@prefix this: . @prefix sub: . @prefix np: . @prefix dct: . @prefix xsd: . @prefix rdfs: . @prefix prov: . @prefix npx: . sub:Head { this: np:hasAssertion sub:assertion; np:hasProvenance sub:provenance; np:hasPublicationInfo sub:pubinfo; a np:Nanopublication . } sub:assertion { dct:title "KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Models"; , ; , , , , , , , ; a prov:Entity . a ; rdfs:label "BM25" . a ; rdfs:label "DenseRetriever" . a ; rdfs:label "Graph-guided reasoning" . a ; rdfs:label "ITER-RETGEN" . a ; rdfs:label "ITRG" . dct:subject ; a ; rdfs:comment "KG-Retriever is a novel Retrieval-Augmented Generation (RAG) framework that leverages a Hierarchical Index Graph (HIG) to provide comprehensive and efficient knowledge to LLMs during the inference stage. Its goal is to improve the quality, credibility, and efficiency of LLM-generated responses by addressing challenges like multi-hop question answering and information fragmentation. This directly aligns with using KGs to enhance LLM performance during inference."; rdfs:label "KG-Retriever"; . dct:subject ; a ; rdfs:comment "This method describes the specific use of large language models (Qwen-72B) with in-context learning and designed prompts to extract entities and relations from unstructured text within documents. This process is crucial for constructing the entity-level knowledge graph layer of the Hierarchical Index Graph within the KG-Retriever framework, directly using LLMs to perform a core KG construction task."; rdfs:label "KG-Retriever's Entity-Level KG Construction using LLMs"; . a ; rdfs:label "KGP" . a ; rdfs:label "LLM with CoT" . a ; rdfs:label "Naive LLM" . } sub:provenance { sub:assertion prov:wasAttributedTo ; prov:wasDerivedFrom . } sub:pubinfo { this: dct:created "2026-02-26T15:49:44.125Z"^^xsd:dateTime; dct:creator ; npx:hasNanopubType ; rdfs:label "LLM-KG assessment for paper 10.48550/arXiv.2412.05547" . sub:sig npx:hasAlgorithm "RSA"; npx:hasPublicKey "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAwNz2QK3SEifno78S7+48zUB0xpTex3mAzW73ZimHqNcdEMU5/apslrGrTHGFAt/Chocgo++r6JQp5ygY7NyJHGWdaIqnt85pjX4PbNfLAvapyUO00qZP34fY61w4eZ9UMtleWEsmZKRtQPyJ8ODl46i/rfPuZlcJGpM9Nmy5mpGWuepqIEvF4a/t7pLVeCEDFSYXT+yaiygt6ynIK5f7TtEDhZpeUf/Q74WhMPJXm4yTU/hqOX4IW+50kWHNArGGZwUaXwzyG6M3Zd6UMModryGkLqS4H/MSE3ZA1Ylnms7BfWLEXhMWlaKi6HRV4nGRDLhxVSi9LSRi3LWKLhNIIQIDAQAB"; npx:hasSignature "Rl3BF7oUweCgNMnsf9axb1SOZmKjPyo26r++N4GzYqszupiiioJR8F4JfYiNr0qpxneKAqvqSnNmAIN0xWMnqLOJiEPePwsO6/H2uNemnjGee5YWbIHzskc3yaeNkU62ZjVQnyXun6ngHEMt/ZQRmuZFy/Ky15kS5XgpoBtyZLJGJK1debvH/4wtmKBPePY4G973v01yIBlXSl2/EAtwy7/c0pecon2bESYsa3I1esLfZcYnF1mADfW9Vjh4/M2AprNm6TcvIoRoX7vJgvi/5JoGSqwDTpusuF+hVvYImzGa90iPn/4SRmAL5N7F7/CildbVcP3mr6eZ5i1ZGIA9EQ=="; npx:hasSignatureTarget this:; npx:signedBy . }