. . . . "Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning" . . . . . . . . . . . . . . . . . . . . . . . . . "CSProm-KG" . . "CompGCN" . . "ComplEx" . . "ConvE" . . "GPT-4" . . "GS-KGC" . . "KERMIT" . . "KG-FIT" . . "KG-LLaMA" . . "KG-S2S" . . "KICGPT" . . "KoPA" . . "MKGL" . . "MPIKGC" . . "PKGC" . . "QuatE" . . "RED-GNN" . . "RotatE" . . . "SAT (Structure-aware Alignment-Tuning) is a novel framework designed to enhance LLMs for Knowledge Graph Completion (KGC) tasks. It achieves this by introducing hierarchical knowledge alignment to bridge the representation gap between graph structures and natural language, and structural instruction tuning with a lightweight adapter to unify various KGC tasks. The primary goal is to improve the LLM's performance on KG completion tasks." . "SAT" . . . "SimKGC" . . "Structural-aware IT" . . "TransE" . . . "2026-02-26T15:25:26.850Z"^^ . . . "LLM-KG assessment for paper 10.48550/arXiv.2509.01166" . "RSA" . "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAwNz2QK3SEifno78S7+48zUB0xpTex3mAzW73ZimHqNcdEMU5/apslrGrTHGFAt/Chocgo++r6JQp5ygY7NyJHGWdaIqnt85pjX4PbNfLAvapyUO00qZP34fY61w4eZ9UMtleWEsmZKRtQPyJ8ODl46i/rfPuZlcJGpM9Nmy5mpGWuepqIEvF4a/t7pLVeCEDFSYXT+yaiygt6ynIK5f7TtEDhZpeUf/Q74WhMPJXm4yTU/hqOX4IW+50kWHNArGGZwUaXwzyG6M3Zd6UMModryGkLqS4H/MSE3ZA1Ylnms7BfWLEXhMWlaKi6HRV4nGRDLhxVSi9LSRi3LWKLhNIIQIDAQAB" . "HvYBcTTjW4CUw0pWNZHO9i/0cDMzlJhkV10n3m7TUkimGQdLCFfJ4aNzRX+ItAe9R6uS5SXq2EpjuKb0csUcBr8TfxMGCsj0FNu0l4ORQ220gFFwR1X7bVp3A9V4we0/F1ewZXaO1fdWE2MyBlfyKmEc7re0QX8FVWqhYVo+UMBmhvLW30c9RhYDo8sDL7AEBeIpeDw6rfuhx0OuTp9bRo9kYq4g+AltBrlZqNmOwq2aQdciDrtyDhHLJueIWiAzsLrWB6w4BtTQqseOjfy8gKEZzSO1FaqnzrL7wxVPjNteh2Axou2RTgiJmJ7TrMt632iZ1nLjVnzgeIm7x0OomA==" . . .