Knowledge-Based Spatial Reasoning for Scene Generation from Text Descriptions

Abstract

This system translates basic English descriptions of a wide range of objects in a simplistic zoo environment into plausible, three-dimensional, interactive visualizations of their positions, orientations, and dimensions. It combines a semantic network and contextually sensitive knowledge base as representations for explicit and implicit spatial knowledge, respectively. Its linguistic aspects address underspecification, vagueness, uncertainty, and context with respect to intrinsic, extrinsic, and deictic frames of spatial reference. The underlying, commonsense reasoning fomlalism is probability-based geometric fields that are solved through constraint satisfaction. The architecture serves as an extensible test-and-evaluation framework for a multitude of linguistic and artificial-intelligence investigations.