Combining symbolic and sub-symbolic approaches – Improving neural Question-Answering-Systems through Document Analysis for enhanced accuracy and efficiency in Human-AI interaction
ATHINA, German Research Centre for Artificial Intelligence (DFKI)
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
Results Description
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 focused on the task of question answering (QA) for the biomedical domain. ILSP developed a neurosymbolic QA engine addressing experts’ natural language questions by jointly applying neural snippet retrieval, biomedical Knowledge Graphs and our previous work on neural QA on a large collection of PUBMED articles, thus, facilitating medical experts in their work. DFKI further augmented this system by integrating the output of document analysis and segmentation modules. The final QA system supports exact answers, a diverse set of questions and more efficient Human-AI interactions. Moreover, a demonstrator was built upon scientific articles on Covid-19 and SARS-CoV-2.
Publications
Pappas Dimitris, Lyris Ioannis, Kountouris George and Papageorgiou Haris: "A Neurosymbolic Question Answering System Combining Structured and Unstructured Biomedical Knowledge", Proceedings of the 3rd Conference on AI for Humanity and Society (AI4HS), Stockholm, 2022
Links to Tangible results
Video: "Combining symbolic and sub-symbolic approaches – Improving neural Question-Answering-Systems through Document Analysis for enhanced accuracy and efficiency in Human-AI interaction"
Demonstrator: A neurosymbolic Question Answering System on Covid-19 and SARS-CoV-2
