1. 11.30 – 12.00
Semantic job search- showcase
Jakub Zavrel, CEO Textkernel

2. ESCO and Semantic Search & Match for Jobs and People
Brussels, 9/10 October 2017

3. What is Semantic Search?
Find what you mean, not what you type
Search for things, not for strings

4. Semantic Search & Match. Why?
More relevant candidates, faster!
1. More candidates: Expanding words to concepts
No guessing what candidate wrote in the resume
2. Relevant candidates: Better filtering and ranking
Avoid wrong matches by understanding context
3. Work faster
- Start working on the most promising candidates first
- Be a great searcher without advanced Boolean search
- Use documents (jobs, resumes) to start automated searches
2 word user query = 35+ system query

5. Three Elements of Semantic Search
Document understanding
Domain knowledge
Machine Learning
CV & job parsing
Information extraction
Taxonomies
Ontologies
Skills
Keywords
Algorithms learn from a large set of data
Learning to Rank
Deep Learning

6. job branch langskill location
Match!
Match! models construct the Search! Data Model out of extracted information.
Vacancy Extraction
job=Java Ontwikkelar
city=Amsterdam
langskill=Duits
experience=7
CV Extraction
job=Java Developer
city=Amsterdam
langskill=German
experience=7
Match!
Vacancy Match
Normalizer
job=23
branch=IT
langskill=DE
experience=5..10
loc=Amsterdam+10
CV Match
Normalizer
job=23
branch=IT
langskill=DE
experience=7
loc=Amsterdam
Search!
Data Model
job branch langskill location
experience
The XML fits the Search! Data Model and is semantically enriched when INDEXING.
The result is a QUERY that fits the Search! Data Model. It is semantically enriched when executed

7. ESCO – What it means for us
Direct availability of semantic query enrichments in EU languages (on top of 6 that we developed internally)
Cross lingual search because of aligned taxonomies.
Occupation to skills expansion for better search

8. Demo

14. Some numbers on coverage
Numbers of results?
Occupation
ESCO Essential
ESCO Optional
TK Skills
ICT Security Technician
2230
2255
ICT Network Technician
128
454
Data Scientist 21 92
200
Office Manager 23
187
9514
Sales Manager 17 47
164
Sales Assistant 1
543
Waiter/waitress
207
Receptionist
190
193
197

15. Some numbers on coverage
How is the general coverage of the data by ESCO?

16. Some numbers on coverage
How is the general coverage of the data by ESCO?

17. Summary
Textkernel will support ESCO v1 in its Search & Match products.
Direct availability of semantic enrichments in 26 EU languages.
A standard to evolve.
Further steps needed to address:
Gap between ESCO terms and actual language use
Maintenance process