Our consulting services in the field
of semantic technologies and solutions.
JANZZ.technology enables human resources departments, companies operating in the field of personnel recruitment or HCM, providers of ERP and ATSs as well as public employment services to make giant strides in terms of transparency and efficiency when it comes to recruitment, skills and job matching, as well as the increasingly important adoption of semantic technologies and solutions. The cognitive computing solutions offered by JANZZ.technology can deal with the highly complex data involved in HR processes. For example, they enable automated filtering of a high number of applications making it that much easier to find the right applicant in a faster and more efficient way.In order to benefit the most from the planned investment, which tends to be of a long-term nature, the often very challenging and complex projects need to be optimally structured and specified from the outset in the form of integrated business solutions – scalable and sustainable business solutions that perfectly combine technologies, knowledge and specialists.
The rising number of increasingly intelligent web applications calls for extensive background and contextual knowledge as well as technological knowledge. Our experience, concepts and consulting services make it possible to exploit the full potential of data and applications already available to you in connection with labor markets, training and employment environment, job descriptions, skills profiles, classification systems and much more.
In conjunction with labor-market, vocational-training and qualification topics, JANZZ.technology offers a large number of excellent consulting services. These range from data structuring and evaluation through semantic matching and the associated necessary methodologies to the modelling, structuring and maintenance of private, highly specific ontologies, taxonomies and thesauri, with the aim of making background and contextual knowledge machine processable. We support our clients in meeting their individual needs and requirements relating to data and data structuring, or the integration of already existing – for example country-specific – taxonomies. We show what the advantages of applying semantic technologies are, and provide answers to the following key questions, for example within the context of detailed system audits:How can we make the potential of existing data on job descriptions and skills profiles visible?
- How can we benefit from semantic matching and drive the automation of processes forward? What applications actually exist, and which ones are appropriate to use?
- Should we develop an in-house ontology or buy/use an existing one? Is our already available data (from online job information centres, CV libraries, etc.) really suitable for operating semantic skills and job matching?
- How should data, for example in CRM systems of public employment services or companies, be structured in order to make semantic matching or gap analyses, benchmarking, etc., possible?
- Which key elements need to be taken into account in the planning and project management of complex applications, portals and matching engines? What schedules are realistic and what costs are to be expected? Do prototypical test applications make sense, can they simplify complex processes and make these more binding in terms of timing and costs?
Application example: ontology development and semantic matching for a public employment service in an EU country
On the one hand, the system previously used by a public employment service to place job seekers and to advertise corporate job vacancies in the public section of the platform no longer met the expectations of the market or of public authorities, while, on the other hand, the system environment was also reaching the end of its life cycle from a technical perspective. Originally configured more for expert use, the system was increasingly expanded and modified to take account of new needs and requirements, for example in the UI and web fields. Despite this, key characteristics and functions were lacking, in particular in the field of (semantic) skills and job matching as well as in terms of intelligent support and self-service functions and many others. The challenge was to bring data from extensive information systems together in such a way – data was previously gathered at different places – that this could be used effectively in future for matching purposes and intelligent support functions.
Improvements made possible by JANZZ.technology
Improved search options are now available for companies and job seekers, and offered services were extended and refined through semantic matching. A platform was created that can be used publicly, is consequently more user-friendly and intuitive and replaces the previous platform, which was used only as an internal tool. This was achieved through the combination of internal and external technologies and taxonomies. Public expenditure was reduced, and the ways of dealing with challenges relating to data protection and cross-border data exchange were improved.
Solution and methodology
- Close collaboration with the project managers allowed for a clear conceptualization of the process as regards the type of data structuring, the specification of the matching components, and the analysis of the available data and the process with a focus on areas of potential improvement.
- Configuration and implementation of JANZZsme!, with JANZZon! as a supporting feature, via JANZZrestAPI facilitated high-performance semantic matching.
- The country’s already existing taxonomy was integrated where necessary in JANZZon! in parts.
- A SaaS solution was set up and configured in order to match the available skills profiles of job seekers with the country’s vacancies (in each case amounting to currently over 500,000 datasets).
Accomplishment and results for the client
By combining JANZZsme! technology with the comprehensive background and contextual knowledge of JANZZon! concerning occupation data and the country’s already existing taxonomy, the efficiency and precision of the matches were also significantly improved in the self-service field. The quality of the results from comparing vacancies with the skills profiles of job seekers was clearly boosted. Further application possibilities are identified and functions developed on an ongoing basis.