We are pleased to showcase both during a demo presentation and at our booth the latest on machine learning, matching and semantics! See more on : https://www.hrinnov-techday.be/HRITD2018nl
Sometimes, an easy and simple overview will tell you more than extended white papers..... We all know the theory behind semantic technology - we all know that semantics really understand what you are looking for and in what context.... But what does this really mean? Although many semantic search engines claim to understand what
Actonomy will be exhibiting at the Recruitment Agency Expo in London and we will welcome you at our booth D7. The latest development on machine learning and deep learning and how this will impact the HR business will be shown. Please subscribe to our newsletter to get more details on what will be shown
Semantics is more than synonyms and certainly more than a Boolean search on the different parts of a ‘concept’!
Many solutions are called semantic search engines when synonyms are used while searching. It is clear that systems should search for 'software developer' and 'programmer' and many more .... but that is only one aspect of semantic search! Software providers tend to confuse the market with terminology that doesn't really match with what the
Actonomy's flagship product xMP Semantic Mind - the fully integrated platform for text extraction, classification, semantic search and match - is now available as the latest release V5.5.1 offering several extensions : Major content updates and extensions of the core ontology/knowledge base with several new job titles and skills across the different languages. Advanced
Actonomy is proud to be sponsor again of the NORA Online Awards in 2017. Over the last 10 years we have been on stage announcing the Best Generalist Jobboard Award! 10 years of commitment to the industry and to the players in the market! See more on : https://norauk.com
Asimov was a large scale research project that was done by Actonomy, Crosslang, De Persgroep in collaboration with iMEC (IDlab) and KUL on how machine learning could be used to generate automatically ontologies in a multilingual environment. In addition it also studied the usage of user behavior to enhance the matching results. This has now