25/10/ 2011

Building system for online job posting analysis using ESCO

Pascaline Descy, Vladimir KvetanDepartment for Skills and Labour Market

Cedefop

Emilio ColomboCRISP

Outline

• Description of the project

– (V. Kvetan)

• Using ESCO as the overarching taxonomy

– (E. Colombo)

• Relevance and wider context of the proejct

– (P. Descy)

Why do we need skills intelligence?

• Skills are key for Europe’s competitiveness

• Skills are key for economic growth

• Skills are key for individual’s chances

=> Understand current and future skill needs

=> Assess skills mismatch its causes and consequences

=> Share data and evidence with a wide audience

Why exploring online job postings?

No EU-wide data on skill demands by employers

Surveys

• expensive

• suffer from time lags

• focused on limited set of skills

Job postings can help

• Understand timely market demands

• Collect detailed data on job-specific and generic skills

• Broken down by detailed occupation and region

Feasibility of online job postings analysis

Multilingual web vacancy data in 5 countries

Final results• Regions• Occupations • Contract type and working hours• Skills and jobs requirements

– ESCO skills– Additional skills– Other job requirements– Experience

Feasibility study outcomes

Setting up a pan European instrument is feasible

ESCO can be used as the overarching taxonomy

Results will provide unique and new information

Blacksmiths and toolmakers What skills “make a difference”?

Painters in UK• Job specific skills

• Transversal skills

• New skills

Possible future data applications

Using ESCO

• ESCO is used both as input and as output

• Input: main vocabulary for classifying

– Occupations

– Skills

• Output: main framework for presenting results

ESCO V1 vs V0• ESCO V1 is a major improvement over V0

• It is richer and more detailed (particularly on skills)

• It contains synonyms and related words

• It is organised as a graph and not as a strict hierarchy

• It is multilanguage

Richness is important• In the prototype, the classification process required

experts constructing a training

• Using the training set a machine learning algorithm was able to classify all the vacancies

• Limitations: experts generally classify vacancies univocally. This narrows the knowledge base. Moreover small mistakes can be made.

– Example programmer. Expert 1 can classify it as Software developer (ISCO 2512). Expert 2 can classify as system analyst (2511). Correct at 3 digit, not at 4 digit

Richness is important• New system: enriched knowledge base with the

entire information content of ESCO.

– Example: the machine embeds in the learning algorithm allthe possible relations that ESCO contains for programmers

• Synonyms and related words are coded in originallanguage and not directly translated from English.Original language is similar to descriptions containedin vacancies

=> Knowledge base is richer and larger

Richness is important

• ESCO knowledge base forms the initial training set;experts validate it or work only on vacancies that aredifficult to classify

=> Using the same vacancies of the prototype wediscovered 50 additional occupations (ISCO 4 digit) incomparison to classifying with ESCO V0

Hierarchy vs graph• ESCO V0 was strictly hierarchical

• ESCO V1 is partially hierarchical and organised like a graph

Hierarchy vs graph

• A graph structure is better when dealing with totallyunstructured data such as vacancies since

• we need to interpret a piece of text that containswords that can be strongly or weakly related to theconcept we need to grasp

Setting up an wide EU system

2017-2018

• Landscaping EU online job market

• Extend coverage to more countries and websites

• Apply ESCO v1 to classify occupations and skills

• Release early data set for 7 countries (FR, ES, UK, DE, IT, CZ, IE)

2018-2020

• Cover all EU Member States

• Refine algorithms and classifiers

• Open data access

• Data integration into online EU tools (Europass, Skills Panorama, Eures)

Use of Data• Inform career mobility,

• CVT choices

• job search strategies

By job-seekers / career counsellors

• Fine-tune training offer

• Train for shortage occupations

By training providers / PES

• Refine recruitment and talent management strategiesBy employers

• Understand skills demand, emerging skills, skill gapsBy policy-makers

Education, training, lifelong learning

Employment and skills matching

Labour mobility

Detailed and market driven data for policy

Working together

Time is needed…

Burčiak (SK)Federweisser (DE)

Vin bourru (FR)Scrumpy (UK)

Víno (SK)Wein (DE)Vin (FR)

Wine (UK)