Linking employment and academic competence contexts to achieve
correspondence
By: Alexandra González ErasDirector: Prof. Sylvie Ratté
Agenda
• Background• Problematic of the research• Literature Review• Research Objectives: Long term and short term• Proposed Methodology• Originality of the proposal• Tentative Timelime• Expected Contributions
• Curriculum Vitae• Skills (Generic and Specific)• Knowledge (Functions-Roles)• Training Needs
• Professional Profiles: • Unstructured• No follow standards
• Validatation Frames• Generic and Ambiguous• Interoperability
• Job Requirements• Skills Needs• Experts
Background
Competence Correspondence
Candidates
Standards
Employers
Educational Institutions
Stakeholders
Employers ambiguity job requirementsCandidates CV unstruturedUniversities unstructured professional profiles
Problematic
• Unstructured competences: garbage of words • Semantic problem: To many models and meanings.
• matching entities to specify an alignment, i.e., a set of correspondences
• alignment interpretation according to application needs , i.e., query answering or data translation
Ambiguous competence information
• Adaptative Assesment: latest trending competences• Assessing requirement knowledge and skills• Competence creating and planning: organizaing
training workshops to teach skills• Competence profiles and job recruitment platforms:
low amount of applicants to a one job advertisement
Changeable competence
environment
Background
Literature ReviewApproach Techniques Pro (+) Again (-) Authors
Semantic Annotation
Machine Learning
NLP
Hybrid Techniques
Language
Diccionaries
Ontologies
ExtractionM
atching
Literature ReviewLINKING Buitelaar 2007 Patterns Revolutionary
A2
A3
A4
A5
A6
A7
A8
A9
A10
RECOMMENDATION A1
A2
A3
A4
A5
A6
A7
A8
A9
A10 LLLLLL
Literature Review: Challengers
• Semantic Matching• Similarity measures
• Sparsing Data• Semantic Marching• Fuzzy Techniques
• Standards-> taxonomies and ontologies
• Standars Traduction añadir los gap o problems
• Automática /Semiautomática• Standards Manual
EXTRACTION TRANSLATION
LINKINGRECOMMENDATION
Objectives• General• Get a methodology to linking employment and academic contexts to
achieve competence correspondence.
• Specifics
• Obtain a concept model que permita la correspondencia de los elementos que conforman las competencias a través de los 3 contextos.
• Extract competences elements from spanish unstructured textual sources, according with concept model.
• Linking model based on similarity measures relating skills and knowledge to achieve a specific competence.
• Recomend skills and knowledge to achieve a specific competence based on previews results of verification process.
Methodology: Overview
Extraction
Data Gathering• Retrieval techniques
Extraction Parameters• Commons elements
• KkValidation of corpora
Linking Define Similarity Measures Validation
Translation Define Similarity model Aplication Validation
Recommendation
Proposed Methodology Techniques
• Competence similarity schema
• Competence recommendation schema
• Competence translation schema
• Competence retrieval schema
EXTRACTION TRANSLATION
VALIDATIONRECOMMENDATION
Originality
• Los sistemas recomendadores están orientados a la recomendación de recursos educativos y candidatos, • Se propone la creación de un recomendador de habilidades y
conocimiento requerido para alcanzar una competencia• Los modelos de verification de competencias están basados
en el matching semántico vía ontologías• Se propone crear un modelo híbrido que permita mejorar
matching de knowledge and skills a través de topic modelling.
Tentative Timelime
Expected Contributions
• Publications:• Extraction competence model from spanish unstructured
textual sources, linking employment and academic contexts.• Linking competence model based on skills and knowlegde
correspondence to achieve a specific competence.• Competence recomended model based on skills and knowlegde
to achieve a specific competence.• Possible Journals• Advances in Artificial Intelligence• Inderscience Publishers, International Journal of Knowledge and
Learning
• Elsevier, Computers in Industry
References• Malzahn, N., Ziebarth, S., & Hoppe, H. U. (2013). Semi-automatic creation and exploitation of
competence ontologies for trend aware profiling, matching and planning. Knowledge Management & E-Learning: An International Journal (KM&EL), 5(1), 84-103.
• Hassan, Fuad Mire, et al. "Ontology Matching Approaches for eRecruitment." International Journal of Computer Applications 51.2 (2012).
• De Leenheer, P., Christiaens, S., & Meersman, R. (2010). Business semantics management: a case study for competency-centric HRM. Computers in Industry , 61 (8), 760-775
• Segalas, J., et al. "What has to be learnt for sustainability? A comparison of bachelor engineering education competences at three European universities." Sustainability Science 4.1 (2009): 17-27.
• Fazel-Zarandi, Maryam, and Mark S. Fox. "Semantic Matchmaking for Job Recruitment: An Ontology-Based Hybrid Approach." Proceedings of the 8th International Semantic Web Conference. 2009.
• Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval (Vol. 1). Cambridge: Cambridge University Press.
• Xheneumont, J.-C. (2007). Requirements analysis for a methodology to design competency taxonomies. CoDrive Project.
• Zhong, Jiwei, et al. "Conceptual graph matching for semantic search." Conceptual Structures: Integration and Interfaces. Springer Berlin Heidelberg, 2002. 92-106.
Questions• Textual Sources• Job Platforms: Linkedin.com, multitrabajos.com, top ecuadorian
universities web sites• Corpora Treatment• Supervised approach: • Hybrid techniques:
• Validation process• Standards Validation: DISCO II• Experts validation: • Similarity measures schemas: according with context
requirements
Top Related