Nanopublications

@prefix this: <https://w3id.org/np/RAkEC3g1S3-AUa4oS1tpjvWWtUUz8VEbzlSRYczzMRW3A> .
@prefix sub: <https://w3id.org/np/RAkEC3g1S3-AUa4oS1tpjvWWtUUz8VEbzlSRYczzMRW3A/> .
@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/> .

sub:Head {
  this: np:hasAssertion sub:assertion ;
    np:hasProvenance sub:provenance ;
    np:hasPublicationInfo sub:pubinfo ;
    a np:Nanopublication .
}

sub:assertion {
  <https://doi.org/10.48550/arXiv.2502.03992> dct:title "Ontology-Guided, Hybrid Prompt Learning for Generalization in Knowledge Graph Question Answering" ;
    <http://purl.org/spar/cito/describes> <https://neverblink.eu/ontologies/llm-kg/methods#OntoSCPrompt> ;
    <http://purl.org/spar/cito/discusses> <https://neverblink.eu/ontologies/llm-kg/methods#EmbedKGQA> , <https://neverblink.eu/ontologies/llm-kg/methods#GraphNet> , <https://neverblink.eu/ontologies/llm-kg/methods#HGNet> , <https://neverblink.eu/ontologies/llm-kg/methods#PullNet> , <https://neverblink.eu/ontologies/llm-kg/methods#STaGQA> , <https://neverblink.eu/ontologies/llm-kg/methods#TERP> ;
    a prov:Entity .
  <https://neverblink.eu/ontologies/llm-kg/methods#EmbedKGQA> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "EmbedKGQA" .
  <https://neverblink.eu/ontologies/llm-kg/methods#GraphNet> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "GraphNet" .
  <https://neverblink.eu/ontologies/llm-kg/methods#HGNet> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "HGNet" .
  <https://neverblink.eu/ontologies/llm-kg/methods#OntoSCPrompt> dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#LLMAugmentedKGQuestionAnswering> ;
    a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:comment "This method introduces a novel two-stage LLM-based KGQA system to generalize across heterogeneous KGs. It utilizes an ontology-guided hybrid prompt learning strategy, integrating KG ontology into prompts for semantic parsing and KG content population, and employs task-specific decoding strategies to ensure SPARQL query validity. The primary goal is to improve KGQA performance and generalization using LLMs." ;
    rdfs:label "OntoSCPrompt" ;
    <https://neverblink.eu/ontologies/llm-kg/hasTopCategory> <https://neverblink.eu/ontologies/llm-kg/top-categories#LLMAugmentedKG> .
  <https://neverblink.eu/ontologies/llm-kg/methods#PullNet> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "PullNet" .
  <https://neverblink.eu/ontologies/llm-kg/methods#STaGQA> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "STaG-QA" .
  <https://neverblink.eu/ontologies/llm-kg/methods#TERP> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "TERP" .
}

sub:provenance {
sub:assertion prov:wasAttributedTo <https://neverblink.eu/ontologies/llm-kg/agent> ;
prov:wasDerivedFrom <https://doi.org/10.48550/arXiv.2502.03992> .
}

sub:pubinfo {
  this: dct:created "2026-02-26T16:27:05.185Z"^^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.2502.03992" .
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