Deep Sparse Latent Feature Models for Knowledge Graph Completion

• URL: http://arxiv.org/abs/2411.15694v1
• Date: Sun, 24 Nov 2024 03:17:37 GMT
• Title: Deep Sparse Latent Feature Models for Knowledge Graph Completion
• Authors: Haotian Li, Rui Zhang, Lingzhi Wang, Bin Yu, Youwei Wang, Yuliang Wei, Kai Wang, Richard Yi Da Xu, Bailing Wang,
• Abstract summary: In this paper, we introduce a novel framework of sparse latent feature models for knowledge graphs. Our approach not only effectively completes missing triples but also provides clear interpretability of the latent structures. Our method significantly improves performance by revealing latent communities and producing interpretable representations.
• Score: 24.342670268545085
• License:
• 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.

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