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Found 3 papers, 1 papers with code
On the Aggregation of Rules for Knowledge Graph Completion
Rule learning approaches for knowledge graph completion are efficient, interpretable and competitive to purely neural models.
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.
LibKGE - A knowledge graph embedding library for reproducible research
LibKGE ( https://github. com/uma-pi1/kge ) is an open-source PyTorch-based library for training, hyperparameter optimization, and evaluation of knowledge graph embedding models for link prediction.
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