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sub:assertion {
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    dct:title "Fusing Domain-Specific Content from Large Language Models into Knowledge Graphs for Enhanced Zero Shot Object State Classification";
    <http://purl.org/spar/cito/describes> <https://neverblink.eu/ontologies/llm-kg/methods#KaradimisEtAl2024LLMKGFusedOSC>;
    <http://purl.org/spar/cito/discusses> <https://neverblink.eu/ontologies/llm-kg/methods#ALIGN>,
      <https://neverblink.eu/ontologies/llm-kg/methods#BLIP>, <https://neverblink.eu/ontologies/llm-kg/methods#CLIP>,
      <https://neverblink.eu/ontologies/llm-kg/methods#HuangEtAl2022MultimodalKGEmbedding>,
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  <https://neverblink.eu/ontologies/llm-kg/methods#KaradimisEtAl2024LLMKGFusedOSC> a
      <http://purl.org/spar/fabio/Workflow>;
    dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#SynergizedReasoning>;
    rdfs:comment "This method introduces a novel multi-stage pipeline for zero-shot object state classification. It synergistically integrates LLM-generated domain-specific knowledge, fuses it with general-purpose embeddings, and then leverages Knowledge Graphs and Graph Neural Networks to project these combined semantic embeddings into a visual space. This unified framework enables effective reasoning by combining LLM-derived knowledge and KG structure to solve a complex vision task.";
    rdfs:label "Karadimis Et Al 2024 LLM KG Fused Object State Classification";
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