Technologies, trends and theories:
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Our knowledge base contains information, interesting facts and selected articles on the latest trends and current developments on global labor markets and in the world of semantic technologies relating to human resources and recruitment, occupation (big) data and ontologies / knowledge graphs, job classifications, CV parsing, skills and job matching and much more.

ESCO: We expected an ontology – we got a disappointing term collection

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Almost four years had passed. We have waited for a long time – and we were curious to see what the EU has announced grandiosely. Steadily excited to see if it solves well-known problems of classification systems. The classification of the European Union for occupational data is called “ESCO” (European Skills, Competences, Qualifications and Occupations). Up to now, classifications have been solved by all states on their own, such as ROME in France or KLdB in Germany or CP in Italy.  » Read more about: ESCO: We expected an ontology – we got a disappointing term collection  »

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Our Article on the “Google for Jobs” Initiative Now Also Available in French, Spanish and Arabic!

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The landscape of online job search has gained a significant addition with wide-ranging implications. In line with its recently announced initiative Google for Jobs, Google launched a new jobs search feature right on its search result pages that lets you search for jobs across virtually all of the major online job boards. Google’s new initiative not only has the potential to disrupt the online job search market, but the initiative’s underlying data model, an occupation ontology,  » Read more about: Our Article on the “Google for Jobs” Initiative Now Also Available in French, Spanish and Arabic!  »

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Google Launches its Ontology-powered Jobs Search Engine. What Now?

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This week, the landscape of online job search has gained a significant addition with wide-ranging implications. In line with its recently announced initiative “Google for Jobs”, Google launched a new jobs search feature right on its search result pages that lets you search for jobs across virtually all of the major online job boards. Google’s new initiative not only has the potential to disrupt the online job search market, but the initiative’s underlying data model,  » Read more about: Google Launches its Ontology-powered Jobs Search Engine. What Now?  »

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Building a Job Matching Engine for the Global Labor Market

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Understanding resumes and job ads, and finding the best matches among a great number of them is probably one of the most challenging tasks for machines today. The results and the precision of search and matching processes are dependent upon the scope and depth as well as the quality and comprehensiveness of the applied contextual and background knowledge.
Job postings are often worded in industry- and company-specific jargon that job seekers do not search for and would not use when writing their resumes.  » Read more about: Building a Job Matching Engine for the Global Labor Market  »

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How an Ontology Can Help with Content-based Matching

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Job and candidate search, job recommendations and automated candidate evaluations have one thing in common. They are a matching problem.
Simply put, given a set of CVs and a set of vacancies, the most similar items should match, that is, these items should come out at the top of the search, recommendation or evaluation. Most applications use either of two high-level approaches to achieve this: behavior-based or content-based. They each have pros and cons, and there are also ways to combine the approaches to take advantage of both techniques.  » Read more about: How an Ontology Can Help with Content-based Matching  »

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