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Research Article | Open Access

Knowledge graph construction with structure and parameter learning for indoor scene design

TNList, Department of Computer Science, Tsinghua University, Beijing 100084, China.
School of Computer Science and Informatics, Cardiff University, Cardiff, CF24 3AA, UK.
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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.

Keywords

knowledge graphscene designstructure learningparameter learning

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Computational Visual Media
Volume 4 Issue 2,
June 2018
Pages 123-137
DOI: 10.1007/s41095-018-0110-3

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Cite this article:
Liang Y, Xu F, Zhang S-H, et al. Knowledge graph construction with structure and parameter learning for indoor scene design. Computational Visual Media, 2018, 4(2): 123-137. https://doi.org/10.1007/s41095-018-0110-3

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Revised: 08 January 2018
Accepted: 13 January 2018
Published: 21 March 2018
© The Author(s) 2018

This article is published with open access at Springerlink.com

The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www. editorialmanager.com/cvmj.