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

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.