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

Liang, Yuan, Xu, Fei, Zhang, Song-Hai, Lai, Yukun ORCID: https://orcid.org/0000-0002-2094-5680 and Mu, Taijiang 2018. Knowledge graph construction with structure and parameter learning for indoor scene design. Computational Visual Media 4 (2) , pp. 123-137. 10.1007/s41095-018-0110-3

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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 weaklysupervised 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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	Springer
ISSN:	2096-0433
Date of First Compliant Deposit:	28 March 2018
Date of Acceptance:	13 January 2018
Last Modified:	11 May 2023 09:12
URI:	https://orca.cardiff.ac.uk/id/eprint/110311

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