On multi-relational link prediction with bilinear models


Wang, Yanjie ; Gemulla, Rainer ; Li, Hui


[img]
Preview
PDF
On Multi-Relational Link Prediction with Bilinear Models.pdf - Published

Download (287kB)

URL: https://madoc.bib.uni-mannheim.de/44074
Additional URL: https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/v...
URN: urn:nbn:de:bsz:180-madoc-440741
Document Type: Conference or workshop publication
Year of publication: 2018
Book title: The Thirty-Second AAAI Conference on Artificial Intelligence, The Thirtieth Innovative Applications of Artificial Intelligence Conference, The Eighth AAAI Symposium on Educational Advances in Artificial Intelligence : New Orleans, Louisiana USA
Page range: 4227-4234
Conference title: AAI-18: Thirty-Second AAAI Conference on Artificial Intelligence
Location of the conference venue: New Orleans, LA
Date of the conference: February 2-7, 2018
Place of publication: Palo Alto, CA
Publishing house: AAAI Press
ISBN: 978-1-57735-800-8
ISSN: 2374-3468
Publication language: English
Institution: School of Business Informatics and Mathematics > Praktische Informatik I (Gemulla 2014-)
License: CC BY 4.0
Subject: 004 Computer science, internet
Keywords (English): Relational Learning ; Embedding Learning ; Knowledge Graph
Abstract: We study bilinear embedding models for the task of multi-relational link prediction and knowledge graph completion. Bilinear models belong to the most basic models for this task, they are comparably efficient to train and use, and they can provide good prediction performance. The main goal of this paper is to explore the expressiveness of and the connections between various bilinear models proposed in the literature. In particular, a substantial number of models can be represented as bilinear models with certain additional constraints enforced on the embeddings. We explore whether or not these constraints lead to universal models, which can in principle represent every set of relations, and whether or not there are subsumption relationships between various models. We report results of an independent experimental study that evaluates recent bilinear models in a common experimental setup. Finally, we provide evidence that relation-level ensembles of multiple bilinear models can achieve state-of-the-art prediction performance.

Dieser Eintrag ist Teil der Universitätsbibliographie.

Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt.




+ Citation Example and Export

Wang, Yanjie ; Gemulla, Rainer ORCID: 0000-0003-2762-0050 ; Li, Hui On multi-relational link prediction with bilinear models. Open Access 4227-4234 In: The Thirty-Second AAAI Conference on Artificial Intelligence, The Thirtieth Innovative Applications of Artificial Intelligence Conference, The Eighth AAAI Symposium on Educational Advances in Artificial Intelligence : New Orleans, Louisiana USA (2018) Palo Alto, CA AAI-18: Thirty-Second AAAI Conference on Artificial Intelligence (New Orleans, LA) [Conference or workshop publication]
[img]
Preview


+ Search Authors in

BASE: Wang, Yanjie ; Gemulla, Rainer ; Li, Hui

Google Scholar: Wang, Yanjie ; Gemulla, Rainer ; Li, Hui

ORCID: Wang, Yanjie ; Gemulla, Rainer ORCID: 0000-0003-2762-0050 ; Li, Hui

+ Download Statistics

Downloads per month over past year

View more statistics



You have found an error? Please let us know about your desired correction here: E-Mail


Actions (login required)

Show item Show item