[ { "@id": "https://w3id.org/np/RA_6CsM56gRUw5F1G7StJvgSiaBjwqwa4vg4dp4Pez6fs/pubinfo", "@graph": [ { "@id": "https://w3id.org/np/RA_6CsM56gRUw5F1G7StJvgSiaBjwqwa4vg4dp4Pez6fs", "http://purl.org/dc/terms/created": [ { "@value": "2026-02-26T15:57:20.083Z", "@type": "http://www.w3.org/2001/XMLSchema#dateTime" } ], "http://purl.org/dc/terms/creator": [ { "@id": "https://neverblink.eu/ontologies/llm-kg/agent" } ], "http://purl.org/nanopub/x/hasNanopubType": [ { "@id": "https://neverblink.eu/ontologies/llm-kg/PaperAssessmentResult" } ], "http://www.w3.org/2000/01/rdf-schema#label": [ { "@value": "LLM-KG assessment for paper 10.48550/arXiv.2402.06764" } ] }, { "@id": "https://w3id.org/np/RA_6CsM56gRUw5F1G7StJvgSiaBjwqwa4vg4dp4Pez6fs/sig", "http://purl.org/nanopub/x/hasAlgorithm": [ { "@value": "RSA" } ], "http://purl.org/nanopub/x/hasPublicKey": [ { "@value": "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAwNz2QK3SEifno78S7+48zUB0xpTex3mAzW73ZimHqNcdEMU5/apslrGrTHGFAt/Chocgo++r6JQp5ygY7NyJHGWdaIqnt85pjX4PbNfLAvapyUO00qZP34fY61w4eZ9UMtleWEsmZKRtQPyJ8ODl46i/rfPuZlcJGpM9Nmy5mpGWuepqIEvF4a/t7pLVeCEDFSYXT+yaiygt6ynIK5f7TtEDhZpeUf/Q74WhMPJXm4yTU/hqOX4IW+50kWHNArGGZwUaXwzyG6M3Zd6UMModryGkLqS4H/MSE3ZA1Ylnms7BfWLEXhMWlaKi6HRV4nGRDLhxVSi9LSRi3LWKLhNIIQIDAQAB" } ], "http://purl.org/nanopub/x/hasSignature": [ { "@value": "u6dPlUuTUn1QedW6Fo7ljSYw2G4COewda0DpI0s95BWkF2vMzjoMKMokBVugNbLAF7WgBfWLNdwTqbnSBYf9xRted+aca4PBWgEF/7gswieTkJn0cmbIkRP85Fl+j9VWoAgVzb4c5Zu8KcI3cXo5W7LLfNx6yCW1fgamArf4JmksrxJIsftt9gcgHTJKFMt3XsI0WBTmVF3hcrJqj4tf/vAOIiuEnHkcMK2hOk4+0BAjf9MXAKG1b5ufatAxOHCQxqwe4dTz7da/ClVar+D0yMszjGQHpWfsEPcVvWz6hxmK2/0PueA/cKu4mrpSyACehPyNjjEwIqWSKn/XqMqzCg==" } ], "http://purl.org/nanopub/x/hasSignatureTarget": [ { "@id": "https://w3id.org/np/RA_6CsM56gRUw5F1G7StJvgSiaBjwqwa4vg4dp4Pez6fs" } ], "http://purl.org/nanopub/x/signedBy": [ { "@id": "https://neverblink.eu/ontologies/llm-kg/agent" } ] } ] }, { "@id": "https://w3id.org/np/RA_6CsM56gRUw5F1G7StJvgSiaBjwqwa4vg4dp4Pez6fs/provenance", "@graph": [ { "@id": "https://w3id.org/np/RA_6CsM56gRUw5F1G7StJvgSiaBjwqwa4vg4dp4Pez6fs/assertion", "http://www.w3.org/ns/prov#wasAttributedTo": [ { "@id": "https://neverblink.eu/ontologies/llm-kg/agent" } ], "http://www.w3.org/ns/prov#wasDerivedFrom": [ { "@id": "https://doi.org/10.48550/arXiv.2402.06764" } ] } ] }, { "@id": "https://w3id.org/np/RA_6CsM56gRUw5F1G7StJvgSiaBjwqwa4vg4dp4Pez6fs/Head", "@graph": [ { "@id": "https://w3id.org/np/RA_6CsM56gRUw5F1G7StJvgSiaBjwqwa4vg4dp4Pez6fs", "http://www.nanopub.org/nschema#hasAssertion": [ { "@id": "https://w3id.org/np/RA_6CsM56gRUw5F1G7StJvgSiaBjwqwa4vg4dp4Pez6fs/assertion" } ], "http://www.nanopub.org/nschema#hasProvenance": [ { "@id": "https://w3id.org/np/RA_6CsM56gRUw5F1G7StJvgSiaBjwqwa4vg4dp4Pez6fs/provenance" } ], "http://www.nanopub.org/nschema#hasPublicationInfo": [ { "@id": "https://w3id.org/np/RA_6CsM56gRUw5F1G7StJvgSiaBjwqwa4vg4dp4Pez6fs/pubinfo" } ], "@type": [ "http://www.nanopub.org/nschema#Nanopublication" ] } ] }, { "@id": "https://w3id.org/np/RA_6CsM56gRUw5F1G7StJvgSiaBjwqwa4vg4dp4Pez6fs/assertion", "@graph": [ { "@id": "https://doi.org/10.48550/arXiv.2402.06764", "http://purl.org/dc/terms/title": [ { "@value": "GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encoding" } ], "http://purl.org/spar/cito/describes": [ { "@id": "https://neverblink.eu/ontologies/llm-kg/methods#GLaMLLMSummarization" }, { "@id": "https://neverblink.eu/ontologies/llm-kg/methods#GLaMNodeDescriptorsAdjacencySummarization" }, { "@id": "https://neverblink.eu/ontologies/llm-kg/methods#GLaMRelationalGrouping" }, { "@id": "https://neverblink.eu/ontologies/llm-kg/methods#GLaMTriples" } ], "http://purl.org/spar/cito/discusses": [ { "@id": "https://neverblink.eu/ontologies/llm-kg/methods#FewShotPrompting" }, { "@id": "https://neverblink.eu/ontologies/llm-kg/methods#GNNLLMJointModelCoupling" }, { "@id": "https://neverblink.eu/ontologies/llm-kg/methods#GNNLLMSoftPrompting" }, { "@id": "https://neverblink.eu/ontologies/llm-kg/methods#RetrievalAugmentedGeneration" } ], "@type": [ "http://www.w3.org/ns/prov#Entity" ] }, { "@id": "https://neverblink.eu/ontologies/llm-kg/methods#GLaMLLMSummarization", "http://purl.org/dc/terms/subject": [ { "@id": "https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMPretraining" } ], "@type": [ "http://purl.org/spar/fabio/Workflow" ], "http://www.w3.org/2000/01/rdf-schema#comment": [ { "@value": "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." } ], "http://www.w3.org/2000/01/rdf-schema#label": [ { "@value": "GLaM (LLM Summarization)" } ], "https://neverblink.eu/ontologies/llm-kg/hasTopCategory": [ { "@id": "https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM" } ] }, { "@id": "https://neverblink.eu/ontologies/llm-kg/methods#GLaMNodeDescriptorsAdjacencySummarization", "http://purl.org/dc/terms/subject": [ { "@id": "https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMPretraining" } ], "@type": [ "http://purl.org/spar/fabio/Workflow" ], "http://www.w3.org/2000/01/rdf-schema#comment": [ { "@value": "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." } ], "http://www.w3.org/2000/01/rdf-schema#label": [ { "@value": "GLaM (Node Descriptors, Adjacency Lists, and Summarization Combination)" } ], "https://neverblink.eu/ontologies/llm-kg/hasTopCategory": [ { "@id": "https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM" } ] }, { "@id": "https://neverblink.eu/ontologies/llm-kg/methods#GLaMRelationalGrouping", "http://purl.org/dc/terms/subject": [ { "@id": "https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMPretraining" } ], "@type": [ "http://purl.org/spar/fabio/Workflow" ], "http://www.w3.org/2000/01/rdf-schema#comment": [ { "@value": "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." } ], "http://www.w3.org/2000/01/rdf-schema#label": [ { "@value": "GLaM (Relational Grouping)" } ], "https://neverblink.eu/ontologies/llm-kg/hasTopCategory": [ { "@id": "https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM" } ] }, { "@id": "https://neverblink.eu/ontologies/llm-kg/methods#GLaMTriples", "http://purl.org/dc/terms/subject": [ { "@id": "https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMPretraining" } ], "@type": [ "http://purl.org/spar/fabio/Workflow" ], "http://www.w3.org/2000/01/rdf-schema#comment": [ { "@value": "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." } ], "http://www.w3.org/2000/01/rdf-schema#label": [ { "@value": "GLaM (Triples)" } ], "https://neverblink.eu/ontologies/llm-kg/hasTopCategory": [ { "@id": "https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM" } ] }, { "@id": "https://neverblink.eu/ontologies/llm-kg/methods#FewShotPrompting", "@type": [ "http://purl.org/spar/fabio/Workflow" ], "http://www.w3.org/2000/01/rdf-schema#label": [ { "@value": "Few-Shot Prompting" } ] }, { "@id": "https://neverblink.eu/ontologies/llm-kg/methods#GNNLLMJointModelCoupling", "@type": [ "http://purl.org/spar/fabio/Workflow" ], "http://www.w3.org/2000/01/rdf-schema#label": [ { "@value": "GNN-LLM Joint Model Coupling" } ] }, { "@id": "https://neverblink.eu/ontologies/llm-kg/methods#GNNLLMSoftPrompting", "@type": [ "http://purl.org/spar/fabio/Workflow" ], "http://www.w3.org/2000/01/rdf-schema#label": [ { "@value": "GNN-LLM Soft Prompting" } ] }, { "@id": "https://neverblink.eu/ontologies/llm-kg/methods#RetrievalAugmentedGeneration", "@type": [ "http://purl.org/spar/fabio/Workflow" ], "http://www.w3.org/2000/01/rdf-schema#label": [ { "@value": "Retrieval Augmented Generation (RAG)" } ] } ] } ]