Nanopublications

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@prefix sub: <https://w3id.org/np/RA_6CsM56gRUw5F1G7StJvgSiaBjwqwa4vg4dp4Pez6fs/> .
@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.2402.06764> dct:title "GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encoding" ;
    <http://purl.org/spar/cito/describes> <https://neverblink.eu/ontologies/llm-kg/methods#GLaMLLMSummarization> , <https://neverblink.eu/ontologies/llm-kg/methods#GLaMNodeDescriptorsAdjacencySummarization> , <https://neverblink.eu/ontologies/llm-kg/methods#GLaMRelationalGrouping> , <https://neverblink.eu/ontologies/llm-kg/methods#GLaMTriples> ;
    <http://purl.org/spar/cito/discusses> <https://neverblink.eu/ontologies/llm-kg/methods#FewShotPrompting> , <https://neverblink.eu/ontologies/llm-kg/methods#GNNLLMJointModelCoupling> , <https://neverblink.eu/ontologies/llm-kg/methods#GNNLLMSoftPrompting> , <https://neverblink.eu/ontologies/llm-kg/methods#RetrievalAugmentedGeneration> ;
    a prov:Entity .
  <https://neverblink.eu/ontologies/llm-kg/methods#FewShotPrompting> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "Few-Shot Prompting" .
  <https://neverblink.eu/ontologies/llm-kg/methods#GLaMLLMSummarization> dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMPretraining> ;
    a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:comment "This method leverages the LLM's generative capabilities to rewrite or summarize the encoded graph statements into more coherent representations for fine-tuning. The goal is to enhance semantic alignment between the KG and the LLM's vocabulary, thereby improving the LLM's factual recall and multi-hop reasoning after training." ;
    rdfs:label "GLaM (LLM Summarization)" ;
    <https://neverblink.eu/ontologies/llm-kg/hasTopCategory> <https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM> .
  <https://neverblink.eu/ontologies/llm-kg/methods#GLaMNodeDescriptorsAdjacencySummarization> dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMPretraining> ;
    a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:comment "This method combines multiple encoding strategies, specifically using LLM's zero-shot capabilities to create text-based node descriptors from the k-hop context subgraph, utilizing adjacency lists, and performing summarization. This comprehensive approach aims to instill robust graph-based reasoning capabilities into the LLM via fine-tuning." ;
    rdfs:label "GLaM (Node Descriptors, Adjacency Lists, and Summarization Combination)" ;
    <https://neverblink.eu/ontologies/llm-kg/hasTopCategory> <https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM> .
  <https://neverblink.eu/ontologies/llm-kg/methods#GLaMRelationalGrouping> dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMPretraining> ;
    a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:comment "This GLaM variant encodes the neighborhood subgraph by including the entire adjacency list of the central node or partitioning neighbors based on relation types. This strategy is used to fine-tune the LLM to better understand graph structure for improved knowledge expression." ;
    rdfs:label "GLaM (Relational Grouping)" ;
    <https://neverblink.eu/ontologies/llm-kg/hasTopCategory> <https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM> .
  <https://neverblink.eu/ontologies/llm-kg/methods#GLaMTriples> dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMPretraining> ;
    a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:comment "This method is a specific implementation within the GLaM framework where the neighborhood subgraph is encoded into (source, relation, target) triples for fine-tuning. It aims to improve the LLM's factual reasoning by embedding graph knowledge directly into its parameters during the training phase." ;
    rdfs:label "GLaM (Triples)" ;
    <https://neverblink.eu/ontologies/llm-kg/hasTopCategory> <https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM> .
  <https://neverblink.eu/ontologies/llm-kg/methods#GNNLLMJointModelCoupling> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "GNN-LLM Joint Model Coupling" .
  <https://neverblink.eu/ontologies/llm-kg/methods#GNNLLMSoftPrompting> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "GNN-LLM Soft Prompting" .
  <https://neverblink.eu/ontologies/llm-kg/methods#RetrievalAugmentedGeneration> a <http://purl.org/spar/fabio/Workflow> ;
    rdfs:label "Retrieval Augmented Generation (RAG)" .
}

sub:provenance {
sub:assertion prov:wasAttributedTo <https://neverblink.eu/ontologies/llm-kg/agent> ;
prov:wasDerivedFrom <https://doi.org/10.48550/arXiv.2402.06764> .
}

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