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2020 | OriginalPaper | Buchkapitel

Multilingual Knowledge Graph Embeddings with Neural Networks

verfasst von : Qiannan Zhu, Xiaofei Zhou, Yuwen Wu, Ping Liu, Li Guo

Erschienen in: Data Science

Verlag: Springer Singapore

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Abstract

Multilingual knowledge graphs constructed by cross-lingual knowledge alignment have attracted increasing attentions in knowledge-driven cross-lingual research fields. Although many existing knowledge alignment methods such as MTransE based on linear transformations perform well on cross-lingual knowledge alignment, we note that neural networks with stronger nonlinear capacity of capturing alignment features. This paper proposes a knowledge alignment neural network named KANN for multilingual knowledge graphs. KANN combines a monolingual neural network for encoding the knowledge graph of each language into a separated embedding space, and a alignment neural network for providing transitions between cross-lingual embedding spaces. We empirically evaluate our KANN model on cross-lingual entity alignment task. Experimental results show that our method achieves significant and consistent performance, and outperforms the current state-of-the-art models.

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Vorheriges Kapitel The List 2-Distance Coloring of Sparse Graphs

Nächstes Kapitel Sparse Optimization Based on Non-convex Regularization for Deep Neural Networks

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Titel: Multilingual Knowledge Graph Embeddings with Neural Networks
verfasst von: Qiannan Zhu
Xiaofei Zhou
Yuwen Wu
Ping Liu
Li Guo
Verlag: Springer Singapore
Buch: Data Science
Print ISBN: 978-981-15-2809-5

Electronic ISBN: 978-981-15-2810-1

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-981-15-2810-1_15

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