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Found 5 papers, 1 papers with code
Disentangling Exploration of Large Language Models by Optimal Exploitation
Exploration is a crucial skill for self-improvement and open-ended problem-solving.
A*Net and NBFNet Learn Negative Patterns on Knowledge Graphs
In this technical report, we investigate the predictive performance differences of a rule-based approach and the GNN architectures NBFNet and A*Net with respect to knowledge graph completion.
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.
