. . . . "Knowledge Graph Large Language Model (KG-LLM) for Link Prediction" . . . . . . . . . . . . "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." . "Analogy" . . "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." . "CompleX" . . "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." . "ConGLR" . . "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." . "ConvRot" . . "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." . "DistMult" . . . "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." . "Knowledge Graph Large Language Model (KG-LLM)" . . . "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." . "RESCAL" . . "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." . "TransE" . . "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." . "wsGAT" . . . "2026-03-13T16:03:07.317Z"^^ . . . . "LLM-KG assessment for paper 10.48550/arXiv.2403.07311" . "RSA" . "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAwNz2QK3SEifno78S7+48zUB0xpTex3mAzW73ZimHqNcdEMU5/apslrGrTHGFAt/Chocgo++r6JQp5ygY7NyJHGWdaIqnt85pjX4PbNfLAvapyUO00qZP34fY61w4eZ9UMtleWEsmZKRtQPyJ8ODl46i/rfPuZlcJGpM9Nmy5mpGWuepqIEvF4a/t7pLVeCEDFSYXT+yaiygt6ynIK5f7TtEDhZpeUf/Q74WhMPJXm4yTU/hqOX4IW+50kWHNArGGZwUaXwzyG6M3Zd6UMModryGkLqS4H/MSE3ZA1Ylnms7BfWLEXhMWlaKi6HRV4nGRDLhxVSi9LSRi3LWKLhNIIQIDAQAB" . "M8A86QxaelEblRjE6EJVb/G181uD5Mr5emikye+XbhLXhmpxa/59PdgCAIpnXlJ5g0iwsfFkcaJQnPsyr+8w7kCG/pll0z1V12nxSRBjWL+zhRSWNdqtuXcEgFfHAlxGhYkg49OI7pNLjwtakobWrRmFfoXD4GodW7vFyMwS3GyBGeUlfmO4D4giKTP/ZRQueUldq5qVBIFSaTc7sEbZV7BeJfFsZMZ2aaQVnjcV6k6SYd6sk2Ml6Y1crMoXuaO93e9/MnyGWiwPvoNAo2PmiR8pMHO42BSg8BYLpoN8wa7bfg0UatTJb++KWzwmrKTuALfNR2cKMrnC921loiVDbg==" . . .