https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c/Head https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c/assertion https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c/provenance https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c/pubinfo https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c/assertion https://doi.org/10.48550/arXiv.2403.07311 http://purl.org/dc/terms/title Knowledge Graph Large Language Model (KG-LLM) for Link Prediction https://doi.org/10.48550/arXiv.2403.07311 http://purl.org/spar/cito/describes https://neverblink.eu/ontologies/llm-kg/methods#KnowledgeGraphLargeLanguageModel https://doi.org/10.48550/arXiv.2403.07311 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#Analogy https://doi.org/10.48550/arXiv.2403.07311 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#CompleX https://doi.org/10.48550/arXiv.2403.07311 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#ConGLR https://doi.org/10.48550/arXiv.2403.07311 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#ConvRot https://doi.org/10.48550/arXiv.2403.07311 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#DistMult https://doi.org/10.48550/arXiv.2403.07311 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#RESCAL https://doi.org/10.48550/arXiv.2403.07311 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#TransE https://doi.org/10.48550/arXiv.2403.07311 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#WsGAT https://doi.org/10.48550/arXiv.2403.07311 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/ns/prov#Entity https://neverblink.eu/ontologies/llm-kg/methods#Analogy http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#Analogy http://www.w3.org/2000/01/rdf-schema#comment Analogy is a knowledge graph embedding model designed to capture graph structures. It is used as a baseline for comparison against the KG-LLM framework in multi-hop link prediction tasks. https://neverblink.eu/ontologies/llm-kg/methods#Analogy http://www.w3.org/2000/01/rdf-schema#label Analogy https://neverblink.eu/ontologies/llm-kg/methods#CompleX http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#CompleX http://www.w3.org/2000/01/rdf-schema#comment CompleX utilizes complex embeddings for entities and relations to capture asymmetric relationships in knowledge graphs. It is used as a baseline for comparison against the KG-LLM framework in multi-hop link prediction tasks. https://neverblink.eu/ontologies/llm-kg/methods#CompleX http://www.w3.org/2000/01/rdf-schema#label CompleX https://neverblink.eu/ontologies/llm-kg/methods#ConGLR http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#ConGLR http://www.w3.org/2000/01/rdf-schema#comment ConGLR is a GNN-based model that leverages context-aware graph representation learning and logical reasoning for improved inductive relation prediction. It is used as a baseline for comparison against the KG-LLM framework. https://neverblink.eu/ontologies/llm-kg/methods#ConGLR http://www.w3.org/2000/01/rdf-schema#label ConGLR https://neverblink.eu/ontologies/llm-kg/methods#ConvRot http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#ConvRot http://www.w3.org/2000/01/rdf-schema#comment ConvRot integrates convolutional networks and rotational embeddings to enhance link prediction performance in knowledge graphs. It is used as a GNN-based baseline for comparison against the KG-LLM framework. https://neverblink.eu/ontologies/llm-kg/methods#ConvRot http://www.w3.org/2000/01/rdf-schema#label ConvRot https://neverblink.eu/ontologies/llm-kg/methods#DistMult http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#DistMult http://www.w3.org/2000/01/rdf-schema#comment DistMult represents relations as diagonal matrices for simplicity and efficiency in knowledge graph embedding. It is used as a baseline for comparison against the KG-LLM framework in multi-hop link prediction tasks. https://neverblink.eu/ontologies/llm-kg/methods#DistMult http://www.w3.org/2000/01/rdf-schema#label DistMult https://neverblink.eu/ontologies/llm-kg/methods#KnowledgeGraphLargeLanguageModel http://purl.org/dc/terms/subject https://neverblink.eu/ontologies/llm-kg/categories#LLMAugmentedKGCompletion https://neverblink.eu/ontologies/llm-kg/methods#KnowledgeGraphLargeLanguageModel http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#KnowledgeGraphLargeLanguageModel http://www.w3.org/2000/01/rdf-schema#comment The KG-LLM framework proposes a novel approach that converts multi-hop knowledge graph paths into structured natural language Chain-of-Thought prompts. These prompts are then used to instruction fine-tune large language models, incorporating In-Context Learning to enhance their performance in multi-hop link prediction and multi-hop relation prediction tasks, leveraging LLMs to complete missing facts within KGs. https://neverblink.eu/ontologies/llm-kg/methods#KnowledgeGraphLargeLanguageModel http://www.w3.org/2000/01/rdf-schema#label Knowledge Graph Large Language Model (KG-LLM) https://neverblink.eu/ontologies/llm-kg/methods#KnowledgeGraphLargeLanguageModel https://neverblink.eu/ontologies/llm-kg/hasTopCategory https://neverblink.eu/ontologies/llm-kg/top-categories#LLMAugmentedKG https://neverblink.eu/ontologies/llm-kg/methods#RESCAL http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#RESCAL http://www.w3.org/2000/01/rdf-schema#comment RESCAL is a tensor factorization method used for knowledge graph embedding that captures rich interactions between entities and relations. It is used as a baseline for comparison against the KG-LLM framework in multi-hop link prediction tasks. https://neverblink.eu/ontologies/llm-kg/methods#RESCAL http://www.w3.org/2000/01/rdf-schema#label RESCAL https://neverblink.eu/ontologies/llm-kg/methods#TransE http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#TransE http://www.w3.org/2000/01/rdf-schema#comment TransE is a traditional knowledge graph embedding model that represents relationships as translations in the embedding space. It is used as a baseline for comparison against the KG-LLM framework in multi-hop link prediction tasks. https://neverblink.eu/ontologies/llm-kg/methods#TransE http://www.w3.org/2000/01/rdf-schema#label TransE https://neverblink.eu/ontologies/llm-kg/methods#WsGAT http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#WsGAT http://www.w3.org/2000/01/rdf-schema#comment wsGAT is a graph attention network model that uses weighted self-attention mechanisms to perform various knowledge graph tasks, including link prediction. It is used as a GNN-based baseline for comparison against the KG-LLM framework. https://neverblink.eu/ontologies/llm-kg/methods#WsGAT http://www.w3.org/2000/01/rdf-schema#label wsGAT https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c/provenance https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c/assertion http://www.w3.org/ns/prov#wasAttributedTo https://neverblink.eu/ontologies/llm-kg/agent https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c/assertion http://www.w3.org/ns/prov#wasDerivedFrom https://doi.org/10.48550/arXiv.2403.07311 https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c/pubinfo https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c http://purl.org/dc/terms/created 2026-03-13T16:03:07.317Z https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c http://purl.org/dc/terms/creator https://neverblink.eu/ontologies/llm-kg/agent https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c http://purl.org/nanopub/x/hasNanopubType https://neverblink.eu/ontologies/llm-kg/PaperAssessmentResult https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c http://purl.org/nanopub/x/supersedes https://w3id.org/np/RAtsoiWc5ZvdO9lft0xqhlYJoPJRdoCN-epHOOm4VSpZE https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c http://www.w3.org/2000/01/rdf-schema#label LLM-KG assessment for paper 10.48550/arXiv.2403.07311 https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c/sig http://purl.org/nanopub/x/hasAlgorithm RSA https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c/sig http://purl.org/nanopub/x/hasPublicKey MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAwNz2QK3SEifno78S7+48zUB0xpTex3mAzW73ZimHqNcdEMU5/apslrGrTHGFAt/Chocgo++r6JQp5ygY7NyJHGWdaIqnt85pjX4PbNfLAvapyUO00qZP34fY61w4eZ9UMtleWEsmZKRtQPyJ8ODl46i/rfPuZlcJGpM9Nmy5mpGWuepqIEvF4a/t7pLVeCEDFSYXT+yaiygt6ynIK5f7TtEDhZpeUf/Q74WhMPJXm4yTU/hqOX4IW+50kWHNArGGZwUaXwzyG6M3Zd6UMModryGkLqS4H/MSE3ZA1Ylnms7BfWLEXhMWlaKi6HRV4nGRDLhxVSi9LSRi3LWKLhNIIQIDAQAB https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c/sig http://purl.org/nanopub/x/hasSignature M8A86QxaelEblRjE6EJVb/G181uD5Mr5emikye+XbhLXhmpxa/59PdgCAIpnXlJ5g0iwsfFkcaJQnPsyr+8w7kCG/pll0z1V12nxSRBjWL+zhRSWNdqtuXcEgFfHAlxGhYkg49OI7pNLjwtakobWrRmFfoXD4GodW7vFyMwS3GyBGeUlfmO4D4giKTP/ZRQueUldq5qVBIFSaTc7sEbZV7BeJfFsZMZ2aaQVnjcV6k6SYd6sk2Ml6Y1crMoXuaO93e9/MnyGWiwPvoNAo2PmiR8pMHO42BSg8BYLpoN8wa7bfg0UatTJb++KWzwmrKTuALfNR2cKMrnC921loiVDbg== https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c/sig http://purl.org/nanopub/x/hasSignatureTarget https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c https://w3id.org/np/RAQKBbEQ--W2vSHDaZ0LrgiFnCkSkeH3JX7ZrENSNnS4c/sig http://purl.org/nanopub/x/signedBy https://neverblink.eu/ontologies/llm-kg/agent