Combining symbolic and sub-symbolic approaches – Improving neural Question-Answering-Systems through Document Analysis for enhanced accuracy and efficiency in Human-AI interaction

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.

Output

Paper(s) in a conference or/and journal

Demonstrator

Presentations

Project Partners:

• ATHINA, Haris Papageorgiou
• German Research Centre for Artificial Intelligence (DFKI), Georg Rehm

Primary Contact: Haris Papageorgiou, ATHENA RC/Institute for Language & Speech Processing