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LO-MATCH platform for job matchmaking

LO-MATCH platform for job matchmaking. Tools for job seekers and employers Fabrizio Lamberti Dipartimento di Automatica e Informatica Politecnico di Torino Italy. Related activities. Work package 6

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LO-MATCH platform for job matchmaking

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  1. LO-MATCH platform for job matchmaking Tools for job seekers and employers Fabrizio Lamberti Dipartimento di Automatica e Informatica Politecnico di Torino Italy

  2. Related activities • Work package 6 • Strategies and tools for semantic description in personnel recruitment and job seeking contexts • Work package 7 • Identification of profiles and KSC semantic description • Work package 8 • Software development for job matchmaking • Work package 9 • Piloting

  3. Expected results • Deliverables • D24: Report on the methodology for ontological description • of offer and demand in the context of personnel • recruitment and job seeeking • D25: Specification of a semantic engine for learning outcome • based job matchmaking • D26: Occupational definitions • D27: MATCH ontology • D28: LO-MATCH platform • D29: Training sessions

  4. Matchmaking • Definition • “Matchmaking is the problem of matching offers and requests, such as supplies and demands in a marketplace, services and customers in a service agency, etc.” • Objective • To discover best available offers to a given request • Context of interest for the project • Labor market

  5. Matchmaking • Obstacles • marked differences in the strategies in use for describing job profiles, on one side, and curricula, on the other side &*$! @# £#$ #$*!%? %£#$ &%$! #$%!% %&!$!$ ! @$%!*@#!

  6. Project goal • The goal of MATCH is to make it possible that • when an job seeker draws up his/her curriculum (offer) • this is expressed in a way that is “compliant” to the way companies/employers have expressed their requirements (demand) • and vice versa (exchanging offer and demand) • so that the best MATCH between the offer and the demand is found

  7. Need for a “common language” • To maximize the overlap in matching curricula to labor market demand • Information pieces involved in the overall process have to be expressed by means of a “common language” • This would allow to • Limit ambiguities • Enable for the automatic management of information • Aspects to be taken into account • Partners’ know how • European tools (EFQ, Europass, etc.) • ICT solutions (ontologies, semantic modeling and reasoning)

  8. Need for a “common language” Savoir-être Habilités Abilities Attitudes Learning outcomes Capacità Saperi

  9. Occupational definitions • Bartender • To perform the counter and bar room fitting and arrangement • Organization of bar service • Equipment for bars • Tools for the preparation of beverages • … • To apply hygiene and food safety regulations • To use products for cleaning the premises • … • To be able to ensure the cleanliness of the premises with autonomy and assuming full responsibility • …

  10. Ontological descriptions: MATCH ontology To use Cleaning tools To be able to ensure the cleanliness of the premises with autonomy and assuming full responsibility Cleaniness Utilizzare strumenti per la pulizia degli ambienti Autonomy To use products for autonomously sanitizing the premises Autonomously To tidy up To tidy up the working environment To sanitize

  11. Semantic engine for job matchmaking Cleaniness To sanitize Utilizzare strumenti per la pulizia degli ambienti Autonomously Autonomy To tidy up the working environment

  12. Results achieved • Deliverables • D24: Report on the methodology for ontological description • of offer and demand in the context of personnel • recruitment and job seeeking • D25: Specification of a semantic engine for learning outcome • based job matchmaking • D26: Occupational definitions • D27: MATCH ontology • D28: LO-MATCH platform • D29: Training sessions

  13. LO-MATCH • An intermediary platform • With an interface based on a Web portal • Integrating a semantic inference engine • Acting as a facilitator in the definition of request and offer descriptions in learning outcome terms • As well as a solver capable of finding the best match • Targeted to job seekers and employers • Relying upon a knowledge base developed by the partners • Encompassing semantically annotated profiles in KSCs

  14. http://www.lo-match.polito.it

  15. D28: LO-MATCH platform • Four incremental releases • v1.0 Collection of profiles in KSC • v2.0 Preliminary interface • v3.0 Semantic annotation of • profiles • v4.0 Semantic-based job • matchmaking

  16. v1.0 Collection of profiles in KSC (Partners)

  17. v2.0 Preliminary interface (Job seekers/Employers)

  18. v3.0 Semantic annotation of profiles (Partners)

  19. v4.0 Job matchmaking (Job seekers/Employers)

  20. The matchmaking process • Perspectives of the job seeker/employer • Can be roughly split in three asynchronous moments • The employer define the requested profile • The job seeker define his or her own curriculum • The best match between job descriptions and curricula is identified • With the LO-MATCH platform • Work/Education/Training/… experiences characterized with learning outcomes from the occupational descriptions (KSC) • Best match is found by exploiting lexical and semantic relations among learning outcomes

  21. Dissemination

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