. . . . "KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge" . . . . . . . . . . . . . . . . . . . . . "ComplEx" . . "ConvE" . . "CSProm-KG" . . "DistMult" . . "HAKE" . . "KG-BERT" . . . "KG-FIT is a novel framework that enhances Knowledge Graph Embeddings (KGE) by integrating open-world entity knowledge from Large Language Models (LLMs). It uses LLMs to guide hierarchical clustering of entities and refine the hierarchy, then fine-tunes KG embeddings using this LLM-derived hierarchical structure, textual embeddings, and standard link prediction objectives. The goal is to improve KG embeddings and their expressiveness for tasks like link prediction." . "KG-FIT" . . . "KG-S2S" . . "KGT5" . . "LLM Embedding Zero-Shot Link Prediction" . . "LMKE" . . "pRotatE" . . "PKGC" . . "RotatE" . . "SimKGC" . . "StAR" . . "TransE" . . "TuckER" . . . "2026-02-26T15:23:33.730Z"^^ . . . "LLM-KG assessment for paper 10.48550/arXiv.2405.16412" . "RSA" . "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAwNz2QK3SEifno78S7+48zUB0xpTex3mAzW73ZimHqNcdEMU5/apslrGrTHGFAt/Chocgo++r6JQp5ygY7NyJHGWdaIqnt85pjX4PbNfLAvapyUO00qZP34fY61w4eZ9UMtleWEsmZKRtQPyJ8ODl46i/rfPuZlcJGpM9Nmy5mpGWuepqIEvF4a/t7pLVeCEDFSYXT+yaiygt6ynIK5f7TtEDhZpeUf/Q74WhMPJXm4yTU/hqOX4IW+50kWHNArGGZwUaXwzyG6M3Zd6UMModryGkLqS4H/MSE3ZA1Ylnms7BfWLEXhMWlaKi6HRV4nGRDLhxVSi9LSRi3LWKLhNIIQIDAQAB" . "epP7SRCLMGLuom3zy64aT2uxm90GEnM2+YGmjlz3bEhtEIZiQyQaYOQfrlQjVlyGtgJj2s74kph0QvVNduOZhsZCG1FaPkHqJ/3Tv7nWwkUtTp8cTCtnnuIzoCoDFFGK72jQY29w7uMLoya9Kg8kHmZJc/jjgeIfhqj502beeSPH8mSm2ivd110WAo5AAm+ak27YuhrgWKGqRMp0fd0jZ19coVjuZKCqEyLvjFD+n1FP5kgv7rDhqnr0e9gkLEt2ScUQ5QsViLajGljjaGQdEd2zVPacQ26powQTSI6nkURKWnVa2Lq4rzFapPmg0+WPOd3mubUcaX3ekKzF3lpT+Q==" . . .