DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) / 2018

AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale

Holter, Ole Magnus, Ell, Basil

Computer VisionFoundation ModelsLarge Language Models

Although geospatial question answering systems have received increasing attention in recent years, existing prototype systems struggle to properly answer qualitative spatial questions. In this work, we propose a unique framework for answering qualitative spatial questions, which comprises three main components: a geoparser that takes the input questions and extracts place semantic information from text, a reasoning system which is embedded with a crisp reasoner, and finally, answer extraction, which refines the solution space and generates final answers. We present an experimental design to evaluate our framework for point-based cardinal direction calculus (CDC) relations by developing an automated approach for generating three types of synthetic qualitative spatial questions. The initial evaluations of generated answers in our system are promising because a high proportion of answers were labelled correct.

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### Paper attached as context
Title: AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale
Authors: Holter, Ole Magnus, Ell, Basil
Year: 2018
Venue: DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)
Categories: Computer Vision, Foundation Models, Large Language Models
Citations: 45,527
Paper URL: https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.LDK.2019.21
Open PDF: Not available

Abstract:
Although geospatial question answering systems have received increasing attention in recent years, existing prototype systems struggle to properly answer qualitative spatial questions. In this work, we propose a unique framework for answering qualitative spatial questions, which comprises three main components: a geoparser that takes the input questions and extracts place semantic information from text, a reasoning system which is embedded with a crisp reasoner, and finally, answer extraction, which refines the solution space and generates final answers. We present an experimental design to evaluate our framework for point-based cardinal direction calculus (CDC) relations by developing an automated approach for generating three types of synthetic qualitative spatial questions. The initial evaluations of generated answers in our system are promising because a high proportion of answers were labelled correct.

Learning resources:
- Google Scholar: Google Scholar references (https://scholar.google.com/scholar?q=AI-Assisted%20Pipeline%20for%20Dynamic%20Generation%20of%20Trustworthy%20Health%20Supplement%20Content%20at%20Scale)
- Papers with Code: Papers with Code search (https://paperswithcode.com/search?q=AI-Assisted%20Pipeline%20for%20Dynamic%20Generation%20of%20Trustworthy%20Health%20Supplement%20Content%20at%20Scale)
- YouTube: YouTube explanations (https://www.youtube.com/results?search_query=AI-Assisted%20Pipeline%20for%20Dynamic%20Generation%20of%20Trustworthy%20Health%20Supplement%20Content%20at%20Scale+paper+explained)