Nanopublications

@prefix this: <https://w3id.org/np/RArAqaMF4WuSw6nrwJuRr5Y8m7zJoczmtBfSUtLUlg40w> .
@prefix sub: <https://w3id.org/np/RArAqaMF4WuSw6nrwJuRr5Y8m7zJoczmtBfSUtLUlg40w/> .
@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:hasProvenance sub:provenance ;
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sub:assertion {
  <https://doi.org/10.48550/arXiv.2509.03540> dct:title "Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction" ;
    <http://purl.org/spar/cito/describes> <https://neverblink.eu/ontologies/llm-kg/methods#InferenceTimeKnowledgeGraphConstruction> ;
    <http://purl.org/spar/cito/discusses> <https://neverblink.eu/ontologies/llm-kg/methods#ChainOfThoughtPrompting> , <https://neverblink.eu/ontologies/llm-kg/methods#GenerateOnGraph> , <https://neverblink.eu/ontologies/llm-kg/methods#GraphOfThoughts> , <https://neverblink.eu/ontologies/llm-kg/methods#TextBasedRAGBaseline> , <https://neverblink.eu/ontologies/llm-kg/methods#ThinkOnGraph> ;
    a prov:Entity .
  <https://neverblink.eu/ontologies/llm-kg/methods#ChainOfThoughtPrompting> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "Chain-of-Thought Prompting" .
  <https://neverblink.eu/ontologies/llm-kg/methods#GenerateOnGraph> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "Generate-on-Graph" .
  <https://neverblink.eu/ontologies/llm-kg/methods#GraphOfThoughts> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "Graph-of-Thoughts" .
  <https://neverblink.eu/ontologies/llm-kg/methods#InferenceTimeKnowledgeGraphConstruction> dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMInference> ;
    a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:comment "This method proposes a novel four-stage pipeline that dynamically constructs and expands knowledge graphs (KGs) during the LLM's inference process. The KG is built by extracting initial triplets from the question, iteratively expanding them using the LLM's internal knowledge, and refining them through external retrieval from sources like Wikipedia and Google Search. The ultimate goal is to improve the factual accuracy and robustness of LLM-generated answers in Question Answering tasks." ;
    rdfs:label "Inference-Time Knowledge Graph Construction" ;
    <https://neverblink.eu/ontologies/llm-kg/hasTopCategory> <https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM> .
  <https://neverblink.eu/ontologies/llm-kg/methods#TextBasedRAGBaseline> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "Text-based RAG baseline" .
  <https://neverblink.eu/ontologies/llm-kg/methods#ThinkOnGraph> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "Think-on-Graph" .
}

sub:provenance {
sub:assertion prov:wasAttributedTo <https://neverblink.eu/ontologies/llm-kg/agent> ;
prov:wasDerivedFrom <https://doi.org/10.48550/arXiv.2509.03540> .
}

sub:pubinfo {
  this: dct:created "2026-02-26T15:42:46.361Z"^^xsd:dateTime ;
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    npx:hasNanopubType <https://neverblink.eu/ontologies/llm-kg/PaperAssessmentResult> ;
    rdfs:label "LLM-KG assessment for paper 10.48550/arXiv.2509.03540" .
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