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This paper introduces and empirically evaluates methods for measuring semantic similarity between knowledge graphs using KG embeddings, proposing EmbPairSim and AvgEmbSim scoring functions that outperform baselines like Sentence-BERT on WikiText-2 and CC-News datasets.
This paper introduces CORE, a new knowledge graph completion model that uses cyclic orthotope relation embeddings on a torus manifold to address boundary constraints in region-based models. Experiments show competitive performance in link prediction tasks.