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

• URL: http://arxiv.org/abs/2006.09610v1
• Date: Wed, 17 Jun 2020 02:32:36 GMT
• Title: Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network
• Authors: Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang
• Abstract summary: 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, which sentence) and semantic information (semantic similarity) to do the canonicalization. We propose a graph neural network model to aggregate representations of noun phrases and relational phrases through the multi-layered metagraph structure.
• Score: 43.48148444558244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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