Why a Naive Way to Combine Symbolic and Latent Knowledge Base Completion Works Surprisingly Well
We compare a rule-based approach for knowledge graph completion against current state-of-the-art, which is based on embbedings. Instead of focusing on aggregated metrics, we look at several examples that illustrate essential differences between symbolic and latent approaches. Based on our insights, we construct a simple method to combine the outcome of rule-based and latent approaches in a post-processing step. Our method improves the results constantly for each model and dataset used in our experiments.
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