EMERGE is a benchmark dataset of 233K Wikipedia passages paired with 1.45 million Wikidata edit operations across seven yearly snapshots from 2019 to 2025 for evaluating knowledge graph updates from emerging text.
KEPLER: A unified model for knowledge embedding and pre-trained language representation.Transactions of the Association for Computational Linguistics, 9:176–194
4 Pith papers cite this work. Polarity classification is still indexing.
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GS-Quant generates coarse-to-fine discrete codes for KG entities via semantic hierarchy injection and causal sequence reconstruction, enabling LLMs to perform knowledge graph completion by treating the codes as vocabulary tokens.
GA-S2S integrates T5 with RGAT to jointly process text and k-hop subgraph topology for knowledge graph link prediction, reporting up to 19% relative accuracy gain over seq2seq baselines on CoDEx.
Gyan is a novel explainable non-transformer language model that achieves SOTA results on multiple datasets by mimicking human-like compositional context and world models.
citing papers explorer
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GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion
GS-Quant generates coarse-to-fine discrete codes for KG entities via semantic hierarchy injection and causal sequence reconstruction, enabling LLMs to perform knowledge graph completion by treating the codes as vocabulary tokens.
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Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction
GA-S2S integrates T5 with RGAT to jointly process text and k-hop subgraph topology for knowledge graph link prediction, reporting up to 19% relative accuracy gain over seq2seq baselines on CoDEx.
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Gyan: An Explainable Neuro-Symbolic Language Model
Gyan is a novel explainable non-transformer language model that achieves SOTA results on multiple datasets by mimicking human-like compositional context and world models.