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Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network
Noun phrases and relational phrases in Open Knowledge Bases are often not
canonical, leading to redundant and ambiguous facts. In this work, we integrate
structural information (from which tuple, which sentence) and semantic
information (semantic similarity) to do the canonicalization. We represent the
two types of information as a multi-layered graph: the structural information
forms the links across the sentence, relational phrase, and noun phrase layers;
the semantic information forms weighted intra-layer links for each layer. We
propose a graph neural network model to aggregate the representations of noun
phrases and relational phrases through the multi-layered meta-graph structure.
Experiments show that our model outperforms existing approaches on a public
datasets in general domain