ISO/IEC Standards for

1

• ISO/IEC Standards for
• Semantic Computing
• What? Why?
• Wuhan Symposium on Ontology and MetaModeling
• Wuhan University, P. R. China
• March 16-17, 2006

Bruce Bargmeyer, Lawrence Berkley National
Laboratory University of California Tel 1
510-495-2905 bebargmeyer_at_lbl.gov
2
Topics

• ISO/IEC Standards for Semantic Computing
• What are the advances that we are working on?
• Why do we want to make these advances? What
  issues are we addressing? What is our
  motivation?
• Focus on Standards under development in ISO/IEC,
  JTC 1/SC 32 (Data Management and Interchange) WG
  2 (Metadata)
• ISO/IEC 19763 Framework for Metamodel
  Interoperability
• ISO/IEC 24707 Common Logic
• ISO/IEC 11179 Metadata Registries
• These three standards address similar challenges
• Align these with standards from ISO TC 37 -
  Terminology and other language and content
  resources (as well as with W3C OMG)
• How do we want to make these advances?
  Collaborative RD between the US/Canada, Europe,
  Asia
• Presentation available at xmdr.org under
  presentations or at http//hpcrd.lbl.gov/SDM/XMD
  R/presentations/

3
Some Inspirational ISO TC 37 Standards

• ISO 704 Terminology work -- Principles and
  methods
• ISO 860 Terminology work -- Harmonization of
  concepts and terms
• ISO 1087 Terminology work -- Vocabulary -- Part
  1 Theory and application
• ISO 1087 Terminology work -- Vocabulary -- Part
  2 Computer applications
• ISO 12200 Computer applications in terminology
  -- Machine-readable terminology interchange
  format (MARTIF) -- Negotiated interchange
• ISO 12620 Computer applications in terminology
  -- Data categories
• ISO 16642 Computer applications in terminology
  -- Terminological markup framework
• TC 37 term concept systems includes thesauri,
  taxonomies, ontologies,

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Where have we been? Where are we planning to go?
Semantics
System manuals
Semantic grids
Data dictionaries
Semantics services (SSOA)
11179 E1
Data ontology lifecycle management
XMDR Project
Data Standards
11179 E2
Complex semantics management
Data Management/ Data Administration
ISO/IEC 11179 E3 19763 P 1-4 24707
Data engineering
Terminologies
Metadata Registries (MDR)
Semantic Web Ontologies
XML related standards
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Evolution of metadata technology

• From unstructured natural language text metadata
  to structured metadata
• explicit modeling and characterization of
  relationships
• graph based metamodels to aid comprehension and
  searching
• formal ontologies
• support for inference
• AND from human consumption to machine processing
  for
• detailed query/search
• inference (e.g., transitive search, subsumption
  testing, etc.),
• Mapping between equivalent value domains, units
  conversion,
• With new key technologies
• Graph databases (e.g., RDF) facilitate
  visualization machine processing
• Description logic (e.g., OWL DL) for more precise
  semantics machine reasoning
• Software Reasoners (e.g., inference engines)

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Metadata standards have addressed different
realms of metadata
Conceptual Models (of the real world)
Information Artifacts
OMG Standards MOF, UML, CWM schemas, models,
Terminology Standards ISO 1082, thesauri,

• 11179 E2 Metadata Registry Std
• records links both types of metadata
• (but less formal structure on conceptual side)

Ontology Standards OWL, CL (ISO/IEC 24707), ....

• Data Element Concepts
• Conceptual Domains
• Classification Schemes
• ..

• Data Elements
• Value Domains
• e.g, enumerated
• ..

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Aligning different realms of metadata standards
Conceptual Models (of the real world)
Information Artifacts
OMG Standards MOF, UML, CWM schemas, models,
Terminology Standards ISO 1082, thesauri,
11179 E3 Metadata Registry

• Data Element Concepts
• Conceptual Domains
• Concepts
• Ontologies
• ..

• Data Elements
• Value Domains
• e.g, enumerated
• ..

Ontology Standards OWL,CL (ISO/IEC 24707),, ....
Incorporating more structured and formal semantic
components to facilitate data integration,
harmonization, information discovery, and
semantic computing.
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Aligning different realms of metadata standards
Conceptual Models (of the real world)
Information Artifacts
OMG Standards MOF, UML, CWM schemas, models,
Terminology Standards ISO 1082, thesauri,
19763 Framework for Metamodel Interoperability

• Ontologies
• Metamodels
• ..

Ontology Standards OWL, CL (ISO/IEC 24707), ....
Incorporating more structured and formal semantic
components to facilitate data integration,
harmonization, information discovery, and
semantic computing.
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Major Issues in Semantics Management addressed by
ISO/IEC 19763, 24707 and 11179

• Independent development and autonomous evolution
• Multiple ways to specify the same thing within a
  language (formalism, notation) and between
  languages
• Precise specification so that software (agents,
  applications, systems) can process without human
  intervention
• Harmonization and vetting within a community of
  interest
• Life cycle management (data, concept systems,
  ....)
• Processing based on semantic reasoning, rather
  than procedure

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ISO/IEC 19763

• Framework for Metamodel Interoperability

The following slides are adapted from an FMI
project presentation.
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What ISO/IEC 19763Framework for Metamodel
Interoperability

• Objective
• Promote interoperability based on ontologies.
• Obstacles to ontology-based interoperation
• Issue 1
• Each ontology is developed independently and
  evolves autonomously.
• Issue 2
• Ontologies are described in several languages,
  sometimes with different names for the same thing
  in a Universe of Discourse or with the same name
  for different things in a UoD.
• FMI is to solve these problems, providing a
  registration framework for ontologies.

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Issue Difficulty caused by independent
development and autonomous evolution
This ontology has a definition of green card
and does not have a definition of Christmas
card.
This ontology does not have a definition of
green card but has a definition of Christmas
card.

• To avoid this difficulty, FMI Ontology
  Registration provides two types of ontologies,
  Reference Ontology and Local Ontology.

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Reference ontology and local ontology

• Reference Ontology
• standardized ontology
• for some business domain
• pre-defined and relatively stable

Reference Ontology3
Reference Ontology1
Reference Ontology2
Local Ontology for application system B
Local Ontology for application system A

• Local ontology
• localized ontology for some application system
  based on reference ontologies
• relatively unstable and evolves autonomously and
  continuously.

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With Reference Ontology

• FMI Ontology Registration provides the
  registration framework where a local ontology is
  defined based on reference ontologies

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ISO/IEC 24707

• Common Logic

The following slides are adapted from a Common
Logic project presentation.
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What Goal of Common Logic

• Two agents, A and B, each have a first-order
  formalization of some knowledge
• A and B wish to communicate their knowledge to
  each other so as to draw some conclusions.
• Any inferences which B draws from A's input
  should also be derivable by A using basic logical
  principles, and vice versa
• The goal of Common Logic is to provide a logical
  framework which can support this kind of use and
  communication without requiring complex
  negotiations between the agents.

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Issue Syntax

• A and B may have used different surface syntactic
  forms to express their knowledge.
• A well-known problem
• Usually solved by defining a standard syntax into
  which others can be translated, such as KIF
• CL provides a common 'interlingua' syntax XCL
  into which the others can be translated.
• XCL uses XML concepts and design principles
• Provides a clean separation between the
  description of logical form and the surface
  syntactic form appropriate to a particular usage.
  
• Allows for linking of CL text across documents
  and conveying CL written in non-XCL syntaxes
  between applications using XML protocols.

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Issue Different Axiomatic Styles

• A and B may have made divergent assumptions about
  the logical signatures of their formalizations.
• A uses relation name where B uses function
• A and B use same relation with different argument
  orderings or different numbers of arguments.
• A particular concept, such as marriage, might be
  represented by A as an individual, but by B as a
  relation.
• Can be solved by mappings between the logical
  forms of such divergent choices
• CL removes conventional limitations on
  first-order signatures
• For example, a name in CL may serve both as an
  individual name and as a relation name.

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Issue Multiple Domains

• A and B may be developed with different intended
  universes of discourse in mind
• Assertions may be misinterpreted due to this
  difference
• Assertions in a domain might be interpreted to be
  talking about things that they have not even
  conceived of
• E.g., taxonomic classifications of animals
• complement of the set of mammals may be taken
  to include fruit, sodium molecules, styles of
  avant-garde paintings or the names of fictional
  characters in movies.
• CL has a syntactic form to help resolve this
  issue
• a 'top-level' syntactic form called a module
  which automatically gives a name to the universe
  of discourse of a named ontology
• Automatically inserts namespace on any
  contained quantifiers when information is
  combined.

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ISO/IEC 11179

• Metadata Registries
• -- With enhancements proposed by the
• eXtended Metadata Registry (XMDR) project

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What ISO/IEC 11179Metadata Registry Extensions

• Register (and manage) any semantic artifacts that
  are useful for managing data.
• E.g., this includes registering concepts in any
  way related to data e.g., permissible values
  and data element definitions.
• It extends to registration of the full concept
  systems related to an organizations information
  held in structured, semi-structured or
  unstructured (text) form.
• E.g., may want to register keywords, thesauri,
  taxonomies, ontologies, axiomatized ontologies.
• Provide new services for semantic computing
  Semantics Service Oriented Architecture, Semantic
  Grids, semantics based workflows, Semantic Web .

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What ISO/IEC 11179Metadata Registry Extensions

• In addition to natural language, we want to
  capture semantics with more formal techniques
• First Order Logic, Description Logic, Common
  Logic, OWL
• However, maintain backward compatibility for
  implementers of 11179 E2

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Motivation Urgent demands for Data Integration
and Harmonization

• Facilitate consolidation reorganization of
  government, private companies, and other
  organizations
• Ongoing acquisitions and mergers of organizations
• Corporations E.g, telecon, energy, banking,
• Government E.g., many agencies put under Dept of
  Homeland Security
• In National Institute of Heath, the National
  Cancer Institute was created to focus on cancer
• Enable cooperation between countries and groups
• World Trade Organization
• North American Free Trade agreement
• European environment Basel Convention
• UN Food and Agriculture global food supply
• Enable sharing of data required quickly for
  emergencies
• Bird flu terrorism

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Motivation for ISO/IEC 11179Metadata Registry
Extensions

• Support traditional data management and data
  administration in more powerful way.
• Go beyond traditional Data Standards and Data
  Administration. We want to support computer
  processing based on semantics--concepts and
  relationships.

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Strong Commonality of Purpose19763 24707
11179

• Semantics management - creating, managing,
  harmonizing, using, exchanging,
• Data,
• Concepts relationships (concept systems),
• Sentences/axioms,
• Created by diverse organizations,
• For diverse purposes
• Management approach coordinate and cultivate,
  rather than top-down command and control

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Can we align these standards? 19763, 24707, 11179
MOF ODM -- Can this picture work?
Ontology Evolution
11179-3
MOF
ISO/IEC19763
ISO/IEC 11179
Administered Item
Administered Item
Content Management
Metamodels for Basic Ontology Constructs
Registration Metamodel
XMDR Registry
Query Service
ODM Metamodel for CL
Normative Basic Elements
ODM Metamodel for OWL
Terminology Basic Classes Basic
Relationship
Ontologies
Analysis and Extraction
Registering
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More Description of Proposed Direction for
ISO/IEC 11179
28
Who uses metadata registries for what purposes?

• Analysts, researchers, managers, public who use
  existing information. They use an MDR
• To find what the data means and what is its
  meaning and provenance (e.g. quality, purpose of
  collecting the data)
• For legacy systems as well as recent ones, even
  data warehouses
• Often want to use the data for purposes other
  than the reason that the data was collected
  (secondary, tertiary use)
• Creators of new systems, schemas, databases
• To maintain compatibility with existing data and
  document new data
• To integrate existing information in new ways
• To meet requirements for data re-use (data
  standardization)

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Who could use extended metadata registries for
what purposes?

• Analysts, researchers anyone trying to create,
  harmonize, and manage data, concept systems,
  knowledge bases, rule bases, ontologies, RDF
  statements
• Engineering and Harmonization
• Vetting (gaining approval), establish trust, and
  enable stewardship
• Creators of new semantic computing systems
  applications
• Ground OWL ontologies and RDF statements
  (subjects, predicates, objects) in agreed upon
  definitions maintained in a metadata registry
• Use managed semantics within a community of
  interest
• Integrate existing semantics in new ways
• Improve semantics re-use
• Computers that are processing semantic computing
  applications
• Agents to access, map, and reason over data and
  concepts
• Applications that interact with both concepts in
  concept systems and data in databases.
• Grid computing - grid software can use the MDR
  XML representations for exchanging comparing
  objects (also, possibly RDF or OWL
  representations). Service Metadata in an MDR
  can be used on the grid to support semantic
  service discovery, service consolidation and
  dynamic creation of services workflows.

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Advanced Semantics Use Scenario (One of Many)
For MDR

• A user wants to study any possible association
  between asthma and particulate emissions (small
  particles in polluted air).
• User wants to discover any documents (e.g.,
  studies) about asthma, potential correlation of
  asthma with particulate emissions, possible
  treatments, persons affected, and researchers in
  this field (distinguishing between reliable and
  unreliable sources)
• In different documents, terms used for the same
  concepts may vary by regions, disciplines,
  scientific nomenclature, vernacular usage, and
  language.
• Relevant terms (concepts) may be related through
  generalization and specialization and through
  other relationships between concepts.
• User may want to link concepts and individuals
  found in documents to data in databases
• This requires linking terminology in concept
  systems to metadata describing data.
• Note No assumption of federation or central
  control over data and text generation. (However,
  well managed concept systems and metadata--e.g.,
  data definitionswould help to generate more
  coherent information in the future.

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Semantics Scenarios

• Semantics scenario for asthma and particulates
  not yet fully developed and demonstrated.
• The Cancer Bioinformatics Grid is engaging the
  challenge for similar scenarios
• Researcher a, patient b, cancer c, treatment d
• Looking across literature and data, past and
  present
• Designing for the future
• Semantics services support researchers and public
• Defense intelligence is a leading edge
  application
• Person a, associate b, substance c, event
  d--where all may be coded and probabilistic.
• Semantics services to support analysis workflow.

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Semantics Scenarios

• MDR support for these scenarios requires stronger
  semantics management linking concept systems to
  other metadata.

33
How can we overcome current limitations of 11179
technology?

• Add more rigorous formal specification for
• Concepts and concept systems (ontologies)
• Relationships between metamodel components
• Continue evolution toward increasing granularity
  details
• Use concepts to unify different types of metadata
• and axioms for conceptual structural
  relationships
• Support more powerful software tools
• for richer text searching beyond relational
  technology
• for inference queries based on structural
  metadata
• Build interfaces to aid searching navigation
• hide complexities of inference queries
• combine text searching and inference
• Bridge the realms of concepts data
• Align with other relevant standards

34
How do proposals for 11179 E3 differ from current
11179 technology?

• Evolutionary aspects
• Finer-grained, more formal metadata
• e.g., distinct attributes for measurement units
• rather than just part of textual description
• Revolutionary aspects
• Use of formal ontologies, logic, and inference
• to specify 11179 metamodel
• to store, search, retrieve and display metadata
• Use of machine inference queries to complement
  relational queries for content searching
• Use of logic engines machine reasoning

35
Challenge Draw Together
Terminology
Metadata Registries
11179 Metadata Registry
36
NCI caDSR XMDR-like
Concept System (Enterprise Vocabulary Services
(EVS)) integrated with 11179 metadata
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Concept Management

• In general, we want to register any concept based
  graph structure comprised of nodes,
  relationships, and possibly axioms
• possibly including millions of concepts, millions
  of terms, and millions of relationships (maybe
  billions).
• We want to link the concepts (e.g., research
  organization w, person x, disease y, location z)
  to data and text.

38
Example Concept Systems

• NBII Biocomplexity Thesaurus
• National Cancer Institute Metathesaurus
• NCI Data Elements (National Cancer Institute
  Data Standards Registry
• UMLS (non-proprietary portions)
• GEMET (General Multilingual Environmental
  Thesaurus)
• EDR Data Elements (Environmental Data Registry)
• USGS Geographic Names Information System (GNIS)
  HL7 Terminology, Data Elements
• Mouse Anatomy
• GO (Gene Ontology)
• EPA Web Registry Controlled Vocabulary
• BioPAX Ontology
• NASA SWEET Ontologies

39
Concepts and traditional metadata can be
represented queried as graphs
Nodes represent concepts or types of metadata
A
Lines (arcs) represent relationships
2
1
b
a
c
d
40
Finding Hidden Information in Registry Metadata
(Including Concept Systems)
Waterfowl
Waterfowl
Goose
Duck
Goose
Duck
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Include Concept System Semantics in Metadata
Registries
Represent concepts and relationships as nodes
and edges in formal graph structures e.g., is-a
hierarchies.
Waterfowl
Duck
Goose
42
What new search capabilities do graph models
inference support?

• SQL-like structured queries (e.g., RDQL)
• e.g., SELECT ?x WHERE (?x rdftype
  xmdrValueDomain)
• Can span items that are only indirectly connected
• e.g., data elements associated with a permissible
  value
• Expand queries to subsumed classes in a hierarchy
• e.g., all cities within state and states within
  countries (partonomy)
• e.g., all species subsumed under birds
  (taxonomy)
• Search for higher level concepts or metadata
• e.g., all superclasses (ancestors) of a
  particular class
• e.g., least common ancestor (subsuming concept)
  for cat and snake
• Explore sibling items
• e.g., other airport codes comparable to SFO

43
Inference
Disease
is-a
is-a
Infectious Disease
Chronic Disease
is-a
is-a
is-a
is-a
Heart disease
Polio
Smallpox
Diabetes
Signifies inferred is-a relationship
44
Taxonomies partonomies can be used to support
inference queries
E.g., if a database contains information on
events by city, we could query that database for
events that happened in a particular county or
state, even though the event data does not
contain explicit state or county codes.
45
Relationship metadata can be used to infer
non-explicit data
Analgesic Agent

• For example
• patient data on drugs currently being taken
  contains brand names (e.g. Tylenol, Anacin-3,
  Datril,)
• (2) thesaurus connects different drug types and
  names with one another (via is-a, part-of, etc.
  relationships)
• (3) so patient data can be linked and searched
  by inferred terms like acetominophen and
  analgesic as well as trade names explicitly
  stored as text strings in the database

Non-Narcotic Analgesic
Analgesic and Antipyretic
Acetominophen
Nonsteroidal Antiinflammatory Drug
Datril
Anacin-3
Tylenol
46
Least Common Ancestor Query
What is the least common ancestor concept in NCI
Thesaurus for Acetominophen and Morphine
Sulfate? (answer Analgesic Agent)
Analgesic and Antipyretic
47
Example sibling queries concepts that share
ancestor

• Environmental
• "siblings" of Wetland (in SWEET ontology)
• Health
• Siblings of ERK1 finds all 700 other kinase
  enzymes
• Siblings of Novastatin finds all other statins
• 11179 Metadata
• Sibling values in an enumerated value domain

48
More complex sibling queries concepts with
multiple ancestors

• Health
• Find all the siblings of Breast Neoplasm
• Environmental
• Find all chemicals that are a
• carcinogen (cause cancer) and
• toxin (are poisonous) and
• terratogen (cause birth defects)

site neoplasms
breast disorders
Breast neoplasm
Non-Neoplastic Breast Disorder
Eye neoplasm
Respiratory System neoplasm
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What new support can MDRs give to Semantic Web
Applications?
MDR may be used to ground the Semantics of an
RDF Statement.
The address state code is AB. This can be
expressed as a directed Graph e.g., an RDF
statement
50
What does the RDF statement mean?
Subject Address Predicate State
Code Object AB

• What kind of address is this?
• Location address?
• Mailing address?
• What is a state code?
• What code set is used?

Solution Establish a vocabulary that can be
referenced by all who might want to use or
understand this statement.
51
Semantic Precision Terms from an MDR
A more precise triple
Subject
Predicate
Object
Mailing Address Or Mailing Address
State USPS Code Or Mailing AddressState Name
AB Or Alberta
Deeper level Show that the value meaning for
Alberta isThe Canadian Province of Alberta.
52
Grounding RDF nodes and relations URIs
Reference a Metadata Registry
dbAe0139
ai MailingAddress
dbAma344
ai StateUSPSCode
ABaiStateCode
_at_prefix dbA http/www.epa.gov/databaseA _at_prefix
ai http//www.epa/gov/edr/sw/AdministeredItem
53
URI Resolution in a Metadata Registry
Node and relationship meaning established through
a URI pointing to an ISO/IEC 11179 Metadata
Registry
Mailing Address
http//www.epa/gov/edr/sw/AdministeredItemMailin
gAddress

• The exact address where a mail piece is intended
  to be delivered, including urban-style address,
  rural route, and PO Box

State USPS Code
http//www.epa/gov/edr/sw/AdministeredItemStateU
SPSCode

• The U.S. Postal Service (USPS) abbreviation that
  represents a state or state equivalent for the
  U.S. or Canada

Mailing Address State Name
http//www.epa/gov/edr/sw/AdministeredItemStateN
ame

• The name of the state where mail is delivered

Needed Persistent URIs pointing to each item in
a 11179 Metadata Registry (Not currently part of
the standard).
54
Metadata relationships can also be used to infer
connected information
Database

• For example
• An agency has hundreds of different databases,
  with metadata for each in a 11179 Registry
  .
• Manager asks which databases can be searched to
  find specific information for China?
• Search code values for China ( synonyms like
  CN) and show all databases that are connected
  only indirectly via Ennumerated Value Domain,
  Value Domain and Permissible Value

Data Element
Value Domain
Ennumerated Value Domain
Non-ennumerated Value Domain
Permissible Value
Permissible Value Meaning
55
Different ontologies support semantics management
at different levels
11179 classes, properties, and relationships
Metamodel Level
Concepts and Terms
11179 Registry Level
Database B
Application Software Level
Database A
56
Nodes and relations support inference on 11179
metamodel
57
Another Use Scenario Allergy AlertLinking
concept searches, metadata searches, and database
queries (outline)

• Event Doctor prescribes medicine. Will patient
  have allergic reaction?
• Event triggers concept system search to determine
  if the prescription is a drug and if so, what
  type of drug. The first search is for an isA
  relation, followed by a search for a partonomy
  relation
• Then system must perform a metadata search to
  find data elements in information systems
  relating to patient allergy
• Result of metadata search enables database lookup
  in patient record
• Database lookup produces a drug reaction code
• System must look up the code in a concept
  systemto find type of reaction and category of
  drug
• Relate drug reaction to category of prescribed
  drug
• Produce Allergy Alert for Dr. Patient

58
Scenario Allergy Alert

• Event
• Prescription 500 mg Prevpac bid

59
Scenario Allergy Alert

• Is this a prescription for a drug?
• Yes concept system lookup says prescription
  category is for drugs and devices, That is,
  Prevpac isA Drug
• If so, what category (ies) of drug?
• Lookup in Concept system (partonomy) shows that
  Prevpac contains
• Lansoprozole - proton pump inhibitor
• Amoxicillin - beta-lactam antibiotic
• Clarithromycin - macrolide antibiotic

60
Scenario Allergy Alert

• Does the patient have an allergy to any of the
  drugs?
• Need to Metadata lookup to find relevant data
  elements in patient record databases
• Need to join the contents of the database(s)
• Diagnosis Allergy to ___________
• Observation Apparent reaction to ______

61
Scenario Allergy Alert

• Search Database Patient Record
• Result Dx ICD-9-CM code 996.2 Unspecified
  adverse effect of drug, medicinal and biologic
  substances
• Search Concept System
• Result Adverse reaction SNOMED
• 294461000 (antibacterial drug allergy)
• 246075003 (causative agent)
• 392284008 (nafcillin)

62
Scenario Allergy Alert
All of these contain a form of penicillin
63
Penicillin Allergy
64
Scenario Allergy Alert
Nafcillin isA Penicillin Amoxicillin isA
Penicillin
65
Alert

• Warning!!! Patient has had a prior adverse
  reaction to Nafcillin which is similar to the
  component Amoxicillin in the current
  prescription.
• Note The Rand Corporation states that billions
  of dollars per year can be saved in healthcare
  expenditures and better results can be achieved
  with improved medical systems of this type.
• --Rand Review, Fall 2005

66
Summary MDR Concept System Store
Concept systems Keywords Controlled
Vocabularies Thesauri Taxonomies Ontologies Axioma
tized Ontologies (Essentially graphs
node-relation-node axioms)

ISO/IEC 11179 Metadata Registry
67
Summary MDR to Manage Concept Systems
Concept system Registration Harmonization
Standardization Acceptance (vetting) Mapping
(correspondences)

ISO/IEC 11179 Metadata Registry
68
Summary MDR for Life Cycle Management

Life cycle management Data and Concept
systems (ontologies)
ISO/IEC 11179 Metadata Registry
69
Summary MDR for Grounding Semantics
Metadata Registries
Semantic Web RDF Triples Subject (node URI) Verb
(relation URI) Object (node URI)
Ontologies
ISO/IEC 11179 Metadata Registry
70
Challenges and Future Goals for XMDR RD
Prototype

• Evaluate alternative technologies
• For different modules of the XMDR metadata
  registry architecture
• Test Tools
• Best software for architecture components
• RDF tool adaptation for metadata registries
• User-friendly interface
• Form interface for registration uploading
  metadata
• Demonstrate key use cases and applications
• References to externally maintained content
• Data, ontologies, terminologies
• Scalability performance

71
Challenges and Future Goals (cont)

• Progress proposals through standards committees
• Harmonize with W3C and OMG standards
• Incorporate Common Logic, Web Services, etc.
• Ontology Lifecycle Management (OLM)
• Mapping between concepts
• Improve linkage of concepts to data
• Generate schemas from axiomatized ontologies

72
Sharing Research Results

• At this symposium, there will be several
  presentations of research results and advanced
  development by people from the US and EU and
  Asia.
• I especially look forward to hearing progress in
  research underway at Wuhan University.
• This sharing of information is key to making
  rapid advances in this area.