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

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RECOMMENDATION A1

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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