DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)· 2018· Preprint
AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale
- 45,742citations
- 2018year
Short summary
A new framework addresses the challenge of answering qualitative spatial questions by integrating a geoparser, a crisp reasoning system, and answer extraction.
AI-generated from the title and abstract; the full text is not read.
Key points
- Proposes a three-component framework for qualitative spatial question answering.
- Includes a geoparser, a crisp reasoning system, and answer extraction.
- Evaluated using synthetic questions for point-based cardinal direction calculus.
- Initial results indicate a high proportion of correct answers.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics), 2018 · DOI ↗
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Field: Artificial Intelligence
Artificial IntelligenceComputer Science