“We transfer the latest research and development advances in data retrieval, analysis, annotation and linking into the media and content industries.”

Company Description

MODUL Technology GmbH is your partner in innovative solutions for media and data collection, annotation and linking, integrating extracted and external knowledge into existing workflows and powering new and extended user interfaces and applications such as: news event or topic detection, social media retrieval and analysis, multimedia linking with related information and content, predictive analytics for future events and topics.

As a research and innovation spin-off of MODUL University Vienna, the Technology GmbH enables university faculty (as the technology experts) as well as hired researchers and developers to work on near-to-market R&D with a close collaboration with the University itself and support existing and future University spin-offs. It was founded in 2015 as part of the exploitation plans of the LinkedTV project. It has access to technology experts from throughout the Research Centre of New Media Technology, which together with its commercial technology partner webLyzard technology conducts cross-disciplinary research on knowledge acquisition, semantic Web annotation, human-computer interaction, data analytics, natural language processing and multimedia description and linking. In Horizon 2020, we developed solutions for news story detection and story-based video retrieval in the InVID project, as well as event-based knowledge extraction and trending topic prediction in the ReTV project.

We continue to innovate in areas such as deep learning and predictive analytics in national projects such as EPOCH and GENTIO. We plan to build on this successful track record and evolve our technology stack further as part of the upcoming Horizon Europe program.

Automated news story detection and related online video retrieval, jointly developed by MODUL Technology and webLyzard as part of the InVID Horizon 2020 project

Automated event extraction from online sources powers a customized calendar of future events in the ReTV Horizon 2020 project’s Content Wizard tool. 

Predictive analytics derived from large scale Web data analysis is used to predict when selected topics will peak in popularity online, e.g. “eurovision” on May 22 (the final) or “eclipse” on May 26 (date of a total lunar eclipse).