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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion

verfasst von : Prodromos Kolyvakis, Alexandros Kalousis, Dimitris Kiritsis

Erschienen in: The Semantic Web

Verlag: Springer International Publishing

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Abstract

Learning embeddings of entities and relations existing in knowledge bases allows the discovery of hidden patterns in them. In this work, we examine the contribution of geometrical space to the task of knowledge base completion. We focus on the family of translational models, whose performance has been lagging. We extend these models to the hyperbolic space so as to better reflect the topological properties of knowledge bases. We investigate the type of regularities that our model, dubbed HyperKG, can capture and show that it is a prominent candidate for effectively representing a subset of Datalog rules. We empirically show, using a variety of link prediction datasets, that hyperbolic space allows to narrow down significantly the performance gap between translational and bilinear models and effectively represent certain types of rules.

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Vorheriges Kapitel SchemaTree: Maximum-Likelihood Property Recommendation for Wikidata
Nächstes Kapitel Unsupervised Bootstrapping of Active Learning for Entity Resolution
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Fußnoten
1
The matrix, used in Möbius multiplication, and the biases are defined on Euclidean space and are learned through Euclidean SGD.
 
2
Only existential variables can be mapped to labelled nulls.
 
3
In our experiments, we noticed that a rather small dropout rate had no effect on the model’s generalisation capability.
 
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Metadaten
Titel
Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion
verfasst von
Prodromos Kolyvakis
Alexandros Kalousis
Dimitris Kiritsis
Copyright-Jahr
2020
Buch
The Semantic Web

Print ISBN: 978-3-030-49460-5

Electronic ISBN: 978-3-030-49461-2

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-49461-2_12