1) The document discusses regional employability ecosystems that go beyond traditional client/server models. 2) It proposes organizing these ecosystems around personal infrastructure for individuals, semantic coordination, business intelligence infrastructure, and other shared services. 3) The key aspects include governance, communal infrastructure and services, semantics, and trust/security.
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
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
16. 2. En wat met niet-gestructureerde data?
(competentiebeschrijvingen
beroepsbeschrijvingen
ervaringen
vacatures
…)
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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, …)
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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
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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
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20. PROCES PERSONAL
CENTRIC EMPLOYABILITY
PERSONAL (SEMANTIC META-)DATA
DATA (Content + VOCs)
GOV
EDU
PDS
COMPANY
SERVICE
PROVIDER
e- HR-
PORTFOLIOS PROCESSES
LEER-
DOSSIER
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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%
31. Find candidates All animals are equal!
Click to visualise the competency
Results Overview
Semantic Ranking
according to “presence”
of relevant competencies
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