graph-embedding

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.

Introduces Adaptive-masking for Graph Embedding (AGE), a Transformer-based self-supervised learning method that addresses latent feature misalignment between graph and text representations for LLMs in GraphRAG tasks by focusing on predicting non-key nodes.

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.