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• W3202859264 abstract "Noun phrases and Relation phrases in open knowledge graphs are not canonicalized, leading to an explosion of redundant and ambiguous subject-relation-object triples. Existing approaches to solve this problem take a two-step approach. First, they generate embedding representations for both noun and relation phrases, then a clustering algorithm is used to group them using the embeddings as features. In this work, we propose Canonicalizing Using Variational Autoencoders (CUVA), a joint model to learn both embeddings and cluster assignments in an end-to-end approach, which leads to a better vector representation for the noun and relation phrases. Our evaluation over multiple benchmarks shows that CUVA outperforms the existing state-of-the-art approaches. Moreover, we introduce CanonicNell, a novel dataset to evaluate entity canonicalization systems." @default.
• W3202859264 created "2021-10-11" @default.
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• W3202859264 date "2020-12-08" @default.
• W3202859264 modified "2023-09-27" @default.
• W3202859264 title "Open Knowledge Graphs Canonicalization using Variational Autoencoders." @default.
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