Joint Entity and Relation Canonicalization in Open Knowledge Graphs using Variational Autoencoders

• URL: http://arxiv.org/abs/2012.04780v1
• Date: Tue, 8 Dec 2020 22:58:30 GMT
• Title: Joint Entity and Relation Canonicalization in Open Knowledge Graphs using Variational Autoencoders
• Authors: Sarthak Dash, Gaetano Rossiello, Nandana Mihindukulasooriya, Sugato Bagchi, Alfio Gliozzo
• Abstract summary: 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 face 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.
• Score: 11.259587284318835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 face 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.

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