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sub:assertion {
  <https://doi.org/10.48550/arXiv.2405.15374> dct:title "Leveraging Large Language Models for Semantic Query Processing in a Scholarly Knowledge Graph";
    <http://purl.org/spar/cito/describes> <https://neverblink.eu/ontologies/llm-kg/methods#DeepDocumentModel>,
      <https://neverblink.eu/ontologies/llm-kg/methods#KGenhancedQueryProcessing>;
    <http://purl.org/spar/cito/discusses> <https://neverblink.eu/ontologies/llm-kg/methods#DistilBERTModel>,
      <https://neverblink.eu/ontologies/llm-kg/methods#MEL>, <https://neverblink.eu/ontologies/llm-kg/methods#PARSE>,
      <https://neverblink.eu/ontologies/llm-kg/methods#SimpleChunking>, <https://neverblink.eu/ontologies/llm-kg/methods#TNNT>;
    a prov:Entity .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#DeepDocumentModel> dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#LLMAugmentedKGConstruction>;
    a <http://purl.org/spar/fabio/Workflow>;
    rdfs:comment "The Deep Document Model (DDM) is proposed to address limitations in traditional KG construction by providing a comprehensive and fine-grained structured representation of academic papers. It uses advanced NLP techniques (e.g., text segmentation, named entity recognition) to convert unstructured text into a hierarchical knowledge representation, enriching the Scholarly Knowledge Graph (ASKG). This enhanced KG representation is explicitly designed to facilitate more sophisticated interactions with Large Language Models.";
    rdfs:label "Deep Document Model";
    <https://neverblink.eu/ontologies/llm-kg/hasTopCategory> <https://neverblink.eu/ontologies/llm-kg/top-categories#LLMAugmentedKG> .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#DistilBERTModel> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "Distil-BERT model" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#KGenhancedQueryProcessing> dct:subject
      <https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMInference>;
    a <http://purl.org/spar/fabio/Workflow>;
    rdfs:comment "The KG-enhanced Query Processing (KGQP) workflow is designed to mitigate \"AI hallucination\" in LLMs and optimize complex queries by providing KG-based context during question-answering interactions. It involves LLMs transforming user queries into triples, performing SPARQL queries for matching against the KG, and then leveraging LLMs for final triple selection and results ranking. This directly improves the LLM's accuracy and reliability during its inference stage by grounding responses with structured KG knowledge.";
    rdfs:label "KG-enhanced Query Processing";
    <https://neverblink.eu/ontologies/llm-kg/hasTopCategory> <https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM> .
  
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    rdfs:label "PARSE" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#SimpleChunking> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "Simple Chunking" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#TNNT> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "TNNT (The NLP-NER Toolkit)" .
}

sub:provenance {
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    prov:wasDerivedFrom <https://doi.org/10.48550/arXiv.2405.15374> .
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