PIE creates predicate-aware embeddings by weighting subjectless triples and DRSD distills LLM reasoning into an SLM while decoupling confidence from rationales to improve entity alignment and enable human-in-the-loop verification.
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
2 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.
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Predicate Importance Estimation and Decoupled Rationale-Score Distillation for Entity Alignment
PIE creates predicate-aware embeddings by weighting subjectless triples and DRSD distills LLM reasoning into an SLM while decoupling confidence from rationales to improve entity alignment and enable human-in-the-loop verification.
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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.