Variational Knowledge Graph Reasoning
Wenhu Chen, Wenhan Xiong, Xifeng Yan, William Wang

TL;DR
This paper introduces extsc{Diva}, a variational inference framework for knowledge graph reasoning that models latent paths connecting entities to improve link prediction accuracy.
Contribution
The paper proposes a novel variational inference approach with a unified architecture for KG reasoning, handling noise and complex scenarios more effectively.
Findings
Achieved state-of-the-art results on multiple datasets.
Effectively models latent paths for relation prediction.
Handles noise and complex reasoning scenarios.
Abstract
Inferring missing links in knowledge graphs (KG) has attracted a lot of attention from the research community. In this paper, we tackle a practical query answering task involving predicting the relation of a given entity pair. We frame this prediction problem as an inference problem in a probabilistic graphical model and aim at resolving it from a variational inference perspective. In order to model the relation between the query entity pair, we assume that there exists an underlying latent variable (paths connecting two nodes) in the KG, which carries the equivalent semantics of their relations. However, due to the intractability of connections in large KGs, we propose to use variation inference to maximize the evidence lower bound. More specifically, our framework (\textsc{Diva}) is composed of three modules, i.e. a posterior approximator, a prior (path finder), and a likelihood (path…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Topic Modeling · Bayesian Modeling and Causal Inference
