Low-Resource Learning

Text2Tech

The goal of the Text2Tech project is the research and development of automated methods for information extraction from unstructured text sources in order to be able to provide companies with decision-relevant knowledge about technological developments quickly and efficiently. AI-based methods for information extraction (IE) already make it possible to extract selected information, e.g. B. to people, companies and places automatically from text sources. In the Text2Tech project, such approaches are to be further developed in order to extract machine-readable knowledge about technologies, technology categories, companies and their relationships with each other from German and English-language, domain-specific text sources, using the example of the automotive industry. The most important research goals are the modeling and filling of domain-specific knowledge graphs (Knowledge Base Population), the development of methods for cross-lingual proper name recognition and linking (Named Entity Recognition or Entity Linking), relation extraction (Relation Extraction), as well as the development of Model compression methods so that models run efficiently even on small hardware.

PLASS

The aim of the PLASS project is to develop a prototypical B2B platform for AI-based decision support for supply chain management. The focus is on the automatic recognition of decision-relevant information and the acquisition of structured knowledge from global and multilingual text sources. These sources provide a large database for SCM information, especially for the early detection of critical events and risks, but also of opportunities, e.g. through new technologies, at suppliers and supply chains. PLASS enables SMEs and large companies to continuously monitor their suppliers and supply chains, and supports supply chain managers in risk assessment and decision-making.