Automation, digitalization and AI have revolutionized the labour market: competencies that did not exist five years ago are now in high demand. While this did not lead to a surge in unemployment - unemployment rates are historically low – it has never been harder to fill vacancies with the current pool of jobseekers. Next to a loss of economic potential, job seekers that do not meet the demanded competencies face the severe risk of long-term unemployment. In this talk, we will present on how we help VDAB to reorient long-term unemployed persons towards the competencies of the future.

1. The Competences of the Future
How we applied AI to guide jobseekers at VDAB to learn future-
oriented competencies

2. How the labour market looked like
(...BEFORE CORONA...)
NOT THAT BAD AT ALL
• Low unemployment rate
• Increasing activity rate
...BUT...
• A lot of unfilled vacancies per unemployed
(unemployed persons often do not have the
skills demanded on the labour market).
• The skills-mismatch leads to:
• economic growth below potential
• long-term unemployment
2008
Mismatch on the Belgian labour maret
Bron: KBC economics obv Eurostat gegevens
2011

3. OUR APPROACH...
• finding which skills/competencies are
future-proof
• finding which skills/competencies may
serve as a stepping-stone for future-proof
competencies.

4. CompeTrend – trend analysis
Which skills are future-proof
• Trend analysis of the required competencies in
both the vacancies and the CVs:
• Predicting short- and long-term evolution of
competences
• Clustering of trends in different classes:
(disappearing, lowering, stable,...) to detect the
trends of the future.
0
1
2
3
4
2015 2016 2017 2018 2019 2020 2021 2022
Evolution competency Python
Aantal vacatures per CV Voorspellingen vacatures per CV
-2
0
2
4
6
Detrending competency Python
trend seizoen afwijkingen

5. CompeTrend – skills similarity
Detecting similarities between competencies
• Mapping out similarities between different
competencies using association rule mining:
• number of CVs containing both competencies
weighted by number of CVs containing at least
one of the two competencies.
• The following rules can be detected:if
competence Python (or R) in CV, then
competence R (or Python) in CV with 80%
probability.
• Skill similarity(Python, R): 0,8
CV Competences
1 R, Python
2 R, Python, C++, SQL
3 Java, Python, Scala
4 R, Java, Python, C++
5 R, Java, Python, Scala
Which skills serve as a stepping-stone for new skills.
Rule Sim.
{Python} {R} 0,8
{Python} {Java} 0,6
{Python, R}{C++} 0,5
…
…

6. CompeTrend – towards a competency graph
database
Python
SPSS
Java
R
SAS
Data Science
with Python
SAS 1 week
bootcamp
0,8
0,6
0,4
0,2
0,7
0,5
0,4
Education Competency
stable
Competency
growing
Competency
decrease

7. Only vacancies (not jobseekers)
• 7,451,790 vacancies (full population)
• Source: Competentiezoeker (Nalantis) extracted from
vacancies
• Based on Competent 2.0
• Two levels of aggregation:
• 7,000 competences
• 3,620 knowledges
(grouped into 128 profession groups (=beroepgroep) also
ranked by Competentiezoeker)
• 2008-01-01 till 2019-05-31: 137 months
• For this presentation: focus on knowledges
• Full data
• IN1: Operatoren chemie en kunststoffen
• kn_2916: ingenieurswetenschappen en -technieken
• kn_1878: good practices in een laboratorium
• kn_4499: Fysische chemie en experimentele
What data are we working with?
Evolution number of vacancies

8. Trend analyses

9. Time-series forecasts: methodology
We use two methods to forecast:
• a multi-input, multi-output recurrent neural network:
• Multi-input: lags of time-series as input
• Multi-output: current value of time-series as output
• Recurrent neural network: representation that
memorizes of past states.
• a Bayesian, multicomponent linear model:
• One competency at a time
• Does trend and seasonality decomposition
• Does turnpoint analysis
(performs best: 7% error on 6M test set)

10. Clustering: methodology
• We cluster the knowledges in groups according to
their 24M trajectories.
• As nearly all knowledges are increasing (most
even quite steeply) since 2015, we will look at
trajectories relative to the average growth.
• To not redo the clustering analysis every month
again, we detect the “true” clusters based on the
period 2016/01 till 2017/12.
• Next we assign the last 12M real data and next
12M forecasts to the nearest true clusters.
Clustering growth trajectories (5 clusters found)

11. Time-series forecasts: results
kn_2916: ingenieurswetenschappen en -technieken kn_741: eigenschappen van afdeklagen
kn_4428: sportkledij
Cluster 1
Cluster 0
Cluster 3

12. Skills similarity

13. Association Rules: relations between knowledges
• Can be used to:
• Reorient jobseekers to future-orienting
competencies: finding the shortest path from a
decreasing to an increasing knowedge
• Look at the most central knowledges on the
labour market
• Provide a better taxonomy of knowledges
• Vacancy auto-completion
• We look for rules in the knowledges listed in the vacancies.
• That is: suppose you have competency A, how likely is it that
you also have competency B.
• How? Lift(AB):
𝑃(𝑣𝑎𝑐𝑎𝑛𝑐𝑖𝑒𝑠 𝑤𝑖𝑡ℎ 𝑏𝑜𝑡ℎ 𝐴 𝑎𝑛𝑑 𝐵)
𝑃 𝑣𝑎𝑐𝑎𝑛𝑐𝑖𝑒𝑠 𝑤𝑖𝑡ℎ 𝐴 ∗ 𝑃(𝑣𝑎𝑐𝑎𝑛𝑐𝑖𝑒𝑠 𝑤𝑖𝑡ℎ 𝐵)
• Is symmetric: Lift(AB)=Lift(BA)
• If cooccurence of A and B is higher than would be expected
by random chance, lift will be above 1, otherwise it will be
between 0 and 1.

14. From association rules to the graph
[competences_graph_year_2008_2017.html]

15. www.arinti.ai
Veldkant 33a
2550 Kontich
Gaston Geenslaan 11 B4
3000 Heverlee
Wouter Baetens
Managing Partner
+32 472 44 01 43
wouter.Baetens@arinti.ai

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