Knowledge discovery offer numerous challenges and opportunities. In the last decade, a significant number of applications have emerged relying on evidence from the scientific literature. ΑΙ methods offer innovative ways of applying knowledge discovery methods in the scientific literature facilitating automated reasoning, discovery and decision making on data.
This micro-project will focus on the task of question answering (QA) for the biomedical domain. Our starting point is 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 will augment this system with a knowledge graph integrating the output of document analysis and segmentation modules. The knowledge graph will be incorporated in the QA system and used for exact answers and more efficient Human-AI interactions. We will primarily focus upon scientific articles on Covid-19 and SARS-CoV-2.
Paper(s) in a conference or/and journal
- ATHENA RC/Institute for Language & Speech Processing, Haris Papageorgiou
- ILSP/ATHENA RC, Haris Papageorgiou
- DFKI, Georg Rehm
Primary Contact: Haris Papageorgiou, ATHENA RC/Institute for Language & Speech Processing