[TMP-027] Combining symbolic and sub-symbolic approaches – Improving neural Question- Answering-Systems through Document Analysis for enhanced accuracy and efficiency in Human-AI interaction

Contact person: Haris Papageorgiou (Athena RC) (haris@athenarc.gr) 

Internal Partners:

1. ATHENA RC,ILSP , Haris Papageorgiou, haris@athenarc.gr
2. DFKI, Georg Rehm, georg.rehm@dfki.de

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 offer innovative ways of applying knowledge discovery methods in the scientific literature facilitating automated reasoning, discovery and decision making on data. This micro-project focuses 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 primarily focus upon scientific articles on Covid-19 and SARS-CoV-2.

Tangible Outcomes

1. Video presentation summarizing the project