTime

• DataTypeEventRelatedInterval• DataTypeGeneralTimingSpecification• DataTypePeriodicIntervalOfTime

• DataTypeQuantity– DataTypePhysicalQuantity

• DataTypeParametricProbabilityDistributionOfPhysicalQuantities

– DataTypePointInTime

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WordNet “Time” (Verb)

1. S: (v) clock, time (measure the time or duration of an event or action or the person who performs an action in a certain period of time) "he clocked the runners"

2. S: (v) time (assign a time for an activity or event) "The candidate carefully timed his appearance at the disaster scene"

3. S: (v) time (set the speed, duration, or execution of) "we time the process to manufacture our cars very precisely"

4. S: (v) time (regulate or set the time of) "time the clock"

5. S: (v) time (adjust so that a force is applied and an action occurs at the desired time) "The good player times his swing so as to hit the ball squarely“

From http://wordnet.princeton.edu/

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WordNet “Time” (Noun)1. S: (n) time, clip (an instance or single occasion for some event) "this time he succeeded"; "he

called four times"; "he could do ten at a clip"

2. S: (n) time (an indefinite period (usually marked by specific attributes or activities)) "he waited a long time"; "the time of year for planting"; "he was a great actor is his time"

3. S: (n) time (a period of time considered as a resource under your control and sufficient to accomplish something) "take time to smell the roses"; "I didn't have time to finish"; "it took more than half my time"

4. S: (n) time (a suitable moment) "it is time to go"

5. S: (n) time (the continuum of experience in which events pass from the future through the present to the past)

6. S: (n) clock time, time (the time as given by a clock) "do you know what time it is?"; "the time is 10 o'clock"

7. S: (n) fourth dimension, time (the fourth coordinate that is required (along with three spatial dimensions) to specify a physical event)

8. S: (n) time (a person's experience on a particular occasion) "he had a time holding back the tears"; "they had a good time together"

9. S: (n) meter, metre, time (rhythm as given by division into parts of equal duration)

10. S: (n) prison term, sentence, time (the period of time a prisoner is imprisoned) "he served a prison term of 15 months"; "his sentence was 5 to 10 years"; "he is doing time in the county jail"

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Time Representation in SUMO

WordNet N1 maps to SUMO “Process”

• Appearance as argument number 1– documentation Process "Intuitively, the class of things that happen and

have temporal parts or stages. Examples include extended events like a football match or a race, actions like Pursuing and Reading, and biological processes. The formal definition is: anything that lasts for a time but is not an Object. Note that a Process may have participants 'inside' it which are Objects, such as the players in a football match. In a 4D ontology, a Process is something whose spatiotemporal extent is thought of as dividing into temporal stages roughly perpendicular to the time-axis.“

– subclass Process Physical

From http://sigma2.cim3.net:8080/sigma/Browse.jsp?term=Process&kb=SUMO

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SUMO “Process” Examples

• Appearance as argument number 2 (1 of 5)– subclass DualObjectProcess Process

• dual object process is a subclass of process

• Appearance as argument number 3 (1 of 27)– domain agent 1 Process

• the number 1 argument of agent is an instance of process

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SUMO “Process” Examples

• Antecedent (1 of 4)(=>

(and

(instance ?PROC Process)

(subProcess ?SUBPROC ?PROC))

(exists (?TIME)

(time ?SUBPROC ?TIME)))– if ?PROC is an instance of process and ?SUBPROC is a

subprocess of ?PROC, then there exists ?TIME so that ?SUBPROC exists during ?TIME

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Questions to consider

• Ontolog group’s focus on interoperability needs

of NHIN & FHA architects, – How should ontologies enable the interoperability of

patient health records? – How should existing and prospective health domain

ontologies and taxonomies be aligned with upper ontologies to improve the accuracy of conceptual information transfer ?

– Especially among systems using different domain knowledge representations.

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Next Steps for Federal Architects

• Help with the Definitions and Relationships for the Health Domains – so they connect between the FEA Reference Model

Ontology and Domain-Specific Ontologies.

• Help with Reuse of Upper Ontology Components in Domain Ontologies.

• Identify Other Examples of Domain Ontologies and Ontology-Driven Information Systems That Will Serve as Best Practice Examples.

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Mark Musen, MD, PhD

• Mark Musen, MD, PhD:– Professor of Medicine and, by courtesy, Computer

Science, Stanford University School of Medicine.– Dr. Musen's research interests include

• knowledge modeling in biology and medicine, knowledge management,

• automated support for clinical-practice guidelines and for clinical trials, and

• knowledge-based approaches to public-health surveillance.

– http://clinicalinformatics.stanford.edu/scci_seminars/2004-05.html