Technologies, trends and theories:
knowledge at the cutting edge.

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.

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


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?  »

Building a Job Matching Engine for the Global Labor Market

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  »

How an Ontology Can Help with Content-based Matching

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  »

Industry Taxonomies Enhanced by JANZZ’s Occupation Ontology

At the heart of JANZZ.technology’s ontology of occupations and skills, there are over 35 taxonomies, among which occupation, skills and industry taxonomies like O*Net, ESCO, NAICS and ISCO-08. They are mapped by the JANZZ curation team to form a single entity that serves as a relational model for a great part of the world’s economic activity. As part of the latest additions to the occupational ontology JANZZon!, the curation team has inserted the two industry classifications GICS and ICB into the ontology,  » Read more about: Industry Taxonomies Enhanced by JANZZ’s Occupation Ontology  »

Why leading employment services and software providers are betting on ontologies.

Algorithms are out, datasets are in. Perhaps one of the crucial findings in data science today is that datasets – not algorithms – might be the key limiting factor to developing human-level artificial intelligence. This contention is especially true in the case of solutions for the labor and recruitment market. Many companies in the recruitment market and public employment services are taking notice and are investing in the ontology-based solutions of JANZZ.technology.
Therefore, we have taken a brief moment to lay out the underlying reasons why datasets have become so important.  » Read more about: Why leading employment services and software providers are betting on ontologies.  »