Evolving Taxonomy Based on Graph Neural Networks

Taxonomy is the human understanding and organizing of domain knowledge. How to automatically evolve taxonomy becomes critical in this knowledge explosion world. In this paper, we introduce a model to automatically updating taxonomy in a semi-supervised learning way. In our model, the goal is to train a graph neural network model, which can efficiently classify the edge between newly add term with and existing term to be the three types: true hyponym-hypernym relation, transductive hyponym-hypernym relation, and false hyponym-hypernym relation. We explore the ability of graph convolutional neural network, hyperbolic graph convolutional neural network and graph attention network to fulfill this task. We conduct an experiment on SemEval-2016 Task 13 data to test the quality of taxonomy obtained through evolution compared with the winning team's regeneration algorithms.