@article{Liang2018, 
author = {Yuan Liang and Fei Xu and Song-Hai Zhang and Yu-Kun Lai and Taijiang Mu},
title = {Knowledge graph construction with structure and parameter learning for indoor scene design},
year = {2018},
journal = {Computational Visual Media},
volume = {4},
number = {2},
pages = {123-137},
keywords = {knowledge graph, scene design, structure learning, parameter learning},
url = {https://www.sciopen.com/article/10.1007/s41095-018-0110-3},
doi = {10.1007/s41095-018-0110-3},
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.}
}