1. luk vervenne
synergetics nv 1

2. Een veelkoppig monster
• Individu als “de nieuwe stakeholder in zijn eigen
processen” (Life Management)
• Van syntax naar semantiek
• Markten, jobs evolueren steeds sneller
• Competenties: duizend bloemen bloeien
• Top-down vs bottom-up
• …
2

3. Labour Markets
beyond the client/server paradigm
Regional Employability Ecosystems
Server Server Server
Industry / Sector specific
Processes & Services
CORP. GOV. Service
Provider
Base Infrastructure for
Personal InfrastructureClient
Client Client Cloud Region & Sector-wide
User-centric / User-driven
Ecosystems
1. Personal Infrastructure
Organising the communality 2. Semantic Coordination
Assure & Promote Labour Market Mobility: 3. BI Infrastructure
1. Governance (PPP) 4. eContent gateway
2. Communality Based Infrastructure & Services 5. Matching Infrastructure
3. Semantics 6. Trust & Security (+TTP)
4. Trust & Security

4. 1. Bouwen op wat we hebben
(NEN norm)
4

5. Luk Vervenne – Dr. Ingo Dahn

6. Application Profiles
• Standards are too general – I don’t need all that fuzz
• Standards are too restricted – they don’t let me do
what I want
• Solution: Application Profiles!

7. Make My Day!
Make My Profile – it‘s easy!
• Making mandatory what I do want
• Making optional what I tolerate
• Remove what I don’t need
• Add what I need
• Next step:
• Convince your industry sector
• Agree
• Share

8. Making My Profile – Oh so tricky!
• My own extensions
• Mixing and matching many profiles
• All referenced files must :
• exist
• validate against another profile or one of a few…
• And…
• you have to find out which one to use…
• the imsmanifest.xml must exist !!

9. Prerequisites of Profiles
• A community of stakeholders
• Acquaintance with:
• their needs…
• their willingness to agree…
• their willingness to implement!!!
• STEPS: Success of application profile depends on
implementers, data providers and data consumers

10. Conformance Testing
• Test so that data conforms to YOUR profile
• Problem:
• Each profile requires a specific test system
• Test system development is expensive
• Solution:
• Capture profile in machine readable form
• Configure generic test system

11. Profiling: SchemaProf

12. Creating a Test Service

13. Running the Tests

14. Survey Report

15. Detailed Reports

16. 2. En wat met niet-gestructureerde data?
(competentiebeschrijvingen
beroepsbeschrijvingen
ervaringen
vacatures
…)
16

17. Purpose:
Semantic Comparison of Labour Market Data
Compare real-world employability & employment data versus
Reference Data of Competences, Occupations, Qualifications, …
Allows the meaningful search, assessment or match of
experience, professional activities, skills & competences by using :
• Domain Semantics (Annotated Reference framework data)
• Linguistic Semantics (Unstructured data using NL processing)
• Created individually (personal employability data)
• Created at company level (vacancies, job profiles, …)
17

18. Approach
• Knowledge management
• Knowledge encoding (knowledge bases)
• Knowledge-based data processing
• Annotation
• Comparison
• Interoperation
• Inference
• (Natural Language) Data processing
• Interpret data dynamically
• Capture data individuality and specifics
18

19. Data, Knowledge & Semantics
1. Data : experience, goal, competence, preference, hobby, training, job, task
2. Knowledge : frameworks, expert rules, models, ontology
3. Semantics : compare Data + Knowledge for semantic operations:
• Data management
• Knowledge management
• Knowledge-based data management
• Data-oriented knowledge management
Two kinds of Semantics are involved:
Precompiled: Static, knowledge-based operation
= knowledge semantics
Extracted : Dynamic, in real-time in data management
= data semantics
19

20. PROCES PERSONAL
CENTRIC EMPLOYABILITY
PERSONAL (SEMANTIC META-)DATA
DATA (Content + VOCs)
GOV
EDU
PDS
COMPANY
SERVICE
PROVIDER
e- HR-
PORTFOLIOS PROCESSES
LEER-
DOSSIER
20

21. Personal Data Infrastructure
New M a r k e t s
Linked- NL HR-XML Social Personal
In GermanCV, Finance
Portfolio iProfile UK
Network
Mobile Data
Data
context
eGov data Personal
EuroCV EuroPass Consumer
Citizen data Data
Health
Data
Import
1
TRANS-
ePortfolio FORMATION CRUD WS
Export engine
10%
SOA GATEWAY
2
Create, Read, Web
CRUD WS
Update, Delete interface
20% PDS
Integration with
3
WS
(legacy) systems
70%

22. 22

23. PROCES PERSONAL SHARED (PARTIAL)
CENTRIC EMPLOYABILITY SEMANTIC REFERENCE
PERSONAL (SEMANTIC META-)DATA KNOWLEDGE FRAMEWORKS
DATA (Content + VOCs) BASE
CINOP/
GOV
ECVET
UWV
EDU
SEMANTIC SHL
KNOWLEDGE
PDS BASE SBB
COMPANY
VDAB
SERVICE
ESCO
PROVIDER UNIFIED SEMANTIC MATCHING
e- HR- SKILLS &
EVIDENCE COMPETENCES
PORTFOLIOS PROCESSES
LEER- VACANCIES
DOSSIER
23

24. The European need for
semantic interoperability
24

25. Beyond Asset Descriptions
FEDERATION
25

26. : Data & Semantics
The Employability data from :
(1) Candidates (employabilityPortfolio™, CVs,
VACANCY CANDIDATE EuroPASS, HR, educational, Recruiting or Public or
Private Employment Systems
(2) Vacancies are indexed & correlated against
vocational/occupational qualification and
competency catalogues.
ROME : Adult COLO : Graduate
OCCUPATIONAL VOCATIONAL This demo is based on the French ROME 2.0
Classification Qualifications catalogue. However such National catalogues
(NOS/SSC) can be enriched with information from
other European catalogues.
‘ESCO’ skills & competencies
& occupations All vocational categories and qualifications and their
respective competency / skills profiles are correlated.
A competency profile is required for the performant
ACTIVITY
ABSTRACTIONS execution of professional/vocational ”activities”
ONTOLOGY consists of
BEHAVIORAL / SKILLS Each activity is abstracted using concepts and their
SEMANTICS relations
©Synergetics 2012 All rights reserved

27. Activity Semantics
1 Job Category
• Competency framework (ROME CATALOG) 2 Associated competencies
• Semantic Activity abstractions click 3 Semantic entities & relations
2
1
3

28. Competency ANALOGY: similarities
Activity Semantics Compare similarities between 2 competencies
• Job categories
• Competencies
belonging to a
job category
Score on 1
Common
Relation
©Synergetics 2010 all rights resverved

29. Competency GAP analysis : differencies
Compare differences between 2 competencies
Activity Semantics
• Job categories
• Competencies
belonging to a
job category
Score on 1
Common
Relation
©Synergetics 2010 all rights resverved

30. Find candidates
Specify vacancies
1. Select ranking threshold
2. Select (in)experienced candidates
3. Select the job offer to start the search
©Synergetics 2010 All rights reserved

31. Find candidates All animals are equal!
Click to visualise the competency
Results Overview
Semantic Ranking
according to “presence”
of relevant competencies

32. Visualise the profile
1. Click the ‘bar icon’, multiple selections are allowed
3. Select graph type
2. Graphic representation of the “activity semantics” of the competency (indicators)
©Synergetics 2010 all rights resverved

33. Find your candidate
Visualise the semantic differences of the
7 competencies from the 9 selected candidates
7 competencies
…but some are more equal than others 
9 selected candidates
©Synergetics 2010 All rights reserved

34. Conclusion
• The “Activity Semantics” extraction method makes use of Natural Language
Technology :
• Abstraction : Extract universal semantics from different competenvy
descriptions
• Interpretation : Skills and Competency extraction from
employabilityPortfolios™, texts, CVs, vacancies and … Regional
Employability Platform enabled systems!
• For this demo we used (anonymised) real life data from 13.000 candidates
and 13.000 vacancies.
• The Activity Semantics method is based on its own ABAS Ontology
• Semantic annotation & enrichment of existing ESCO and other European
occupation, vocation, qualification and competency frameworks (ROME,
COLO, NOS, UKCES, SSC, QCF, …etc)
• This semantic method allows for considerable refined decision making when
searching for the right candidate or vacancy, based upon a set of required or
desired competencies.
©Synergetics 2010 All rights reserved

35. Semantic DNA
The Technology
• Semantic metadata
• Interpretation
• Input: Textual data
• Output: Semantic DNA
• Comparison
• Input: Semantic DNAs
• Output: Scores of Similarity, Difference, Equivalence.
These are the basis for semantic matching
• Robust text understanding technology
• Language parsing and interpretation
• Customisable and optimisable
• Languages (French, English currently, Dutch coming up)

36. Use of Competence DNA
• Operation
• Extraction of competence semantics
• Semantic comparison of competences
• Customization by competence frameworks
• Knowledge bases of competence frameworks
• Language capability, based on knowledge bases
• Comparison of competences:
• free2ref
• free2free

37. Example free2free comparison
Text Editor for Input
Text Input
Semantic DNA
Semantic comparison