Nanopublications

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@prefix sub: <https://w3id.org/np/RArK6wV1lccoEjvnpW90mGbdcgqRslzKtl7USDyogx1KQ/> .
@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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}

sub:assertion {
  <https://doi.org/10.48550/arXiv.2601.13806> dct:title "Knowledge Graph-Assisted LLM Post-Training for Enhanced Legal Reasoning" ;
    <http://purl.org/spar/cito/describes> <https://neverblink.eu/ontologies/llm-kg/methods#iracKgAssistedLlmPostTraining> ;
    <http://purl.org/spar/cito/discusses> <https://neverblink.eu/ontologies/llm-kg/methods#directPreferenceOptimization> , <https://neverblink.eu/ontologies/llm-kg/methods#graphOfThoughts> , <https://neverblink.eu/ontologies/llm-kg/methods#graphRAG> , <https://neverblink.eu/ontologies/llm-kg/methods#supervisedFineTuning> , <https://neverblink.eu/ontologies/llm-kg/methods#treeOfThoughts> ;
    a prov:Entity .
  <https://neverblink.eu/ontologies/llm-kg/methods#directPreferenceOptimization> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "Direct Preference Optimization" .
  <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#graphRAG> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "GraphRAG" .
  <https://neverblink.eu/ontologies/llm-kg/methods#iracKgAssistedLlmPostTraining> dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMPretraining> ;
    a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:comment "This method proposes a comprehensive approach where a specialized IRAC (Issue, Rule, Analysis, Conclusion) Knowledge Graph is constructed and subsequently used to generate high-quality training data. This data is then employed for post-training (via Supervised Fine-Tuning and Direct Preference Optimization) of Large Language Models, thereby enhancing their intrinsic legal reasoning capabilities and knowledge expression. While it's 'post-training' rather than initial 'pre-training', the core mechanism involves fundamentally improving the LLM's internal knowledge through a structured training process, aligning with the goal of enhancing LLM knowledge expression in a training stage." ;
    rdfs:label "IRAC KG-Assisted LLM Post-Training" ;
    <https://neverblink.eu/ontologies/llm-kg/hasTopCategory> <https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM> .
  <https://neverblink.eu/ontologies/llm-kg/methods#supervisedFineTuning> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "Supervised Fine-Tuning" .
  <https://neverblink.eu/ontologies/llm-kg/methods#treeOfThoughts> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "Tree-of-Thoughts" .
}

sub:provenance {
sub:assertion prov:wasAttributedTo <https://neverblink.eu/ontologies/llm-kg/agent> ;
prov:wasDerivedFrom <https://doi.org/10.48550/arXiv.2601.13806> .
}

sub:pubinfo {
  this: dct:created "2026-02-26T15:57:43.782Z"^^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.2601.13806" .
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