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URL:
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https://madoc.bib.uni-mannheim.de/44074
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Additional URL:
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https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/v...
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URN:
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urn:nbn:de:bsz:180-madoc-440741
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Document Type:
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Conference or workshop publication
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Year of publication:
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2018
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Book title:
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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
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Page range:
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4227-4234
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Conference title:
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AAI-18: Thirty-Second AAAI Conference on Artificial Intelligence
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Location of the conference venue:
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New Orleans, LA
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Date of the conference:
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February 2-7, 2018
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Place of publication:
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Palo Alto, CA
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Publishing house:
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AAAI Press
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ISBN:
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978-1-57735-800-8
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ISSN:
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2374-3468
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Publication language:
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English
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Institution:
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School of Business Informatics and Mathematics > Praktische Informatik I (Gemulla 2014-)
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License:
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Subject:
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004 Computer science, internet
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Keywords (English):
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Relational Learning ; Embedding Learning ; Knowledge Graph
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Abstract:
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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.
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ORCID: 0000-0003-2762-0050 ; Li, Hui
On multi-relational link prediction with bilinear models.
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]
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