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Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment

A translation-based model for multilingual knowledge graph embeddings.

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Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment

By Muhao Chen, Yingtao Tian, Mohan Yang, Carlo Zaniolo
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The paper proposes MTransE, a model that provides transitions for each embedding vector to its cross-lingual counterparts in other spaces. It can be trained on partially aligned graphs and preserves the key properties of monolingual embeddings.

The experiments show promising results on cross-lingual entity matching and triple-wise alignment verification.

Abstract

The paper proposes MTransE, a model that provides transitions for each embedding vector to its cross-lingual counterparts in other spaces. It can be trained on partially aligned graphs and preserves the key properties of monolingual embeddings. The experiments show promising results on cross-lingual entity matching and triple-wise alignment verification.

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multilingual knowledge graph embeddingscross-lingual knowledge alignmenttranslation-based modelentity matchingtriple-wise alignment verificationKnowledge GraphsSemantic InteroperabilityOntology & TaxonomyContent Engineering
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