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Noun phrases and relational phrases in Open Knowledge Bases are often notcanonical, leading to redundant and ambiguous facts. In this work, we integratestructural information (from which tuple, which sentence) and semanticinformation (semantic similarity) to do the canonicalization. We represent thetwo types of information as a multi-layered graph: the structural informationforms the links across the sentence, relational phrase, and noun phrase layers;the semantic information forms weighted intra-layer links for each layer. Wepropose a graph neural network model to aggregate the representations of nounphrases and relational phrases through the multi-layered meta-graph structure.Experiments show that our model outperforms existing approaches on a publicdatasets in general domain.

Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network