JANZZ named as a Sample Vendor for Skills Ontologies in Gartner Hype Cycle for HCM Tech 2020

We are proud to announce that JANZZ.technology has been identified by Gartner as a Sample Vendor of Skills Ontologies in the Hype Cycle for Human Capital Management Technology 2020. This recognition validates the innovative approach of our solutions for businesses and public employment services based on our unique multilingual job and skills ontology.

What is the Gartner Hype Cycle?

“Gartner Hype Cycles provide a graphic representation of the maturity and adoption of technologies and applications, and how they are potentially relevant to solving real business problems and exploiting new opportunities. Gartner Hype Cycle methodology gives you a view of how a technology or application will evolve over time, providing a sound source of insight to manage its deployment within the context of your specific business goals.[1]

JANZZ named as a Sample Vendor for Skills Ontologies in Gartner Hype Cycle for HCM Tech 2020

Skills Ontologies, rated as highly beneficial for HCM, are currently in the first of five stages in the Gartner Hype Cycle: the Innovation Trigger. Gartner describes this stage is the one where “a potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest trigger significant publicity. Often no usable products exist and commercial viability is unproven.” We started developing our skills ontology over a decade ago, in 2009. It is now the most comprehensive multilingual skills ontology worldwide and has proven itself repeatedly over the past few years, being successfully deployed in multiple systems of any scale.

What is a Skills Ontology?

A skills ontology organizes large collections of concepts concerning capabilities, competencies, knowledge, and experience, as well as the relations between them in a data structure. It provides a basis for AI applications in areas such as talent acquisition, talent development and workforce planning. Numerous providers claim that they have an ontology when in reality, they only have a taxonomy or a library.[2] At JANZZ, we have a true ontology, JANZZon!. But it does not just include skills, it is a job and skills ontology. This means that it also encompasses occupations, job titles, work experience, training and qualifications, industries and much more. Matching skills alone without taking other information like occupations/roles into account can result in extremely inaccurate results. A retail cashier and a retail pharmacist will have skills in common, e.g., customer service skills, but their key skills, namely their specialist knowledge and their qualifications, differ dramatically. So even if all other listed skills are a match, it would be completely nonsensical to suggest a cashier for a pharmacist position. Context is essential, and one of the key types of information generated by our job and skills ontology.

Moreover, unlike other skills ontologies on the market, JANZZon! distinguishes levels of skills and their context. For instance, the level of skills required in a junior position are not the same as for a senior specialist, and the skill set of a project manager in application development is not identical to that of a project manager in interior design. These differences are represented in our job and skills ontology JANZZon! and are one of the driving factors in the astonishing accuracy of our job-candidate matching and career pathing tools.

Watch our video about the JANZZ ontology

Why not just stick with skills libraries and taxonomies?

Skills or job libraries, which many technology providers still rely on, are primarily built by experts (often psychologists) analyzing and classifying skills and skills levels related to job categories or functions. These methods are labor intensive and limited, often focusing on cross-functional skills or on a limited number of job-specific technical skills. Moreover, in the fast-changing world of work, these libraries are almost always outdated as soon as they are finalized.

The key issue with these libraries, however, is that there is no such thing as a standard skill profile for a given occupation. This means that search and matching results based on skills libraries are mostly disappointing at best. On the other hand, with the right skills ontology, you get a continuously updated, comprehensive database that provides the basis for technology that “transforms user expectations for relevance of job searches, matching of candidates to job roles, and recommendation of learning content.”[3]

The crucial advantage of a skills ontology compared with skills libraries or taxonomies is that it links synonyms as well as similar and related skills. This dramatically improves search and matching by translating the diverse vocabularies of different stakeholders, job postings and candidate/employee profiles into a common language and giving search terms context. As a result, classical keyword-based approaches can be replaced by semantic search where the system understands the meaning of search terms as opposed to stubbornly comparing strings of characters.[4] For instance, when entering the term CEO, the ontology-based system will exclude results like Assistant to the CEO. Or, upon entering the term Mechanic, it will suggest more precise terms like Auto Mechanic or Boat Mechanic. And the best people for the job can be identified much more accurately – without the noise of unsuitable candidates or the risk of top candidates slipping through the cracks.

Moreover, our ontology-based systems can recognize implicit skills in occupations ranging from Sign Painter to Cybersecurity Project Manager and use these skills to provide more satisfactory results – not only of jobs and candidates, but also in profiling, gap analyses and career pathing. The contextual knowledge stored in our skills ontology is also key to our highly performant job and CV parser.

Pioneering solutions in HCM tech

Most of the current ontology-based HCM applications on the market are still quite rough around the edges and there is no one-size-fits-all solution. Instead, a combination of models and approaches is needed. Here at JANZZ.technology, we already have a well-established skills ontology as well as highly accurate technology for semantic search and match, gap analyses, profiling, and job and CV parsing. However, we are driven to continuously improve and extend our solutions and thus very actively engaged in R&D, ceaselessly developing pioneering technology to tackle new challenges. Our mission is to help improve the HCM experience by providing efficient and highly performant solutions without compromise.

And why are we so far ahead of the Gartner Hype Cycle? Because we started in 2008, long before anyone was talking about AI and knowledge representations, long before Google and the markets realized that advanced AI solutions will simply be impossible without ontologies. That is why we have a head start of several years today.

Take advantage of this and integrate our job and skills ontology into your applications via our simple APIs. Contact us at info@janzz.technology to find out how we can transform your experience with our cutting-edge ontology-based solutions.

[1] Gartner Methodologies, “Gartner Hype Cycle,” 2020. https://www.gartner.com/en/research/methodologies/gartner-hype-cycle
[2] For a better understanding of the fundamental difference between ontologies and taxonomies, read our post: https://janzz.technology/ontology-and-taxonomy-stop-comparing-things-that-are-incomparable/
[3] Poitevin, H., “Hype Cycle for Human Capital Management Technology, 2020”, Gartner. 2020.
[4] For more information on this topic, request a copy of our white paper «Keyword vs. ontology based, semantic matching» via email or contact form.

JANZZ’s job matching platform ParaEmpleo singled out by the IDB as an AI success story

The IDB is an important driver of artificial intelligence as a tool to address challenges in the labor markets in Latin America and the Caribbean (LAC). As part of the IDB’s fAIr LAC initiative studies, they recently published an interesting report on how to utilize AI for labor intermediation in public employment services (PES). This technical paper provides an overview of key aspects considered by PES when deciding to adopt AI for their operations, as well as discussing the benefits and risks of implementing AI-based solutions for PES.

JANZZ is excited to share that its project ParaEmpleo – a semantic job matching platform realized in collaboration with Paraguay’s Ministry of Labor, Employment and Social Security (MTESS) – is described as a “success story in incorporating AI”. It is so far the only project of its kind in LAC, and JANZZ.technology is eager to continue implementing solutions for PES that use AI for social good, generating better social services and giving people more perspective.

You can read the IDB report Artificial Intelligence for Job Seeking : How to Enhance Labor Intermediation in Public Employment Services here.

JANZZ.technology listed as one of the best tech startups in Zurich

We are proud to share that Seedtable has selected JANZZ as one of Zurich’s 96 best tech startups to watch in 2020.

JANZZ has been busy developing and implementing semantic technology for job matching and CV parsing, making progress in leaps and bounds this year. Our products and solutions are now deployed by several public employment services around the world, as well as by large private corporations, and are endorsed by organizations such as World Bank, IDB and ILO. We have also been increasing our team size and can now offer our solutions in over 40 languages.

Seedtable is a weekly newsletter on European tech read by over 12,000 founders, investors and operators every week. It ranks startups across Europe based on their founders’ qualities, growth rate, funding and scalability potential.

Thank you to Seedtable for an inspiring start to the new year!

JANZZ.technology in a recent ILO report: From big data to smart data

Big data and AI still have a hard time today in gaining traction in the field of HR and employment services due to the poor quality and lack of explanatory power in the data. As JANZZ explains in a recent ILO report, any predictive analysis based on big data and determined by a large number of variables is rather inaccurate. The longer the time horizon and more variables included, the less likely such prediction is going to be completely or even partially close to reality.

Hence, any recommendations for market participants such as forecasts of the future employability and required skills of job seekers will generate little or no significant results if based on approaches that simply compile and evaluate all available job advertisements from all available sources in a market over a period of years. Because the skills are often presented and processed without any relevant semantic context, for example, the typical forecasts of general “top skills” as published regularly by LinkedIn and the World Economic Forum. One will find the skills listed are too generic or general to be used in matching, indeed, they are barely relevant for many occupations.

From the very beginning, JANZZ.technology has determined to form big data into smart data using a structured and fully semantic ontological approach and over the years, it has repeatedly proven to be the only game-changer. To learn more, please find the full article in the ILO report:

The feasibility of using big data in anticipating and matching skills needs

Global Labor Market Insights: More quality jobs are needed for female part-timers

In previous articles (The silver workforce, The world’s most homogeneous society is opening new doors) we have talked about the aging and shrinking working-age population in Japan and how Japan is taking measures to deal with the implications of this demographic issue, including extending retirement ages and welcoming migrant workers. This article focuses on another of Japan’s options to boost workforce: the large number of women that are held back or excluded from the labor market.

The idea that women should stay at home as primary caregivers is deeply seated in Japan. A 2016 poll revealed that this view is still held by 45% of men surveyed. When Shinzo Abe came to power in 2012, he and his government unveiled a comprehensive policy package, known as “Abenomics”, to revive the Japanese economy, of which “womenomics” – the plan to create a “Japan in which women can shine” – has been a key element. “Womenomics” aims to redress Japan’s ingrained gender inequality and to solve the labor shortages by encouraging more women to participate in the job market.

The Abe government passed legislation to extend parental leave and eliminate a tax deduction for dependent spouses. They also ensured rapid expansion of childcare facilities for working mothers including free and affordable childcare for low-income families. They have worked intensively with Japan’s business associations to increase hiring, promoting and empowering women, targeting 30% women in leadership positions by 2020.

How effectively has the program been carried out so far? According to last year’s report by the International Labor Organization, the proportion of Japanese women in management and other leadership positions was 12% in 2018, falling far short of the 30% target and well below the 27.1% global percentage. In the World Economic Forum’s annual Global Gender Gap Index from the same year [1], Japan ranked 110 out of 149 countries, barely moving up from the year before. In the 2020 report, Japan slid down to rank 121 [2]. Faced with disappointing numbers, the Japanese government has had to push its target date 2020 as far back as 2030. [3]

Indeed, although the female labor participation rate reached 71% following the initiative of “womenomics”, outperforming the EU and US, critics claim this policy approach has been no more than surface shine. Multiple sources indicate the disproportional representation of Japanese women in part-time and non-regular positions. The Global Gender Gap report 2020 shows that more than a third of female employees hold these positions, compared with just 11.5% of male employees.

When part-time work began to emerge and expand in the 1970s, it was regarded as a manifestation of a more flexible and non-standard labor market. Compared to full-time jobs, they are ideal for working parents to combine work with family responsibilities. They can enable older people to prolong their work life and people with health issues to remain in the labor market. However, on average, many part-time jobs are of poorer quality: they are disproportionately concentrated in the lower-paid professions with poorer working conditions and less job security. In the case of Japan, economists at MIT and University of Tokyo found that 69% of female Japanese workers are active in sectors such as retail or food and accommodation, where traditional female-dominated service jobs are offered [4]. The activities they perform are strongly associated with the informal sector and have the least regulatory protection; for many higher-paid and managerial positions, one can hardly find part-time opportunities.

Not only in Japan, but around the globe, part-time work is largely performed by women with family responsibilities. According to data from the OECD [5], the Netherlands have the highest rate of female part-time employees, with 58% in 2018. Switzerland, Australia, Ireland, UK and Germany are also among the top. However, even in these countries there are still many barriers that hinder the development of part-time employment into an option that truly ensures equal opportunities. This is not only the case in Japan, it is a phenomenon encountered across the globe.

Part-time employment is a proven means to increase the female participation rate in the labor market, contributing to a more flexible and productive workforce. For policy makers, it is important to ensure that wider measures are put in place to enhance the quality of this work, promoting part-time positions and job sharing in areas with better pay, better working conditions and higher job security, as well as actual career opportunities in part-time; and more generally, to design policies in a way that promotes gender equality.

For almost a decade, JANZZ.technology has been observing and working with many labor markets worldwide. We offer our know-how and the right data on skills and specializations to tackle general challenges in job market. If you are interested in leveraging our data and experience, please write now to sales@janzz.technology


[1] WEF. 2018. Global Gender Gap Report 2018. URL: http://reports.weforum.org/global-gender-gap-report-2018/data-explorer/

[2] WEF. 2020. Global Gender Gap Report 2020. URL: https://reports.weforum.org/global-gender-gap-report-2020/the-global-gender-gap-index-2020/results-and-analysis/

[3] Kazuhiko Hori. 2020. Japan gov’t to push back 30% target for women in leadership positions by up to 10 years. URL: https://mainichi.jp/english/articles/20200626/p2a/00m/0fp/014000c

[4] Shinnosuke Kikuchi, Sagiri Kitao and Minamo Mikoshiba. 2020. Who Suffers from the COVID-19 Shocks? Labor Market Heterogeneity and Welfare Consequences in Japan. URL: https://www.carf.e.u-tokyo.ac.jp/admin/wp-content/uploads/2020/07/F490.pdf

[5] OECD. 2019. Directorate of Employment, Labour and Social Affairs. URL: https://www.oecd.org/els/soc/LMF_1_6_Gender_differences_in_employment_outcomes.pdf


Education Zones – Bridging the Gap Between Candidate Education and Employer Requirements in Online Job Matching

Anyone using online job-matching services has undoubtedly encountered seemingly obvious or even ridiculous mismatches. Many of these mismatches are based on inadequate processing of the information related to education. For instance, when a job matching algorithm focuses only on the level of education (e.g. Bachelor or Master or high school), a travel agent may be suggested a job as an IT specialist. They both have a degree, but – crucially – in a different context. In addition, recently completed lower level courses or certificates become more significant as they complement work experience, in many cases gradually rendering the highest level of education obsolete. But these matching algorithms still match jobseekers with 20 years of work experience primarily based on a university degree dating back 20 years instead of their more recent and more relevant further training.

To address these challenges, JANZZ.technology has created the concept of Education Zones, which are clusters of educations related to a given professional field such as “Tourism” or “Computer Science”. This way of recombining degrees, training and other education at various levels and from different fields of education provides a much more realistic representation of professional fields, and when used in a matching algorithm, can generate significantly better matching results between a candidate’s education and a job’s requirements.

Education Zones are most beneficial when a job description contains popular generic phrases like “has an educational background in …”. At the same time they are well-suited to help matching algorithms more accurately capture the growing number of candidates with non-linear education paths by generating a more precise profile of the candidate’s specific knowledge, competences and skills. JANZZ’s Education Zones provide a more effective categorization of educations according to professional fields, creating the basis for sounder, more accurate matches. To find out more about Education Zones, read our white paper:

Education Zones – Bridging the Gap Between Candidate Education and Employer Requirements in Online Job Matching

Creative Associates International reported on UbicaNica.jobs

Creative Associates International reported on UbicaNica.jobs – our AI powered and un-biased job placement platform in Nicaragua. Ayan Kishore, Director at Creative’s Development Lab said in the article, “Nicaragua doesn’t really have many opportunities for somebody to go online and find a job, so if you’re in Nicaragua, you’re looking at newspaper or depending on word of mouth. That’s just not how people are finding opportunities in the rest of the world.” Thanks to the technology behind UbicaNica.jobs delivered by JANZZ and the other key leads on the project, it will be a real game-changer for the country’s job seekers, especially the youth. JANZZ.technology is keen to continue contributing to projects that use AI for social good and adopt ethical and responsible principles, thus generating better social services in more regions.

Click here to read the article.

New AI job platform to bring together all the skills of Nicaragua and boost the economy

Zurich, Switzerland/Washington, DC/Managua, Nicaragua, October 2020 – JANZZ.technology, Creative Associates International, the SDC (Swiss Agency for Development and Cooperation) and Fundación COSEP (Superior Council of Private Enterprise) will soon launch Ubicanica.jobs, an AI-driven platform for Nicaragua designed to strengthen the search for jobs and talents, improve employability and boost the country’s economy.

In the adverse economic environment Nicaragua is currently faced with, and which has become even more precarious in recent months, many Nicaraguans lack information, perspective and visibility—especially the youth of Nicaragua, many of whom are denied opportunities to develop critical skills needed for employment, and access to quality training, job placement services and jobs.

The development organization Creative is tackling these issues through the Aprendo y Emprendo project financed by USAID and the SDC. This initiative aims at strengthening the private Technical Vocational Education and Training (TVET) system in order to provide the youth of Nicaragua with vocational skills, life skills, work readiness skills, and soft skills training that will help them become capable employees and entrepreneurs. As part of this project, Creative partnered up with Fundación COSEP, a foundation of the leading business chamber in Nicaragua, and Swiss tech firm JANZZ.technology to develop Ubicanica.jobs, which contributes by providing visibility, career counseling and job placement services.

“Our objective was to create a unique platform that will bring together all the skills and work opportunities of Nicaragua, matching precisely the right people to the right jobs,” says Stefan Winzenried, CEO of JANZZ.technology. “We really hope that as many people and companies as possible use the platform. The potential of having all job offers, job seekers and everything that is involved in the labor market on a single platform is massive: it will improve employment opportunities for everyone on every level.”

Unlike Nicaragua’s existing platforms, job searches on Ubicanica.jobs are not based on keywords. Instead, the user posts a profile of their individual skills and competences, which the system then analyzes using artificial intelligence and compares with the requirements of potential job offers. The technology behind it processes job-related data through deep learning algorithms and knowledge graphs to identify the best possible matches between jobs and candidates and make accurate suggestions to both job seekers and recruiters. The platform can also show users how to improve their chances in the labor market. The system analyzes which jobs and skills are currently in demand and based on the user’s profile, it recommends courses and training to improve employability.

To help mitigate the effects of the current situation in Nicaragua, the platform needed to be up and running as quickly and cost-effectively as possible. It was thus implemented as a white label product—in just 90 days—and is now operated by Fundación COSEP as a SaaS solution together with JANZZ.technology. COSEP is convinced that utilizing the innovative approach of Ubicanica.jobs will drive the social and economic development of the country. “Employment and entrepreneurship are key to a society’s wellbeing and we all have something to offer,” says one of their leading officials. “Through Ubicanica.jobs we can boost Nicaragua’s economy and strengthen our society by better matching the country’s talents and needs. If we all sign up and use the platform, it’ll be a real game-changer.”


Ζητούνται 1.000.000 βιογραφικά

Προσπαθείτε να γράψετε το καλύτερο βιογραφικό σημείωμα για να εντυπωσιάσετε τους εν δυνάμει εργοδότες και να πάρετε τη δουλειά των ονείρων σας; Γνωρίζετε ότι, κατά μέσο όρο, κάθε θέση εργασίας προσελκύει 250 βιογραφικά και θα έχετε μόνο 2% πιθανότητα να λάβετε συνέντευξη για τη θέση; Ναι, 2%. Τώρα μπορεί να αναρωτιέστε πώς οι recruiters επιλέγουν το 2%. Λοιπόν, οι περισσότεροι χρησιμοποιούν εργαλεία διαχείρισης υποψηφίων για να επιλέγουν βιογραφικά, απορρίποντας έως και το 50% των βιογραφικών, χωρίς να τα έχουν κοιτάξει. Ωχ, ναι, αυτό μπορεί να περιλαμβάνει τη δική σας και γι’ αυτό λαμβάνετε πάντα ένα τυπικό μήνυμα απόρριψης-αλλά-ευχαριστώ μετά.

Στη JANZZ.technology, χτίζουμε μια εναλλακτική λύση που επιτρέπει σε κάθε βιογραφικό να αξιολογείται από τεχνητή νοημοσύνη και, το πιο σημαντικό, κάθε υποψήφιος θα λαμβάνει σχόλια από το σύστημα που επεξεργάζεται τις ελλείπουσες δεξιότητές τους (γιατί δεν προσλαμβάνεστε) και πιθανές προτάσεις για περαιτέρω εκπαίδευση (πώς μπορείτε να βελτιώσετε τις πιθανότητές σας) προκειμένου να εξασφαλίσετε την ίδια δουλειά στο μέλλον.

Για το σκοπό αυτό, σας ζητάμε να μας βοηθήσετε να βελτιώσουμε τον Machine Learning αλγόριθμο μας. Δείτε πώς μπορείτε να συμβάλλετε στη δημιουργία του ανθρώπινου παράγοντα σε συστήματα AI:

  • Στείλτε το βιογραφικό σας σημείωμα στο info@janzz.technology. Εάν σας κάνει να νιώσετε πιο άνετα, μπορείτε να διαγράψετε τα προσωπικά σας στοιχεία.
  • Γλώσσα: αναζητούμε βιογραφικά σε γαλλικά, ιταλικά, αγγλικά, γερμανικά, ελληνικά, νορβηγικά, ολλανδικά, πορτογαλικά, άλλες γλώσσες που χρησιμοποιούνται στην ΕΕ, κορεατικά, κινέζικα, ιαπωνικά, ταϊλανδέζικα, ινδονησιακά, μαλαισιανά, βιετναμέζικα και αραβικά.
  • Μορφή: Οποιαδήποτε. Από το τυπικό έγγραφο 2 σελίδων, έγγραφο. στα πιο δημιουργικά και καινοτόμα.
  • Υποσχόμαστε να μην σας στείλουμε ανεπιθύμητα μηνύματα ή να χρησιμοποιήσετε το βιογραφικό σας για οποιονδήποτε άλλο σκοπό εκτός από την εκπαίδευση του αλγορίθμου. Θα διαγράψουμε επίσης το βιογραφικό σας μετά την εκπλήρωση του σκοπού του.

Βοηθήστε μας να μοιραστούμε το μήνυμα και θα σας ενημερώσουμε με τον τελευταίο αριθμό βιογραφικών που λάβαμε.

100만 개의 이력서를 구함

당신은 꿈의 직장을 얻기 위해 채용담당자에게 인상을 줄 수 있는 최고의 이력서를 작성하려고 하나요? 평균적으로, 각 기업은 250개의 이력서를 받습니다. 당신은 당신의 꿈의 직장을 위해 면접을 볼 수 있는 기회가 2%밖에 없다는 것을 알고 있나요? 이제 당신은 채용담당자들이 어떻게 2%를 뽑는지 궁금할 것입니다. 대부분 그들은 이력서를 확인하기 위해 인재관리 소프트웨어를 사용하며, 한 번도 살펴보지 않은 이력서를 포함하여 50%까지 걸러냅니다. 아쉽게도 당신의 것도 여기에 포함될지도 모릅니다. 그것이 거절 이메일을 받는 이유입니다.

JANZZ.technology에서는 각 이력서가 인공지능에 의해 평가될 수 있도록 하는 대체 솔루션을 구축하고 있습니다. 가장 주목해야 할 것은 각각의 지원자는 추후에 같은 직종의 일자리를 확보하기 위해 누락된 기술(채용되지 않은 이유)과 추가 교육에 대한 가능한 제안(기회를 개선할 수 있는 방법)을 정교한 시스템으로부터 피드백을 받을 것입니다.
이를 위해 JANZZ.technology는 머신 러닝 알고리즘을 개선하는 데 귀하의 도움을 요청합니다. AI 시스템에서 인적 요소를 만드는 데 기여하는 방법은 아래와 같습니다.

  • 이메일 발송: info@janzz.technology (개인정보 삭제가능)
  • 이력서 언어: 프랑스어, 이탈리아어, 영어, 독일어, 그리스어, 노르웨이어, 네덜란드어, 포르투갈어, 그 밖에 다른 유럽권 언어, 한국어, 중국어, 일본어, 태국어, 인도네시아어, 말레이어, 베트남어 및 아랍어
  • 이력서 형식: 무관함 (기본적인 2페이지 이력서부터 창의적이고 혁신적인 이력서까지 모두 가능, word doc. 파일)
  • 당사는 귀하의 이력서를 머신 러닝 목적 이외에 스팸 발송 또는 다른 목적으로 귀하의 이력서를 사용하지 않을 것입니다. 또한 머신 러닝 알고리즘 개선 후에 당신의 이력서를 삭제할 것입니다.

위의 내용을 공유해 주신다면, 최근에 받은 이력서 수를 계속 업데이트해드릴 것입니다.