Article Title
Knowledge graph construction with structure and parameter learning for indoor scene design
Keywords
knowledge graph, scene design, structure learning, parameter learning
Abstract
We consider the problem of learning a representation of both spatial relations and dependencies between objects for indoor scene design. We propose a novel knowledge graph framework based on the entity-relation model for representation of facts in indoor scene design, and further develop a weakly-supervised algorithm for extracting the knowledge graph representation from a small dataset using both structure and parameter learning. The proposed framework is flexible, transferable, and readable. We present a variety of computer-aided indoor scene design applications using this representation, to show the usefulness and robustness of the proposed framework.
Publisher
Tsinghua University Press
Recommended Citation
Yuan Liang, Fei Xu, Song-Hai Zhang et al. Knowledge graph construction with structure and parameter learning for indoor scene design. Computational Visual Media 2018, 4(2): 123-137.
Included in
Computational Engineering Commons, Computer-Aided Engineering and Design Commons, Graphics and Human Computer Interfaces Commons, Software Engineering Commons
