Abstract
We investigate distantly supervised relation extraction with knowledge-guided latent graphs and an iterative graph learner. For the relation extraction tasks, we assume that the input sentences contain latent graphs with useful structural information between mentions and relations of the distantly supervised data. We first embed input sentences with default initialized graphs to utilize this information. We also used the pre-trained Knowledge Bases (KB) to guide the latent space of a Variational Graph Auto-Encoders (VGAE) module. Then the VGAE applies the re-parameterization mechanism to reconstruct the initial graph, injecting extra knowledge into the initial latent graph and reducing its unsteadiness. Subsequently, we optimize the latent graph structures and their corresponding node embeddings simultaneously by the iterative graph learner, and the better latent graphs can improve the downstream relation extraction task. Experiment results on three datasets (NYT10, WIKIDISTANT and GIDS) show that latent graph learning helps our model perform better than previous works.
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