Nanopublication

ID

https://w3id.org/np/RAHz0CL7yQQCaSZ-24RscK6SrWaWliXoGrNzlLuocZ9TQ
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Content

@prefix this: <https://w3id.org/np/RAHz0CL7yQQCaSZ-24RscK6SrWaWliXoGrNzlLuocZ9TQ> .
@prefix sub: <https://w3id.org/np/RAHz0CL7yQQCaSZ-24RscK6SrWaWliXoGrNzlLuocZ9TQ/> .
@prefix np: <http://www.nanopub.org/nschema#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix npx: <http://purl.org/nanopub/x/> .

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    np:hasPublicationInfo sub:pubinfo .
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sub:assertion {
  <https://doi.org/10.48550/arXiv.2501.11911> a prov:Entity;
    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> .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#Coh> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "CoH" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#ConvTransE> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "ConvTransE" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#DistMult> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "DistMult" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#GenTkg> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "GenTKG" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#HisMatch> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "HisMatch" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#Kopa> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "KoPA" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#Regcn> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "REGCN" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#Renet> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "RENET" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#Rgcn> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "RGCN" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#TglLlm> a <http://purl.org/spar/fabio/Workflow>;
    dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#SynergizedKnowledgeRepresentation>;
    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> .
}

sub:provenance {
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    prov:wasDerivedFrom <https://doi.org/10.48550/arXiv.2501.11911> .
}

sub:pubinfo {
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