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sub:assertion {
  <https://doi.org/10.48550/arXiv.2501.11911> dct:title "Integrate Temporal Graph Learning into LLM-based Temporal Knowledge Graph Model";
    <http://purl.org/spar/cito/describes> <https://neverblink.eu/ontologies/llm-kg/methods#TglLlm>;
    <http://purl.org/spar/cito/discusses> <https://neverblink.eu/ontologies/llm-kg/methods#Coh>,
      <https://neverblink.eu/ontologies/llm-kg/methods#ConvTransE>, <https://neverblink.eu/ontologies/llm-kg/methods#DistMult>,
      <https://neverblink.eu/ontologies/llm-kg/methods#GenTkg>, <https://neverblink.eu/ontologies/llm-kg/methods#HisMatch>,
      <https://neverblink.eu/ontologies/llm-kg/methods#Kopa>, <https://neverblink.eu/ontologies/llm-kg/methods#Regcn>,
      <https://neverblink.eu/ontologies/llm-kg/methods#Renet>, <https://neverblink.eu/ontologies/llm-kg/methods#Rgcn>;
    a prov:Entity .
  
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    rdfs:label "ConvTransE" .
  
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    rdfs:label "DistMult" .
  
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    rdfs:label "GenTKG" .
  
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    rdfs:label "HisMatch" .
  
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    rdfs:label "RGCN" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#TglLlm> dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#SynergizedKnowledgeRepresentation>;
    a <http://purl.org/spar/fabio/Workflow>;
    rdfs:comment "TGL-LLM is a novel framework that integrates temporal graph learning into LLM-based temporal knowledge graph models. It achieves this through hybrid graph tokenization (using Temporal Graph Adapters and Hybrid Prompt Design) and a two-stage training paradigm (Data Pruning and Prompt Tuning). These components are designed to create a unified representation of knowledge from both temporal graphs and LLMs, addressing limitations in temporal pattern modeling and cross-modal alignment.";
    rdfs:label "TGL-LLM";
    <https://neverblink.eu/ontologies/llm-kg/hasTopCategory> <https://neverblink.eu/ontologies/llm-kg/top-categories#SynergizedLLMKG> .
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