# Presentation: 542483

## ISO/IEC Standards for 
Semantic Computing – 
What? Why?

Wuhan Symposium on Ontology and MetaModeling
Wuhan University, P. R. China
March 16-17, 2006

**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@lbl.gov

## 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
- 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 R&D between the US/Canada, Europe, Asia
- This Presentation available at: xmdr.org under “presentations” or at http://hpcrd.lbl.gov/SDM/XMDR/presentations/Bruce-Bargmeyer-at-Wuhan-Symposium-March-16-2006.ppt

## 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, ...*

## Where have we been? Where are we planning to go?

**Where have we been? ****Where are we planning to go?**

- System manuals

- Data dictionaries

- 11179 E1

- ISO/IEC
- 11179 E3
- 19763 P 1-4
- 24707

- XML & related standards

**Semantic grids**** **

- 11179 E2

**Semantics services (SSOA)**

**Complex**
** ****semantics **
**management**

- Data engineering

**Data Standards**

**XMDR Project**

- Semantics:

- Semantic Web & Ontologies

- Data + ontology lifecycle
- management

- Terminologies

- Data Management/
- Data Administration

**Metadata Registries (MDR)**

## Evolution of metadata technology

- From unstructured natural language metadata (written as text) to structured metadata
- Explicit modeling and characterization of relationships
- Graph based metamodels to aid comprehension and searching
- Formal ontologies
- AND from human consumption to machine processing for
- Software agents
- Computing inferences
- Semantic applications (e.g., transitive search, subsumption testing, etc.),
- Semantic services, E.g., 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)

## Metadata standards have addressed different realms of metadata 

**OMG Standards:**
- MOF, UML, CWM
*schemas, models, ...*

**Terminology**
**Standards**
- ISO 1082, thesauri, ...

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

_**Information Artifacts**_

_**Conceptual Models**_
**(of the “real world”)**

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

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

**Data Elements**
**Value Domains  **
- e.g, enumerated
**...****..**

## Aligning different realms of metadata standards

**Standards**
- ISO 1082, thesauri, ...

_**Information Artifacts**_

_**Conceptual Models**_
**(of the “real world”)**

**OMG Standards:**
- MOF, UML, CWM
*schemas, models, ...*

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

** **
**11179 E3 Metadata Registry**

**Data Element Concepts**
**Conceptual Domains**
**Concepts**
**Ontologies**
**...****..**

**Data Elements**
**Value Domains  **
- e.g, enumerated
**...****..**

***Incorporating more structured and formal semantic components***
***to facilitate data integration, harmonization, information discovery, ***
***and semantic computing.***

## Aligning different realms of metadata standards

_**Information Artifacts**_

_**Conceptual Models**_
**(of the “real world”)**

**OMG Standards:**
- MOF, UML, CWM
*schemas, models, ...*

**Terminology**
**Standards**
- ISO 1082, thesauri, ...

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

** **
**19763 – Framework for Metamodel Interoperability**

**Ontologies**
**Metamodels**
**...****..**

***Incorporating more structured and formal semantic components***
***to facilitate data integration, harmonization, information discovery,***
***and semantic computing.***

## 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

## ISO/IEC 19763

-  Framework for Metamodel Interoperability

- The following slides are adapted from an FMI project presentation.

## What: ISO/IEC 19763Framework 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.

## 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.

**Notes:**
cartoony  communication cannot be established

## Reference ontology and local ontology

- Reference Ontology3

- Local Ontology for application system A

- Local Ontology for application system B

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

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

- Reference Ontology1

- Reference Ontology2

## With Reference Ontology

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

## ISO/IEC 24707

- Common Logic

- The following slides are adapted from a Common Logic project presentation.

## 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 logic based framework which can support this kind of use and communication without requiring complex negotiations between the agents.

## 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.** **

## 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.

## 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.

## ISO/IEC 11179

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

## What: ISO/IEC 11179Metadata 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 organization’s 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 ....

## What: ISO/IEC 11179Metadata 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

## 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 Institutes 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; ...

**Notes:**
Note that the names and venues change, but the underlying requirements are remarkably similar.

Namely to be able to see over-all data resources, reduce redundancy, increase inter-operability, facilitate information discovery

Whether the time available to do so is years or weeks

Whether integration is voluntary or top-down

Whether organizations are private corporations or governments at local, state, national, or international levels

Emergencies very similar – only they have a much shorter time-frame

## Motivation for ISO/IEC 11179Metadata 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.

## Strong Commonality of Purpose19763 & 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”

## Can we align these standards? 19763, 24707, 11179 & MOF & ODM -- Can this picture work? 

- Administered Item

- Metamodels for Basic Ontology Constructs

- Can we align these standards? 19763, 24707, 11179 & MOF & ODM -- Can this picture work?

- Administered Item

-   Registration   Metamodel

- ISO/IEC 11179

-  **Normative Basic  **
**Elements**

- ISO/IEC19763

- Ontologies

-    11179-3

- Terminology        Basic Classes         Basic Relationship

- XMDR Registry

- Ontology
- Evolution

- Content
- Management

- Query
- Service

- Analysis  and Extraction

**ODM Metamodel **
**for CL**

-    MOF

- Registering

**ODM Metamodel**
**for OWL**

## More Description of Proposed Direction for ISO/IEC 11179

## 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 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 users)
- 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)

## 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.

## 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 definitions—would help to generate more coherent information in the future.**

## 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.

## Semantics Scenarios

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

## 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

## 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

**Notes:**
Many years ago, I remember Frank Olken asserting how we needed 
to store complete and detailed schema information in
graph databases so we could use logic programming to do 
Automatic inferencing in order to search and navigate
Large and complex universes of metadata.

At the time, it seemed pretty esoteric, academic, and impractical.
But in the meantime logic programming and inference technology 
Has matured, computing capabilities have grown substantially,
and our metadata challenges have grown as well.

And now I’m finally beginning to understand Frank’s prophetic vision
As something we can now implement in practical ways in order
To dramatically improve the capabilities and potential of metadata registries.

## Challenge: Draw Together

**Metadata**
**Registries**

**Terminology**

- 11179 Metadata Registry

## NCI caDSR “XMDR-like”

- Concept System (Enterprise Vocabulary Services (EVS)) integrated with 11179 metadata

**Notes:**
EVS=Enterprise Vocabulary Services. We talked about a Data Element being formed by a concept taking on a specific representation.  In ISO 11179 terms this translates to the combination of a specific Data Element Concept and a specific Value Domain, you see this denoted in the middle of this chart by the yellow box outlined in red.  caDSR administered items are backed by the use of externally defined terminologies and controlled vocabularies.  With UMLS as a framework NCI has developed vocabulary services that are accessed via API – application to application interfaces – to provide touch points during creation of content, resulting in administered components that are bound to immutable concept codes.  These touchpoints, denoted by the EVS logos, are currently implemented at the Object Class, Property, Representation Term, Value Domain and Valid Value levels of the metadata model.   

## 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.

## 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
- ...

**Notes:**
Collaborative, Interagency effort to Extend the Semantics Management Capabilities of ISO/IEC 11179
Test and Demonstrate Extended Capabilities with a Reference Implementation
Serve as a Design for Operational 11179 Registries
Focuses on Extending Classification and Terminology Aspects Specified in 11179 Part 2
Adapting and Adopting Useful Emerging Technologies
Result of Work will be Draft Documents that Propose Revisions for ISO/IEC 11179 Parts 2 and 3 as Version 3
Additionally, the Reference Implementation (RI) Prototype will Help Resolve Issues of How to Register, Manage, and Interrelate Complex Metadata Standards

## Concepts and traditional metadata can be represented & queried as graphs

**A **

**2 **

**b **

**a **

**c **

**d **

**1 **

- Nodes represent concepts or types of metadata

- Lines (arcs) represent relationships

## Finding Hidden Information in Registry Metadata (Including Concept Systems)

- Goose

- Duck

- Waterfowl

- Duck

- Goose

- Waterfowl

##  Include Concept System Semantics in Metadata Registries

- Goose

- Duck

- Waterfowl

**Represent concepts and relationships **
**as nodes and edges in formal graph structures**
**e.g., “is-a” hierarchies.**

## What new search capabilities do graph models & inference support?

- SQL-like structured queries (e.g., RDQL)
***e.g., ******SELECT ?x  WHERE (?x rdf:type xmdr:ValueDomain)***
- 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”***

## Inference

**Polio**

**Smallpox**

**Infectious Disease**

**Disease**

**is-a**

**is-a**

**is-a**

**is-a**

**is-a**

**Diabetes**

**Heart disease**

**Chronic Disease**

**is-a**

- Signifies inferred is-a relationship

## 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.**

## Relationship metadata can be used to infer non-explicit data

- Analgesic Agent

- Non-Narcotic Analgesic

- Acetominophen

- Nonsteroidal Antiinflammatory Drug

- Analgesic and Antipyretic

- Datril

- Anacin-3

- Tylenol

***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**

## Least Common Ancestor Query 

- Analgesic and Antipyretic

- What is the least common ancestor concept in NCI Thesaurus for
**Acetominophen**** **and** ****Morphine Sulfate**? (answer = Analgesic Agent)

## 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

## 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)

- 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)

- More complex “sibling” queries: concepts with multiple ancestors

**site neoplasms**

**breast disorders**

**Breast **
**neoplasm**

**Respiratory**
**System **
**neoplasm**

**Non-Neoplastic Breast Disorder**

**Eye **
**neoplasm**

## What new support can MDRs give to Semantic Web Applications?

- The address state code is “AB”.  This can be expressed as a directed
- Graph e.g., an RDF statement:

- MDR may be used to “ground” the Semantics
- of an RDF Statement.

## What does the RDF statement mean?

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

**Subject:****      ****Address**
**Predicate:****   ****State Code**
**Object:****        ****AB**

- _Solution:_  Establish a vocabulary that can be referenced by all who might want to use or understand this statement.

## Semantic Precision: Terms from an MDR

- A more precise triple:

***Mailing Address***
***Or ***
***Mailing Address***

***State USPS Code ***
***Or ***
***Mailing Address******State Name***

***AB ***
***Or ***
***Alberta***

- _Deeper level_:  Show that the value meaning for Alberta is:The Canadian Province of Alberta.

***Subject***

***Predicate***

***Object***

## Grounding RDF nodes and relations – URIs Reference a Metadata Registry

**dbA:ma344**

**“****AB”^^ai:StateCode**

**      ****ai: StateUSPSCode**

**@prefix dbA: “http:/www.epa.gov/databaseA”**
**@prefix ai: “http://www.epa/gov/edr/sw/AdministeredItem#”**

**dbA:e0139**

**ai: MailingAddress**

## URI Resolution – in a Metadata Registry

**“****http://www.epa/gov/edr/sw/AdministeredItem#MailingAddress”**

**“****http://www.epa/gov/edr/sw/AdministeredItem#StateUSPSCode”**

**“****http://www.epa/gov/edr/sw/AdministeredItem#StateName”**

- Node and relationship meaning established through a URI pointing to an ISO/IEC 11179 Metadata Registry

**Mailing Address:**

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

**State USPS Code:**

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

**Mailing Address State Name:**

- 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).

## Metadata relationships can also be used to infer connected information

- Data Element

- Value Domain

- Ennumerated Value Domain

- Non-ennumerated Value Domain

- Permissible Value
- Meaning

- Permissible
- Value

- 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

- Database

## Different ontologies support semantics management at different levels

- 11179 classes, properties, and relationships

**11179**
**Metamodel Level**

**11179 Registry Level**

**Application Software Level**

- Concepts and Terms

- Database A

- Database B

## Nodes and relations support inference on 11179 metamodel

## Another Use Scenario: Allergy AlertLinking 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 a database lookup in a patient record
- Database lookup produces a drug reaction code
- System must look up the code in a concept system—to find type of reaction and category of drug
- Relate drug reaction to category of prescribed drug
- Produce Allergy Alert for Dr. & Patient

## Scenario: Allergy Alert

- Event:
- Prescription: 500 mg Prevpac  bid

## 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

## 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 ______

## 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)

## Scenario: Allergy Alert

- All of these *contain* a form of penicillin

## Penicillin Allergy

## Scenario: Allergy Alert

- Nafcillin isA Penicillin
- Amoxicillin isA Penicillin

## 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

## Summary: MDR Concept System Store

**Concept systems:**
**Keywords**
**Controlled Vocabularies**
**Thesauri**
**Taxonomies**
**Ontologies**
**Axiomatized Ontologies**
**(Essentially graphs: node-relation-node + axioms)**

- ISO/IEC 11179 Metadata Registry

- }

## Summary: MDR to Manage Concept Systems

**Concept system:**
**Registration**
**Harmonization **
**Standardization**
**Acceptance (vetting)**
**Mapping **
**   ****(correspondences)**

- ISO/IEC 11179 Metadata Registry

- }

## Summary: MDR for Life Cycle Management

**Life cycle **
**management:**
**Data and**
**Concept systems**
**(ontologies)**

- ISO/IEC 11179 Metadata Registry

- }

## Summary: MDR for Grounding Semantics

**Metadata**
**Registries**

**Semantic Web**
**RDF Triples**
**Subject (node URI)**
**Verb (relation URI)**
**Object (node URI)**

- ISO/IEC 11179 Metadata Registry

**Ontologies**

## Challenges and Future Goals for XMDR R&D & 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

## 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

## 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.