Shanghai, Nov. 2004

Michel SIMONETTIMC-IMAG research laboratory

Université Joseph FourierGrenoble - France

Michel SIMONETTIMC-IMAG research laboratory

Université Joseph FourierGrenoble - France

ONTOLOGIES beyond fashionA short introduction to ontologies and the

Semantic Web

ONTOLOGIES beyond fashionA short introduction to ontologies and the

Semantic Web

Shanghai, Nov. 2004

FASHION : the Semantic Web

1998: Tim Berners-Lee “Machine and machine man and machine can communicate”

Communication / Understanding

based on ONTOLOGIES

FASHION : the Semantic Web

1998: Tim Berners-Lee “Machine and machine man and machine can communicate”

Communication / Understanding

based on ONTOLOGIES

ONTOLOGIES beyond fashion A short introduction

ONTOLOGIES beyond fashion A short introduction

Shanghai, Nov. 2004

• An example through Information Retrieval

• History, definition, examples

• Ontologies and data integration

• Ontologies and Information systems– Ontology as the starting point of an Information System

– From ontology to database and software

– Gennere example

• An example through Information Retrieval

• History, definition, examples

• Ontologies and data integration

• Ontologies and Information systems– Ontology as the starting point of an Information System

– From ontology to database and software

– Gennere example

ONTOLOGIES A short introductionONTOLOGIES

A short introduction

Shanghai, Nov. 2004

• An example through Information Retrieval• History, definition, examples

• Ontologies and data integration

• Ontologies and Information systems

• An example through Information Retrieval• History, definition, examples

• Ontologies and data integration

• Ontologies and Information systems

ONTOLOGIES A short introductionONTOLOGIES

A short introduction

Shanghai, Nov. 2004

HeterogeneousMedical

Literature Databasesand the Internet

Medical Professionals

& Users

TOXLINE

CancerLitEMIC

HazardousSubstancesDatabank

MEDLINE

Current Information

Interfaces

The Medical Information Gap*

* Aronson AR, Rindflesch TC. Query Expansion Using the UMLS Metathesaurus. In: AMIA Annual Fall Symposium; 1997; 1997. p. 485-89.

Shanghai, Nov. 2004

Information searchon the Internet

Information searchon the Internet

• Google+ Easy to use – Natural language

? Quality of result

– Time-consuming

• Medline - Pubmed+ High quality of scientific content

? Ease of use – Controlled vocabulary (MeSH thesaurus)

– Time-consuming

• Objective : Concept-based rather than word-based search

Shanghai, Nov. 2004

Word-based searchWord-based search

Breast removal Index

Breast removal

Breast augmentation andLaser hair removal

The removal of breast cancer

Mastectomy

Searching Googl

e

Breast and RemovalBreast or Removal

Noise

NoiseSilence

Perti

nent

Mastectomy is a Breast Removal Synonym

Shanghai, Nov. 2004

Concept-based searchConcept-based search

Breast removal

Breast removalAblation du sein

MastectomyMastectomie

Mammectomy Mammectomie

Radical mastectomyMastectomie radicale

Searching

Concept

Pertinent

MASTECTOMY

RADICALMASTECTOMY

Pertinent

Pertinent

Pertinent

Shanghai, Nov. 2004

What is an ontology?Graphical representationWhat is an ontology?Graphical representation

TREATMENT

CHEMOTHERAPY SURGERY

RADICAL MASTECTOMY

RADIOTHERAPY

MASTECTOMY

ABLATION

TUMORECTOMY

HUMAN_BODY

ORGAN

BREAST

followed_by

Remove

IS-A

Part-Of

Mastectomy

Mammectomy

Breast removal

Ablation du sein

Μαστεκτομή

Shanghai, Nov. 2004

Benefits of Concept-based Information Retrieval

Benefits of Concept-based Information Retrieval

• Search is automatically extended to synonyms– E.g., query : « breast removal »

mastectomy, mammectomy, …

• Independence from query language– E.g., query in French : « mastectomie »

answer = documents in any language (e.g., English, French, Spanish, German, Greek, Chinese …)

• Query expansion using the concept hierarchy

• Result presentation using the Ontology’s organization

• General orientation of the Semantic Web

Shanghai, Nov. 2004

• An example through Information Retrieval

• Definition, examples, history• Ontologies and data integration

• Ontologies and Information systems

• An example through Information Retrieval

• Definition, examples, history• Ontologies and data integration

• Ontologies and Information systems

ONTOLOGIES A short introductionONTOLOGIES

A short introduction

Shanghai, Nov. 2004

What is an ontology?What is an ontology?

• The Origins: Plato and Aristotle

• A need to organize knowledge

• History in Computer Science

• Various definitions

• Consensus definition

• Example

• W3C hierarchy of languages

• Ontology usages

• The Origins: Plato and Aristotle

• A need to organize knowledge

• History in Computer Science

• Various definitions

• Consensus definition

• Example

• W3C hierarchy of languages

• Ontology usages

Shanghai, Nov. 2004

The Origins: Plato and AristotleThe Origins: Plato and Aristotle

Aristotle : the study of beings insofar as they exist

Reality: Individuals Vs Concepts Plato, John Human

What is universal, beyond particular representations?

Categories of being - Physical objects- Minds- Classes- Properties- Relations

Porphyry (3rd century) : Porphyry’s trees Categorization by identity and difference

The basis of contemporary ontologies

Shanghai, Nov. 2004

History: a need to organize knowledgeHistory: a need to organize knowledge

Classifications in biology

Linné (1707-1778)

Thesaurus in Information RetrievalConsensus about names and structure

- Russell, Wittgenstein, Frege, Husserl, Peirce

- Nicola Guarino- Barry Smith

- Gruber (1990) Stanford KIF, Ontolingua- Sowa : Conceptual Graphs- Description Logics (DL)- Semantic Web

Philosophy I.A.

Shanghai, Nov. 2004

History in Computer ScienceHistory in Computer Science

Semantic Networks (Shank - 1968) Concepts and relationships Confusion between concepts and individuals

STUDENT IS-A PERSON John IS-A PERSON

Conceptual Graphs (Sowa – 1980) Formalization of semantic networks

First-Order Logic Gruber (1990 – Stanford)

KIF : Knowledge Interchange Format Ontolingua: a language and a platform for ontology exchange

1st use of the term Ontology in Computer Science

Shanghai, Nov. 2004

Consensus definitionConsensus definition

CONCEPTS

RELATIONSHIPS between concepts IS-A relationships (generic/specific) part-of relationships other relationships

VOCABULARY + preferred term for a concept

DEFINITIONS informal, in natural language formal (eg., Description Logics)

Shanghai, Nov. 2004

Example : Breast Cancer* (1)Example : Breast Cancer* (1)

CONCEPTS : BREAST SURGERY MASTECTOMY

RELATIONSHIPS between concepts LEFT MASTECTOMY IS-A BREAST SURGERY MASTECTOMY ENTIRE LEFT BREAST is-proper-material-part-of

ENTIRE LEFT THORAX

LEFT MASTECTOMY has-theme ENTIRE LEFT BREAST

VOCABULARY + preferred term for a conceptbreast surgery mastectomy* From the INFACE Ontology by Language and Computing (www.landc.be)

Shanghai, Nov. 2004

Example : Breast Cancer* (2)Example : Breast Cancer* (2)

CONCEPTS : e.g., MASTECTOMY

RELATIONSHIPS between concepts MASTECTOMY IS-A ABLATION ORGAN part-of HUMAN BODY TUMORECTOMY followed-by RADIOTHERAPY

VOCABULARY + preferred term mastectomy, mammectomy, breast removal, mastectomie, mammectomie, ablation du sein, μαστεκτομή …

DEFINITIONS Surgical removal of the breast

* From a Patient-oriented ontology by Radja Messai – TIMC (UJF)

Shanghai, Nov. 2004

What is an ontology?Graphical representationWhat is an ontology?

Graphical representationTREATMENT

CHEMOTHERAPY SURGERY

RADICAL MASTECTOMY

RADIOTHERAPY

MASTECTOMY

ABLATION

TUMORECTOMY

HUMAN_BODY

ORGAN

BREAST

followed_by

Remove

IS-A

Part-Of

Shanghai, Nov. 2004

W3C hierarchy of languages*W3C hierarchy of languages*

* In the framework of the Semantic Web

• XML (eXtensible Markup XML (eXtensible Markup Language)Language)

• XML Schema (XSD)XML Schema (XSD)• RDF (Resource Description RDF (Resource Description

Framework)Framework)• RDF SchemaRDF Schema• OWL (Web Ontology Language)OWL (Web Ontology Language)

Shanghai, Nov. 2004

• An example through Information Retrieval

• History, definition, examples

• Ontologies and data integration• Ontologies and Information systems

• An example through Information Retrieval

• History, definition, examples

• Ontologies and data integration• Ontologies and Information systems

ONTOLOGIES A short introductionONTOLOGIES

A short introduction

Shanghai, Nov. 2004

ONTOLOGIES Data Integration

ONTOLOGIES Data Integration

Shanghai, Nov. 2004

ProblemProblem

• Various types of data– Structured: databases– Informal: Texts– Semi-structured: XML

• Heterogeneity– Query languages– Structure, vocabulary, …

• Various types of data– Structured: databases– Informal: Texts– Semi-structured: XML

• Heterogeneity– Query languages– Structure, vocabulary, …

Database

XMLTexts

?

Shanghai, Nov. 2004

Query through an OntologyQuery through an Ontology

…..…..

….….

ConceptAnatomo-fonctionnel

ConceptAnatomique Conceptfonctionnel

Hidbrain Midbrain

Correspondence

User

Databases XMLTexts

Adaptor1 Adaptor2 Adaptor3

Shanghai, Nov. 2004

• An example through Information Retrieval

• History, definition, examples

• Ontologies and data integration

• Ontologies and Information systems

• An example through Information Retrieval

• History, definition, examples

• Ontologies and data integration

• Ontologies and Information systems

ONTOLOGIES A short introductionONTOLOGIES

A short introduction

Shanghai, Nov. 2004

Ontologies and Information Systems

in the Health Field

• Common understanding? / Consensus ?• Communication / Sharing

ONTOLOGIES

WHAT we speak about?CONCEPTSDEFINITIONS

HOW do we speak of it? VOCABULARY

Information SystemsOntologies

Shanghai, Nov. 2004

Information SytemsInformation Sytems

« The Information System is a support for communication inside the enterprise and between the enterprise and its environment »

G. Panet & R. Letouche : Modèles techniques Merise avancés.

• The Real Organization– se transforme, agit– communicates – memorise

• The system which is built to REPRESENT– Actions

– Communication

– Memorisation

« The Information System is a support for communication inside the enterprise and between the enterprise and its environment »

G. Panet & R. Letouche : Modèles techniques Merise avancés.

• The Real Organization– se transforme, agit– communicates – memorise

• The system which is built to REPRESENT– Actions

– Communication

– Memorisation

HUMAN

Software

DatabasesData warehouses

Ontologies

Shanghai, Nov. 2004

Cognitive test

Agilité Verbale Dénomination image

Assemblage Objet

Cognitive Function

Vision Memory Language

Mémoire Rétrograde Mémoire Antérograde

Mémoire RétrogradeSémantique

above

Temporal Lobe

Cerebral Cortex

Occipital Lobe

Parietal Lobe

Frontal Lobe

ISA relationship :

CONCEPTS :

Relation transversale :

part-of relationship :

Vocabulary : {Memory, memory fonction, mémoire}

Définitions

validates

Responsible for

validates

Shanghai, Nov. 2004

What is an ontology?What is an ontology?

• CONCEPTS

• RELATIONSHIPS between concepts• ISA relationships (generic/specific)• part-of relationships• other relationships

• VOCABULARY + preferred term for a concept

• DEFINITIONS• informal, in natural language• formal

CONSENSUS

Shanghai, Nov. 2004

Example in mycologyExample in mycology ISA concepts hierarchy

Formal definitions of constraints Object classification / identification Detection of inconsistent definitions

Knowledge BaseDiagnosis aid

Champignon à lames

Russule Ammanite

Virescens Cyanoxantha

Couleur:{blanc,crème}Chair:{grenue, cassante}…

Couleur: blancChair: grenueCouleurChair: blanc

CouleurChair: rosé…

: colour: white chair: grenue couleurChair: white

Shanghai, Nov. 2004

Formal Ontologies and Knowledge Bases

Formal Ontologies and Knowledge Bases

PERSON

ADULT

SENIOR

CONCEPT CLASSIFICATION INSTANCE CLASSIFICATION

PERSON

ADULT

SENIOR

MINOR

age≥18

age≥65

Age<18

PERSON

ADULT SENIOR

age≥18 age≥65

: age = 70

Shanghai, Nov. 2004

Formal Ontologies and Knowledge Bases

Formal Ontologies and Knowledge Bases

Formal Ontology

representation in a logical formalism

E.g. : Description Logic (DL)

Inférence- Subsomption Concept Classification Consistency - Instance Classification

DefConcept PERSON name:STRING and age:INT

DefConcept ADULT = PERSON and age≥18

DefConcept SENIOR = PERSON and age≥ 65

DefConcept SENIOR1 =SENIOR and age< 60

CONSISTENCY: SENIOR1 EMPTY_CONCEPT

SUBSOMPTION:

SENIOR ADULT SENIOR ADULT

SENIOR subsumed by ADULT

Shanghai, Nov. 2004

Database DesignDatabase Design

NAME

PERSON

PATIENT DOCTOR

SEX NHS

1st Name

CC

ACT

HOSPITAL

DISEASE

• Identify the concepts of the domain

• Determine relationships and their cardinalities

Micro-ontology of the domain

First Step

prescritpaie

coding

Ontological schema

consulte

1,*

1,*

1,1

SPEC

Shanghai, Nov. 2004

Database DesignDatabase Design Ontological Schema

Physical level: files …

Name things and their relationships

E-R, UML Schema : classes, methods

Optimize

Relational Schema : tables

Evolutions

Choice - model constraints (associations, …)- cultural choices

- object / value

Choic - model constraints - atomic attributes

- normalization

Choice - index- buffers

- DBMS specific features

Shanghai, Nov. 2004

Database DesignDatabase Design

Evolution level Ontological levelCorrective & evolutive maintenance (80% cost)

Loss of initial semantics

Maintain links between levels

Understanding, Mastering

Evolution level Ontological levelCorrective & evolutive maintenance (80% cost)

Loss of initial semantics

Maintain links between levels

Understanding, Mastering

Shanghai, Nov. 2004

• GENNERE database and software for 2 fields– Nephrology (ESRD: End-Stage Renal Disease)

– Rheumatology (RA: Rhumatoid Arthritis)

User testing and validation is ongoing at Rui Jin hospital

• Perspectives – Data Warehouse for epidemiological studies

– Geographical Information systems

– Improve tools and methods for genericity

• GENNERE database and software for 2 fields– Nephrology (ESRD: End-Stage Renal Disease)

– Rheumatology (RA: Rhumatoid Arthritis)

User testing and validation is ongoing at Rui Jin hospital

• Perspectives – Data Warehouse for epidemiological studies

– Geographical Information systems

– Improve tools and methods for genericity

GENNERE Project achievements and perspectives

GENNERE Project achievements and perspectives

Shanghai, Nov. 2004

• Genericity– Common core ontology : PATIENT – FOLLOW-UP – TREATMENT

Common schema concepts and attributes

– Common set of events: New Patient, New Treatment, Patient Transfer, Decease, …

– Automatic generation of database (ISIS CASE tool)

– Database access through views (with limitations due to DBMS)

– Intensive use of metadata• Domain values: Comorbidities, …

• Multilingualism (UTF8): GUI items, domain values

– Standard medical classifications (thesaurus)

• Genericity– Common core ontology : PATIENT – FOLLOW-UP – TREATMENT

Common schema concepts and attributes

– Common set of events: New Patient, New Treatment, Patient Transfer, Decease, …

– Automatic generation of database (ISIS CASE tool)

– Database access through views (with limitations due to DBMS)

– Intensive use of metadata• Domain values: Comorbidities, …

• Multilingualism (UTF8): GUI items, domain values

– Standard medical classifications (thesaurus)

Genericity: Achievements and LimitsGenericity: Achievements and Limits

Shanghai, Nov. 2004

• Limits– Disease-specific data

• e.g., vaccinations for RA

• core concepts (DISEASE, TREATMENT) have to be derived according to each disease

Rheumatology TREATMENT is more complex

– Country-specific data• Culture and health care system are different

• Patient identification, addresses

– No standard multilingual version of ICD10

– Thesaurus translation into Chinese

– No framework for automatic GUI generation

• Limits– Disease-specific data

• e.g., vaccinations for RA

• core concepts (DISEASE, TREATMENT) have to be derived according to each disease

Rheumatology TREATMENT is more complex

– Country-specific data• Culture and health care system are different

• Patient identification, addresses

– No standard multilingual version of ICD10

– Thesaurus translation into Chinese

– No framework for automatic GUI generation

Genericity: Achievements and LimitsGenericity: Achievements and Limits

Shanghai, Nov. 2004

Genericity was partly achieved– Gain around 50% for 2nd disease (RA) although more complex

– Multilingualism: Chinese, English, French

Extend the ISIS CASE tool– To deal explicitly with Generic and Specific concepts

Through and XML (OWL?) description of the domain

– To perform automatic GUI generation

So as to ease a strong interaction with users

Genericity was partly achieved– Gain around 50% for 2nd disease (RA) although more complex

– Multilingualism: Chinese, English, French

Extend the ISIS CASE tool– To deal explicitly with Generic and Specific concepts

Through and XML (OWL?) description of the domain

– To perform automatic GUI generation

So as to ease a strong interaction with users

ConclusionsConclusions

Shanghai, Nov. 2004

Contact 联系方式Contact 联系方式• Michel SIMONET

Michel.Simonet@imag.fr

• Didier GUILLON

Didier.Guillon@agduc.com

• Dr Haijin YU 俞海瑾 Kidney_rj@medmail.com.cn

• Michel SIMONET

Michel.Simonet@imag.fr

• Didier GUILLON

Didier.Guillon@agduc.com

• Dr Haijin YU 俞海瑾 Kidney_rj@medmail.com.cn

Shanghai Grenoble

Shanghai, Nov. 2004

GENNERE partners GENNERE partners GENNERE partners GENNERE partners

Paris - NECKERParis - NECKER

Paul LANDAISMichel & Ana SIMONET

Didier GUILLON

Belgium - RAMITBelgium - RAMIT

Georges de MOOR

Nan CHEN

Wen ZHANG

Grenoble - AGDUCGrenoble - AGDUC

Michel FORET

Philippe GAUDIN

Grenoble – TIMC IMAGGrenoble – TIMC IMAG

Shanghai – RUI JINShanghai – RUI JIN

Shanghai, Nov. 2004

Results (1)Results (1)

Shanghai, Nov. 2004

Results (2)Results (2)