Since the foundation of our organization we’ve been working in close collaboration with companies from numerous technological areas, that share and support our vision. The clear target of this cooperation is to enable the exchange of expertise and know-how, thus creating success-oriented synergies for all participants.
The main objectives of our partnership with WCC are to increase collaboration on large-scale and complex projects, to combine knowledge and experience, and to incorporate both companies’ comprehensive know-how regarding challenging, global, multilingual projects in the public employment services (PES), staffing, corporate HR and other relevant job matching areas. For example, the comprehensive ontology JANZZon!, which already contains around 20 million terms from the occupation data area, is set to support and augment WCC’s proven and successful ELISE software platform, allowing new multilingual functionalities with even greater accuracy in search and match. The many renowned clients and global organizations of WCC and JANZZ.technology will benefit from it in the future, extending their advantage still further in the areas of automatic classification, multilingual and semantic ontologies, complex and tailored matching algorithms and the processing of big occupation data.
WCC Smart Search & Match is one of the world’s leading suppliers of search and match software solutions and services. WCC focuses on two specific solution areas: employment matching and identity matching. Its ELISE software platform excels in these areas because it uses a unique way of searching and matching data, providing more meaningful results. ELISE is designed to search through vast amounts of data from various sources and give meaningful results in a matter of seconds. It will search and match data in almost any form, be it exact or inexact, structured or unstructured, private or public, and combine multiple modalities (biographic or biometric). WCC’s primary customers are large government organizations and large companies worldwide. The company is headquartered in Utrecht, the Netherlands and has offices in the USA and the Middle East.
We are an IBM Business Partner because we share a vision with IBM of bringing a new level of intelligence to how the world works – how every person, business, organization, government, natural and man-made system interacts. We are committed to do things better, more efficiently, and more productively. As systems become smarter, meaningful new possibilities for progress are created, along with unprecedented opportunities for teaming and collaboration.
IBM is one of the global market leaders in the field of hardware, software and IT services as well as one of the biggest consulting companies. Since its inception over 100 years ago, it has constantly evolved. The commitment to innovation is part of IBM’s core strategy. Over the past decade, it has steadily shifted its business mix by exiting commoditizing markets such as PCs, hard disk drives and DRAMs and focusing on markets such as business intelligence, data analytics, business continuity, security, cloud computing and virtualization.
From 2013 until August 2015, JANZZ.technology worked in a consortium with two partners – Holmes Semantic Solutions (Holmes, or Ho2S) in Grenoble and the University of Oslo – on the SAUGE (Semantic Analysis for Unrestricted Generalized Employment) research project.
The project aimed to further develop the technologies available for extracting every detail from, for example, manually written CVs in PDF format, and converting them to properly structured information. Accessing or extracting structured information from PDF files is an extremely inefficient process, because conventional data transformation is not generally possible here. Based on analyses of non-standardized (unrestricted) CVs (parsing), it has been observed that data transformation is the critical step in the process, because it is based on the linking (hybridization) of those technologies already used in the analysis of symbolic and statistical data. By improving and refining the matching process in this way, it will be possible to interpret searches for requirements entered in natural language, which, due to the restrictions posed by online forms and questionnaires, was previously possible either only to a limited extent, or not at all. In this way, the amount of time and infrastructure currently required for processing applications and converting them into a meaningful pool of data suitable for further processing, for example for use in matching, can be drastically reduced.