Open Knowledge Graphs Canonicalization using Variational Autoencoders
Sarthak Dash, Gaetano Rossiello, Nandana Mihindukulasooriya, Sugato Bagchi, Alfio Gliozzo · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
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) 1 , 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.