2020/06/17 by Tianwen Jiang, Jiang, Tianwen, Tong Zhao +9
Computer Science · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Topic Modeling #cs.AI #cs.CL
paper · pdf · doi:10.48550/arxiv.2006.09610
arxiv created 2020/06/17 · openalex publication_date 2020/06/17 · arxiv updated 2020/06/18 · openalex created_date 2020/06/25 · openalex updated_date 2026/07/28
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.