1PROVE’09

The use of competence ontology

for network building

Kafil Hajlaoui, Xavier Boucher, Michel Beigbeder, Jean Jacques Girardot Ecole Nationale Supérieure des Mines de Saint Etienne - France

{hajlaoui, boucher, mbeigd, girardot}@emse.fr

2PROVE’09

Content

••

ContextContext

and objectives : and objectives : identification of collaborative networksidentification of collaborative networks••

Information extraction on Information extraction on activityactivity

fieldsfields

: : briefbrief

synthesissynthesis

of the of the resultsresults••

Information extraction on Information extraction on enterpriseenterprise

competencescompetences

: : principlesprinciples••

Key Key stepssteps

of the of the methodologymethodology

usedused

to to buildbuild

a a ««

CompetenceCompetence

TracesTraces

»» OntologyOntology

••

Discussion on the use of the Discussion on the use of the ontologyontology

to to extractextract

««

enterpriseenterprise competencecompetence

tracestraces

»»

: : association association withwith

semanticsemantic

patterns and patterns and

performances of the systemperformances of the system••

ConclusionConclusion

3PROVE’09

Introduction

PotentialCollaborativeNetworks

E1

En

Enterprises

described by some

characteristic pieces

of information

Context : to support the creation of collaborative networks of firms

2 key

pieces

of information :Complementarities

of activity

fieldsSimilarity

of competencies(Results

of M. Benali PhD

thesis, based

on the work

of Richardson –

1959)

4PROVE’09

IntroductionHypotheses

:Open universe

provided

by the webPublic information : web sites

E1

En

Virtual Breeding Environment

Virtual organization

General information on:Activity

fieldsCompetencies

+Clustering

method

Market

opportunity+

Data base informationShared

by the partners

Semi-automatic

extraction of informationfrom

the web sites

5PROVE’09

Information Extraction MechanismsCo-operation between firms = Complementary activities

& Similar competencies

IEM-1 InformationExtraction on activityfields

IEM-2 InformationExtraction on competencies

DE

SS Identification of

Collaborativecorporate networks

Web site

• To develop an automated information extraction mechanism, used before applying a clustering algorithm for network identification• Scientific issues : to deal with non-structured information available through websites. How can we use additional semantic information from the “business domain” ?• Application domain for the research : mechanical industry.

6PROVE’09

« Request » information vectors(Each firm)

Matching(NAF identification)

« Document » information vectors(each class of the NAF Code)

IEM-

1 : Activity Field Identificationbased on indexing procedure

Web sites

Controlledindexing

StructuredSemantic resource

(NAF code)

Vectorial

Model

Connexionnist

Model

Conclusions• The right NAF class is

found

at

~ 90 %• Precision

~60%, recall

~95%•

So good performance of the indexing procedure, however

not any

clear domination of the connexionnist

model

Precision Recall

7PROVE’09

IEM-

1 : Activity Field Identificationbased on indexing procedure

Input : complementary degree among NAF Class codes

Complementarity activity Graph

ClusteringProcedure

8PROVE’09

IEM-1 InformationExtraction on activityfields

IEM-2 InformationExtraction on competencies

DE

SS Identification of

Collaborativecorporate networks

Web site

IEM-

2 : Competence Identificationbased on ontology and pattern maching

Informational

context

:

•

Complexity

of the notion of «

Enterprise competence

»

: linked

to technologies, to human

resources, to methods

and know-how at

use in each

company

•

Necessity

of linguistic

approach

: lots of distincts terms

and expressions can bring

pieces

of information ; semantic

ambiguity

(context) ; synonymy

etc…

• No structured

semantic

resource

available

9PROVE’09

IEM-

2 : Competence Identificationbased on ontology and pattern maching

Ontology

Semantic

patterns

Conceptual structure of « competencetraces »

Comparison of distinct companies

Similarity

of «

competence

traces

»

Deal with ambiguïty and other semanticissues during the extraction procedure

10PROVE’09

Enterprise competence

?

•The overall

competence

of a firm•Emerges as a combination

of capabilities, notably

the technological

and methodological

capabilities…•

These

capabilities

results

from

the utilisation of internal

resources

: human, technical, informational, organisational

resources

Ontolology

?

• Of competence

traces•

Built

with

a methodology

which

provide

a structured

approach

to control the semantic

issues : ARCHONTE was

selected

operationalisation

Termsof the domain

differential

ontology reference

ontology computable ontology

ARCHONTE METHODOLOGY

Ontology Building with Archonte

11PROVE’09

Ontology FormalisationOntology concepts have been structured on 3 distinct conceptual levels (genericity)

Meta-physicalConcepts

StructuringConcepts

“Parataxic”Concepts

BasicCompetenceModel

GenericOntology

DomainOntology

CompetenceTraceOntology

12PROVE’09

Generic Ontology :

Top –

Down approachCompetencies

Capability

Resources

Technological Capability

Methodological Capability

Human Resources

Technical Resources

Informational Resources

Organisational Resources

uses1..*

1..*

uses

1..*

1..*

is-a

is-a

is-a

is-a

is-a

is-a

13PROVE’09

Domain Ontology : bottom up approach

14PROVE’09

Operationalisation

: OWL with “Protégé”

tool

15PROVE’09

Using the ontology for extraction

Pre-treatmentof web sites

data

Patternidentification

Ontology

classesActivation

OntologyPatternDB

CompanyCompetenceTrace

Web sites

16PROVE’09

Pattern representation : example

A specific

pattern in a formal

language

Occurrences of the pattern in a web site

Pertinente expression delimited

within

the corpus

17PROVE’09

The result observed

For each company : which classes of the ontology are considered activated ?

18PROVE’09

Performance study

Activation Precision Recall

Expert 0,84 0,75

System 0,87 0,64

Comparaison of the performance of an expert and of the automatic extraction/activation systemin a task consisting in « activating » the ontology classes for a given web site.

Automatic activation of ontology classes Expert activation of ontology classes

Precision PrecisionRecall Recall

19PROVE’09

Conclusion

With

the objective to assess

a level

of competence

similarity

among

companies:

-

We

have proposed

an approach

which

takes

advantage

of semantic

information from

the domain

(concerning

competence

descriptions) …

-… but which

remains

very

adaptable from

one business domain

to another.

-

The association of patterns and ontology, provides

to the information extraction system a good ability

to identify

correctly

the classes of a «

competence

traces

ontology

», corresponding

to a company’s

web site.

-

Further

work

: to measure

the similarity

among

the competences

traces from distinct companies.