© A. Gómez Pérez, M.C. Suarez de Figueroa, B. Villazón, E.Montiel, G. Aguado, M. Espinoza 1...

© A. Gómez Pérez, M.C. Suarez de Figueroa, B. Villazón, E.Montiel, G. Aguado, M. Espinoza1Ontological Engineering

Bancos de dados, glossários, taxonomias e tesauros para

construir e enriquecer ontologias

Asunción Gómez-Pérez ([email protected])

Credits to: Boris Villazón-Terrazas ([email protected])

Mari Carmen Suárez -Figueroa ([email protected])Guadalupe Aguado ([email protected])

Work distributed under the license Creative Commons Attribution-Noncommercial-Share Alike 3.0


Index

Motivation

Types of non ontological resources

From Knowledge resources to Ontologies

Patterns for Re-engineering Non-Ontological Resources

Example

Conclusion


Motivation

Ontological

Resource Re-

engineering

Non-Ontological Resource Re-engineering

Ontological Resource Reuse

Non-Ontological

Resource Reuse

Classical

Merging Ontological Resources

Reusing Ontology Design Patterns

Restructuring Ontological Resources

Localizing Ontological Resources

…..


Motivation

In our team, we want to build an OWL ontology in the pharmaceutical domain, but we want to use several pharmaceutical standards in XML and classification schemes in our own format.

Non Ontological Resource Reengineering

Non Ontological Resource Reuse

Classical


Motivation

In our team, we want to build an ontology about the human resources management domain. The ontology should include information about occupations and activity sectors, data must be kept in the original DBs, and we want to have the ontology in several natural languages.

Classical

Re-engineering Non-ontological resources

Ontological Resource Reuse

Localizing Ontological Resources

Ontology

Mappings

Ontology-DB mapping


Building ontologies in the 1990s and 2000s

Methodologies for building single ontologies do not consider the reuse of knowledge

• Uschold and King’s method • Grüninger and Fox’s methodology • KACTUS approach• METHONTOLOGY • SENSUS method• On-To-Knowledge • DILIGENT

Ontology learning approaches for building ontologies from structured, semi-structured and non-structured data

• Are not integrated with current methodologies• Mainly from non-structured data using NLP techniques


Current situation in 2010

• Reuse of knowledge-aware resources

• Ontologies are built collaboratively

• Ontologies are connected in ontology networks

OntologyDevelopmentProcess


8

Knowledge Resources

Non Ontological ResourceReuse

Non Ontological ResourceReengineering

2

2

2

Non Ontological Resources

Thesauri

DictionariesGlossaries Lexicons

TaxonomiesClassification

Schemas

O. Localization

9

Ontology Support Activities: Knowledge Acquisition (Elicitation); Documentation; Configuration Management; Evaluation (V&V); Assessment

1,2,3,4,5,6,7,8, 9

Ontological ResourceReengineering

4

4

4

O. Aligning

O. Merging

Alignments5

5

5

6

6

6

6

3

Ontological ResourceReuse

3Ontological Resources

O. Repositories and Registries

FlogicRDF(S)

OWL

Ontology DesignPattern Reuse

7

O. Design Patterns

Ontology Restructuring(Pruning, Extension,

Specialization, Modularization)

8

O. Specification O. Conceptualization O. ImplementationO. Formalization

1RDF(S)

OWL

FlogicScheduling

NeOn Methodology


NeOn Methodologyhttp://www.neon-project.org/nw/NeOn_Book

Process and activities covered:

Ontology Specification

Scheduling

Non-Ontological Resource Reuse

Non-Ontological Resource Re-engineering

Reuse General Ontologies

Reuse Domain Ontologies

Reuse Ontology Statements

Reuse Ontology Design Patterns

All processes and activities are described with:

A filling card

A workflow

Examples


Index

Motivation




Example

Conclusion


Lexicon

• A lexicon is a list of words in a language (a vocabulary) along with some knowledge of how to use each word.

– General or domain-specific;

– Monolingual (Wordnet) or multilingual (Eurowordnet)


Lexicon data models

• Record-based data model

• Relation-based data model

Patterns for Re-engineering Lexica


Thesauri• Controlled vocabularies of terms in a particular domain• Relations: hierarchical, associative and equivalence relations

between terms. • Thesauri are mainly used for indexing and retrieving of articles

in large databases.


Thesaurus data models

• Record-based data model

• Relation-based data model


Classification schemes

• A classification scheme1 is the descriptive information for an arrangement or division of objects into groups based on characteristics, which the objects have in common. E.g. water area classification scheme2.

1. International Standard Organization (ISO). Information technology - Metadata registries – Part 1: Framework, 2004. Report ISO/IEC FDIS 11179-1.2. http://www.fao.org/figis/servlet/RefServlet


Classification Scheme Data Models (I)• Path Enumeration Data Model is defined as

a model that stores for each node the path (as a string) from the root to the node.

Water area20000

Environmental area20000.21000

Jurisdiction area20000.24020

FishingStatistical area20000.22000

Inland/marine20000.21000.21001

12

3 1

2

3

• Adjacency List is a recursive structure for hierarchy representations that comprises a list of nodes with a linking column to their parent nodes.

Water area20000

Environmental area21000

Jurisdiction area24020

FishingStatistical area22000

Inland/marine21001

Ocean21002

North/South/Equatorial21003

FAO statistical area22001

Areal grid system22002

FAO statistical area20000.22000.22001

Areal grid system20000.22000.22002

Ocean20000.21000.21002

North/South/Equatorial20000.21000.21003


Classification Scheme Data Models (II)• Snowflake Data Model is a normalized

structure for hierarchy representations. For each hierarchy level a entity is created. In this model each hierarchy node has a column linked to its parent node.

Water area20000

Environmental area21000

Jurisdiction area24020

FishingStatistical area22000

Inland/marine21001

Ocean21002

North/South/Equatorial21003

FAO statistical area22001

Areal grid system22002

• Flattened Data Model, is a denormalized structure. The hierarchy is represented with an entity where each hierarchy level is stored on a different column.

Water area20000

Environmental area20000.21000

Jurisdiction area20000.24020

FishingStatistical area20000.22000

FAO statistical area20000.22000.22001

Areal grid system20000.22000.22002

Ocean20000.21000.21002

North/South/Equatorial20000.21000.21003

Inland/marine20000.21000.21001

First

Second

Third

First

Second

Third


Index

Motivation




Example

Conclusion


Catalog/ID

Thessauri “narrower term” relation

Formal is-a

Frames (properties)

General Logical constraints

Terms/ glossary

Informal is-a

Formal instance

Value Restrs.

Disjointness, Inverse, part-Of ...

Types of Knowledge-aware resources

Lassila O, McGuiness D. The Role of Frame-Based Representation on the Semantic Web. Technical Report. Knowledge Systems Laboratory. Stanford University. KSL-01-02. 2001.

Lightweight Ontologies

Heavyweight Ontologies


Catalog/ID Thesaurus Glossary Informal is-a Informal is-a

Catalog/ID

Implicit knowledge coded in numbers

XX-YY-ZZ02-01-0202: transportation01: road02: 3-lines highway

Thesaurus


Formal is-a Frames (properties) General Logical constraints

Formal instance Value Restrs.

Disjointness, Inverse, part-Of ...

Formal is-a with properties

(define-relation connects (?edge ?source ?target) "This relation links a source and a target by an edge. The source and destination are considered as spatial points. The relation has the following properties: symmetry and irreflexivity.":def (and (SpatialPoint ?source) (SpatialPoint ?target) (Edge ?edge)):axiom-def((=> (connects ?edge ?source ?target) (connects ?edge ?target ?source)) ;symmetry (=> (connects ?edge ?source ?target) (not (or (part-of ?source ?target) ;irreflexivity (part-of ?target ?source))))))

General Logical constraints

(define-class AmericanAirlinesFlight (?X):def (Flight ?X):axiom-def (Disjoint-Decomposition AmericanAirlinesFlight (Setof AA7462 AA2010 AA0488)))

(define-class Location (?X):axiom-def (Partition Location (Setof EuropeanLocation NorthAmericanLocation SouthAmericanLocation AsianLocation AfricanLocation AustralianLocation AntarcticLocation)))

Disjointness

(define-class Travel (?travel) "A journey from place to place":axiom-def (and (Superclass-Of Travel Flight) (Template-Facet-Value Cardinality arrivalDate Travel 1) (Template-Facet-Value Cardinality departureDate Travel 1) (Template-Facet-Value Maximum-Cardinality singleFare Travel 1)):def (and (arrivalDate ?travel Date) (departureDate ?travel Date) (singleFare ?travel Number) (companyName ?travel String)))

Value Restrs.


The problem

• Making explicit the semantic of the relations between concepts

• Aproaches in the transformation


The problem: Discovering the relation

ID Name10 Vehicle10.01 Car10.02 Motorcycle10.03 Bicycle10.01 Vehicle10.01.01 Wheel10.01.02 Seat10.01.03 Door


Semantics of the Relations among the entities• TBox transformation: patterns must disambiguate the semantics of the relations among the

NOR entities.

1.

2.

3.

1.

2.

4. a.

4. b.

subclassisPartOf


Approaches to transform resources into ontologies

ABox

TBox

Population


Index

Motivation




Example

Conclusion


Motivation

I want to transform my adjacency list-based classification into an ontology


Types of non-ontological resources

Non-Ontological Resources are knowledge-aware resources whose semantics have not been formalized yet by means of an

ontology


Types of non-ontological resources


Reuse and Re-engineering Non-ontological Resources


Re-engineering Model for NORs

Ontology Forward

Engineering

Implementation

Formalization

Conceptua-

lization

Speci-

fication

Implementation

Design

Con-

ceptual

Transformation

Patterns for Re-engineeringNon-Ontological Resources

(PR-NOR)

Non-Ontological Resource Ontology

Requirements

NOR Reverse

Engineering

RDF(S)


Template for the PR-NOR

INPUT

OUTPUT

PROCESS

© A Method for Reusing and Re-engineering Non-Ontological Resources for Building Ontologies Boris Villazón-Terrazas

Re-engineering NORs


Patterns for Re-engineering Classification Schemes into Ontologies

– ABox transformation

– TBox transformation


Pattern for re-engineering a classification scheme, which follows the adjacency list data model, into an ontology schema

Patterns for Re-engineering Classification Schemes

INPUT: Non-Ontological Resource

General

Example

OUTPUT: Ontology GeneratedGeneral

Example

ExamplePROCESS: How to Re-

engineer


PR-NOR library at the ODP PortalTechnological support

http://ontologydesignpatterns.org/wiki/Submissions:ReengineeringODPs


Index

Motivation




Example

Conclusion


oES

EURESES (Int)

gES

LombardES (It)

qES

rES

pES

aES

cES

iES

nES

eES

hES

lES

fES

dES

mES

WalloniaES (Be)

bES

PrivateES (Int)

CataloniaES (Es)

ES

LEGENDA

CandCand..CandCand..

VacanVacan..VacanVacan..

VacanVacan..VacanVacan.. CandCand..CandCand..


CandCand..CandCand..VacanVacan..VacanVacan..




Employment Service

Job Seeker’s Candidacy

Employer Job Vacancy

Looking for an European Employment


gES

LombardES (It)

qES

rES

pES

aES

cES

iES

nES

eES

hES

lES

oES

fESd

ES

mES

bES

Requester ES

Responding ES

ES not involved

Job Seeker’s Candidacy

Employer Job Vacancy

LEGENDA

The Goal: Helping Job Seekers on their way

EuropeanEuropeanEmploymentEmploymentMediatorsMediatorsMarketplaceMarketplace

LocalMatching algorithm

EURESES (Int)

LocalMatching algorithmPrivate

ES (Int)LocalMatching algorithm

WalloniaES (Be)

LocalMatching algorithm

CataloniaES (Es)











VacanVacan..VacanVacan.. VacanVacan..VacanVacan..




Ms Ms

Ms

Ms

Ms

MsMs

Ms

Ms

Ms

Centralized network of ontologies

1. Build a reference ontology

Federated network of ontologies

1. Build a reference ontology for the domain

2. Build local ontologies

3. Build mappings between the core and local ontologies

4. Build mappings between the local ontologies and the data sources

MsMs

Ms

MsMs

2. Build mappings between the reference ontology and the data sources


41

Knowledge Resources



2

2

2


Thesauri



Schemas

O. Localization

9


1,2,3,4,5,6,7,8, 9


4

4

4

O. Aligning

O. Merging

Alignments5

5

5

6

6

6

6

3




FlogicRDF(S)

OWL


7

O. Design Patterns



8


1RDF(S)

OWL

FlogicScheduling

NeOn Methodology


Ontology Specification. The Ontology Requirement Specification

Document


43

Knowledge Resources



2

2

2


Thesauri



Schemas

O. Localization

9


1,2,3,4,5,6,7,8, 9


4

4

4

O. Aligning

O. Merging

Alignments5

5

5

6

6

6

6

3




FlogicRDF(S)

OWL


7

O. Design Patterns



8


1RDF(S)

OWL

FlogicScheduling

NeOn Methodology


Searching Resources

• Use the terminology from the ORSD

• Find resources covering the terminologyKnowledge Resources

Ontological Resources

O. Design Patterns

2


Thesauri



Schemas


FlogicRDF(S)

OWL

• Where: - Internet - Standardization bodies (ISO,…) - Intranet of the organization - Ontology Registries


Search and Selectnon-ontological resources

• We select the most appropriate standards and taxonomies for:

– Occupation ClassificationISCO-88 (COM), SOC, ISCO-88, ONET, Eures Taxonomy.

– Classification of Economic Activities

ISIC Rev. 3.1, NACE Rev. 1.1, NAICS

– Apprenticeship classificationsISCED 97, FOET

– Currency ClassificationISO 4217

– Geography ClassificationISO 3166, Eures Taxonomy

Language Classification ISO 6392, CEF

Driving License Classification European Legislation

Skill Classification Eures Taxonomy

Contract Types Classification LE FOREM, Eures and BLL Classification

Work Condition Classification LE FOREM, Eures and BLL Classification

Is the terminology included in the Ontology Requirements Specification Document

covered by the resources?


ISO 4217 (currencies) ISO 3166 (countries)


Multilingual Non-ontological resources - ISCO-88 (COM)


Searching Ontologies in WatsonOntology Requirement Specification Document

The NeOn methodology includes guideliness for reusing statements


49

Knowledge Resources



2

2

2


Thesauri



Schemas

O. Localization

9


1,2,3,4,5,6,7,8, 9


4

4

4

O. Aligning

O. Merging

Alignments5

5

5

6

6

6

6

3




FlogicRDF(S)

OWL


7

O. Design Patterns



8


1RDF(S)

OWL

FlogicScheduling

NeOn Methodology


Slide 50

Reuse and Re-engineering + Incremental


51

Knowledge Resources



2

2

2


Thesauri



Schemas

O. Localization

9


1,2,3,4,5,6,7,8, 9


4

4

4

O. Aligning

O. Merging

Alignments5

5

5

6

6

6

6

3




FlogicRDF(S)

OWL


7

O. Design Patterns



8


1RDF(S)

OWL

FlogicScheduling

NeOn Methodology


Pattern based approach for re-engineering non ontological resources

ISCO-88 (COM)International Standard Classification

of Occupations (for European Union purposes)

FOET Classification of fields of

education and training

NACEStatistical Classification of Economic Activities in the

European Community

ISTATItalian Geography

Standard

Pattern for re-engineering a classification scheme modelled

with a Path Enumeration Data Model

Pattern for re-engineering a classification scheme modelled

with an Adjacency List Data Model

ItalianItalianGeographyGeographyOntologyOntology

EconomicEconomicActivity Activity OntologyOntology

EducationEducationOntologyOntology

OccupationOccupationOntologyOntology

ISO 3166English country names

and code elements

Pattern for re-engineering a classification scheme modelled with a Snowflake Data Model

GeographyGeographyOntologyOntology


ISO 3166-1 (XML) Regions Table (Eures Oracle DB)

….

<ISO_3166-1_Entry> <ISO_3166-1_Country_name>SPAIN</ISO_3166-1_Country_name> <ISO_3166-1_Alpha-2_Code_element>ES</ISO_3166-1_Alpha-2_Code_element> </ISO_3166-1_Entry>…

Location

Country Region

subClass-Of

has region

Spain Cataluña

Canarias

Galicia

Andalucía

Ontology model

Ontology instances

Excerpt of the Geography Ontology

Knowledge Resource Re-engineering and Aggregation


54

Knowledge Resources



2

2

2


Thesauri



Schemas

O. Localization

9


1,2,3,4,5,6,7,8, 9


4

4

4

O. Aligning

O. Merging

Alignments5

5

5

6

6

6

6

3




FlogicRDF(S)

OWL


7

O. Design Patterns



8


1RDF(S)

OWL

FlogicScheduling

NeOn Methodology


Conceptualization: Modular approach for ontology construction

Representation Ontology: WSML

General/Common Ontologies: Time, Geography, Language

Domain O.: Economic Activity, Occupation, Education, Skill, Driving License, Compensation, Labour Regulatory, Competence

ApplicationDomain O. : Job Seeker, Job Offer

-

+

Reusability

-

+

Usability


Reference Ontology

LabourRegulatoryOntology

SkillOntology

Language Ontology

OccupationOntology

Geography Ontology

Time Ontology

EducationOntology

Driving License Ontology

Compensation

Ontology

EconomicActivity

Ontology

Job OfferOntology

Job SeekerOntology

has work condition /

is associated with

has contract type / is associated withis located in /

has salary / is associated with

requires education /

is associated with

is associated with

has activity sector /

is associated with

has nationality from / is nation of

resides in / is residence of

has salary /

has contract type / is associated to

has work condition / is associated to

has location / is associated with

has

activ

ity s

ecto

r /

is a

ssoc

iate

d w

ith

has

activ

ity s

ecto

r /

is a

ssoc

iate

d w

ith

has

job

cate

gory

/

is a

ssoc

iate

d w

ithha

s jo

b ca

tego

ry /

Is a

ssoc

iate

d w

ith

has education /

is education of

has mother tongue / is m

other tongue of

speaks / is spoken by

has language proficiency /

belongs to

LE FOREM + BLL + EURES

EURES

ISO 6392

CEFISCO-88 COM

ONET

EURES

ISO 3166

EURES

DAML Time Ontology

FOET

ISCED97

NACE Rev. 1.1

European Legislation

ISO 4217

Ad hoc wrapper

External Sources

is associated with

has job category /

is associated to has date of birth/ is date of birth of

has begin date /

is begin date of

Competence

Ontology

subClass-Of

subClass-Of

requires competence /

is associated with

has competence /

is competence of


Candidacy

Offered WorkExperience

Objective

ICT Objective

Job Seeker

Vacancy

Organization

Requested Work Experience

ICTVacancy

Job Vacancy

Competence

Education

Language

Contract Type

Compensation

Work Condition

Occupation

Sector

Location

Country

Computing Professionals

has candidacy/belongs to

has objective /belongs to

subClass-Of

has job category

is associated with

subClass-Of

has nationality from /is nation of

resides in /is residence of

has mother language /is mother tongue of

speaks /is spoken by

has competence /is competence of

has education /is education of

has work experience /belongs to

has

work

con

dit

ion

/is

ass

oci

ate

d t

o

has

con

tract

typ

e /

is a

ssoci

ate

d t

o

has

com

pen

sati

on

/is

ass

oci

ate

d t

o

is a

ssoci

ate

d w

ith

/h

as

loca

tion

has activity sector /is associated with


has job category /

is associated with

has job category /

is associated with

has job vacancy/belongs to

has location /is location of

has vacancy/belongs to

subClass-Of

has job category /

is associated with

is associated with /requires work experience


has job category/is associated with

requires education /is associated with

requires competence /is associated with

has work condition /is associated with

has contract type /is associated withhas compensation /

is associated with

is located in /is associated with

has job category/is associated with


Job OfferJob OfferOntologyOntology

Job SeekerJob SeekerOntologyOntology

OccupationOccupationOntologyOntology

LanguageLanguageOntologyOntology

EducationEducationOntologyOntology

CompetenceCompetenceOntologyOntology

LabourLabourRegulatoryRegulatoryOntologyOntology

CompensationCompensationOntologyOntology

GeographyGeographyOntologyOntology

Economic Economic ActivityActivity

OntologyOntology

Details of the ontology


Conclusions

1. The NeOn methodology facilitates the reuse and reengineering of non ontological resources into ontologies

2. The reuse of non-ontological resources that have been reached some degree of consensus in a community allows the development of ontologies easier and quicker

3. The use of external resources for disambiguating the semantics of the relations in the resource, the resultant ontology will have better quality degree.


NeOn Methodology Pointers Scenarios for Building Ontology Networks D5.3.1 and D5.4.2

NeOn Glossary of Processes and Activities D5.3.1 and D5.3.2

Set of Ontology Network Life Cycle Models D5.3.2

Methodological Guidelines for Ontology Requirements Specification D5.4.1

Methodological Guidelines for Scheduling and gOntt plug-in D5.3.2

Methodological Guidelines for Non-Ontological Resource Reuse and Reengineering D5.4.1 and D2.2.2

Methodological Guidelines for Ontological Resource Reuse D5.4.1

Methodological Guidelines for ODP Reuse D5.4.1 and D5.4.2

Methodological Guidelines for Ontology Modularization D5.4.2

Methodological Guidelines for Ontology Evaluation D5.4.2

Methodological Guidelines for Ontology Evolution D5.4.2

Methodological Guidelines for Ontology Localization D5.4.2

© A. Gómez Pérez, M.C. Suarez de Figueroa, B. Villazón, E.Montiel, G. Aguado, M. Espinoza 1...

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