luk vervennesynergetics nv   1
Een veelkoppig monster• Individu als “de nieuwe stakeholder in zijn eigen  processen” (Life Management)• Van syntax naar s...
Labour Markets beyond the client/server paradigmRegional Employability Ecosystems        Server         Server         Ser...
1. Bouwen op wat we hebben       (NEN norm)                             4
Luk Vervenne – Dr. Ingo Dahn
Application Profiles• Standards are too general – I don’t need all that fuzz• Standards are too restricted – they don’t le...
Make My Day!           Make My Profile – it‘s easy!•   Making mandatory what I do want•   Making optional what I tolerate•...
Making My Profile – Oh so tricky!• My own extensions• Mixing and matching many profiles• All referenced files must :   • e...
Prerequisites of Profiles• A community of stakeholders• Acquaintance with:   • their needs…   • their willingness to agree...
Conformance Testing• Test so that data conforms to YOUR profile• Problem:   • Each profile requires a specific test system...
Profiling: SchemaProf
Creating a   Test Service
Running the Tests
Survey Report
Detailed Reports
2. En wat met niet-gestructureerde data?       (competentiebeschrijvingen          beroepsbeschrijvingen               erv...
Purpose:   Semantic Comparison of Labour Market DataCompare real-world employability & employment data versusReference Dat...
Approach• Knowledge management  • Knowledge encoding (knowledge bases)  • Knowledge-based data processing     •   Annotati...
Data, Knowledge & Semantics1. Data : experience, goal, competence, preference, hobby, training, job, task2. Knowledge : fr...
PROCES          PERSONAL CENTRIC       EMPLOYABILITYPERSONAL   (SEMANTIC META-)DATA  DATA        (Content + VOCs)  GOV  ED...
Personal Data Infrastructure                                                            New M a r k e t sLinked-          ...
22
PROCES          PERSONAL                   SHARED                   (PARTIAL) CENTRIC       EMPLOYABILITY               SE...
The European need forsemantic interoperability                            24
Beyond Asset Descriptions                 FEDERATION                              25
: Data & Semantics                                        The Employability data from :                                   ...
Activity Semantics                                                 1 Job Category• Competency framework (ROME CATALOG)    ...
Competency ANALOGY: similaritiesActivity Semantics       Compare similarities between 2 competencies• Job categories• Comp...
Competency GAP analysis : differencies                                  Compare differences between 2 competenciesActivity...
Find candidates                  Specify vacancies1. Select ranking threshold                                           2....
Find candidates               All animals are equal!                                    Click to visualise the competency ...
Visualise the profile                1. Click the ‘bar icon’, multiple selections are allowed                             ...
Find your candidate                 Visualise the semantic differences of the              7 competencies from the 9 selec...
Conclusion• The “Activity Semantics” extraction method makes use of Natural Language  Technology :   • Abstraction : Extra...
Semantic DNAThe Technology• Semantic metadata   • Interpretation       • Input:    Textual data       • Output:   Semantic...
Use of Competence DNA• Operation  • Extraction of competence semantics  • Semantic comparison of competences• Customizatio...
Example free2free comparison                   Text Editor for Input  Text Input                                     Seman...
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10052012 luc vervenne synergetics van syntax portfolio naar semantische uitwisselbaarheid

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10052012 luc vervenne synergetics van syntax portfolio naar semantische uitwisselbaarheid

  1. 1. luk vervennesynergetics nv 1
  2. 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. 3. Labour Markets beyond the client/server paradigmRegional 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 InfrastructureOrganising the communality 2. Semantic CoordinationAssure & Promote Labour Market Mobility: 3. BI Infrastructure1. Governance (PPP) 4. eContent gateway2. Communality Based Infrastructure & Services 5. Matching Infrastructure3. Semantics 6. Trust & Security (+TTP)4. Trust & Security
  4. 4. 1. Bouwen op wat we hebben (NEN norm) 4
  5. 5. Luk Vervenne – Dr. Ingo Dahn
  6. 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. 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. 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. 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. 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. 11. Profiling: SchemaProf
  12. 12. Creating a Test Service
  13. 13. Running the Tests
  14. 14. Survey Report
  15. 15. Detailed Reports
  16. 16. 2. En wat met niet-gestructureerde data? (competentiebeschrijvingen beroepsbeschrijvingen ervaringen vacatures …) 16
  17. 17. Purpose: Semantic Comparison of Labour Market DataCompare real-world employability & employment data versusReference Data of Competences, Occupations, Qualifications, …Allows the meaningful search, assessment or match ofexperience, 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. 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. 19. Data, Knowledge & Semantics1. Data : experience, goal, competence, preference, hobby, training, job, task2. Knowledge : frameworks, expert rules, models, ontology3. Semantics : compare Data + Knowledge for semantic operations: • Data management • Knowledge management • Knowledge-based data management • Data-oriented knowledge managementTwo kinds of Semantics are involved:Precompiled: Static, knowledge-based operation = knowledge semanticsExtracted : Dynamic, in real-time in data management = data semantics 19
  20. 20. PROCES PERSONAL CENTRIC EMPLOYABILITYPERSONAL (SEMANTIC META-)DATA DATA (Content + VOCs) GOV EDU PDSCOMPANY SERVICEPROVIDER e- HR- PORTFOLIOS PROCESSES LEER- DOSSIER 20
  21. 21. Personal Data Infrastructure New M a r k e t sLinked- NL HR-XML Social Personal In GermanCV, Finance Portfolio iProfile UK Network Mobile Data Data context eGov data PersonalEuroCV 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. 22
  23. 23. PROCES PERSONAL SHARED (PARTIAL) CENTRIC EMPLOYABILITY SEMANTIC REFERENCEPERSONAL (SEMANTIC META-)DATA KNOWLEDGE FRAMEWORKS DATA (Content + VOCs) BASE CINOP/ GOV ECVET UWV EDU SEMANTIC SHL KNOWLEDGE PDS BASE SBBCOMPANY VDAB SERVICE ESCOPROVIDER UNIFIED SEMANTIC MATCHING e- HR- SKILLS & EVIDENCE COMPETENCES PORTFOLIOS PROCESSES LEER- VACANCIES DOSSIER 23
  24. 24. The European need forsemantic interoperability 24
  25. 25. Beyond Asset Descriptions FEDERATION 25
  26. 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 : GraduateOCCUPATIONAL 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. 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. 28. Competency ANALOGY: similaritiesActivity 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. 29. Competency GAP analysis : differencies Compare differences between 2 competenciesActivity Semantics• Job categories• Competencies belonging to a job category Score on 1 Common Relation ©Synergetics 2010 all rights resverved
  30. 30. Find candidates Specify vacancies1. Select ranking threshold 2. Select (in)experienced candidates 3. Select the job offer to start the search ©Synergetics 2010 All rights reserved
  31. 31. Find candidates All animals are equal! Click to visualise the competency Results Overview Semantic Ranking according to “presence”of relevant competencies
  32. 32. Visualise the profile 1. Click the ‘bar icon’, multiple selections are allowed 3. Select graph type2. Graphic representation of the “activity semantics” of the competency (indicators) ©Synergetics 2010 all rights resverved
  33. 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. 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. 35. Semantic DNAThe 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. 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. 37. Example free2free comparison Text Editor for Input Text Input Semantic DNA Semantic comparison

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