Knowledge discovery offers numerous challenges and opportunities. In the last decade, a significant number of applications have emerged, relying on evidence from the scientific literature. ΑΙ methods provide innovative ways of applying knowledge discovery methods in the scientific literature facilitating automated reasoning, discovery, and decision making on data.

This micro-project focused on the task of question answering (QA) for the biomedical domain. Our starting point was a neural QA engine developed by ILSP addressing experts’ natural language questions by jointly applying document retrieval and snippet extraction on a large collection of PUBMED articles, thus, facilitating medical experts in their work. DFKI augmented this system with a knowledge graph integrating the output of document analysis and segmentation modules. The knowledge graph was incorporated in the QA system and used for exact answers and more efficient Human-AI interactions. We primarily focused on scientific articles on Covid-19 and SARS-CoV-2.