Deep Sparse Latent Feature Models for Knowledge Graph Completion
Keywords: Knowledge Graph Completion (KGC), Stochastic Blockmodels (SBMs), Variational Autoencoder (VAE)
TL;DR: A probabilistic generative model based on SBMs enhances KGC performance and interpretability.
Abstract: Recent progress in knowledge graph completion (KGC) has focused on text-based approaches to address the challenges of large-scale knowledge graphs (KGs). Despite their achievements, these methods often overlook the intricate interconnections between entities, a key aspect of the underlying topological structure of a KG. Stochastic blockmodels (SBMs), particularly the latent feature relational model (LFRM), offer robust probabilistic frameworks that can dynamically capture latent community structures and enhance link prediction. In this paper, we introduce a novel framework of sparse latent feature models for KGC, optimized through a deep variational autoencoder (VAE). Our approach not only effectively completes missing triples but also provides clear interpretability of the latent structures, leveraging textual information. Comprehensive experiments on the WN18RR, FB15k-237, and Wikidata5M datasets show that our method significantly improves performance by revealing latent communities and producing interpretable representations.
Supplementary Material: zip
Primary Area: learning on graphs and other geometries & topologies
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Submission Number: 10362
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