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
…)
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%
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