https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg/Head https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg/assertion https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg/provenance https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg/pubinfo https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg/assertion https://doi.org/10.48550/arXiv.2406.17231 http://purl.org/dc/terms/title CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph https://doi.org/10.48550/arXiv.2406.17231 http://purl.org/spar/cito/describes https://neverblink.eu/ontologies/llm-kg/methods#CogMG https://doi.org/10.48550/arXiv.2406.17231 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#LiEtAl2023KBBINDER https://doi.org/10.48550/arXiv.2406.17231 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#CogMG http://purl.org/dc/terms/subject https://neverblink.eu/ontologies/llm-kg/categories#SynergizedReasoning https://neverblink.eu/ontologies/llm-kg/methods#CogMG http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#CogMG http://www.w3.org/2000/01/rdf-schema#comment CogMG is a collaborative augmentation framework where LLMs act as agents to identify and decompose knowledge requirements that are not covered by the KG. The LLM then completes missing knowledge using its internal parameters, and facilitates the verification and active update of the KG. This process mutually enhances LLM performance in QA by providing factual knowledge and improves KG completeness and alignment with user needs through LLM-driven evolution, demonstrating synergistic reasoning and interaction. https://neverblink.eu/ontologies/llm-kg/methods#CogMG http://www.w3.org/2000/01/rdf-schema#label CogMG https://neverblink.eu/ontologies/llm-kg/methods#CogMG https://neverblink.eu/ontologies/llm-kg/hasTopCategory https://neverblink.eu/ontologies/llm-kg/top-categories#SynergizedLLMKG https://neverblink.eu/ontologies/llm-kg/methods#LiEtAl2023KBBINDER http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#LiEtAl2023KBBINDER http://www.w3.org/2000/01/rdf-schema#comment KB-BINDER (Li et al., 2023) is an existing method for Knowledge Base Question Answering (KBQA) discussed in the related work. It utilizes LLMs' in-context learning to generate draft logical expressions and match executable programs. The paper uses KB-BINDER as an example to contrast CogMG's novelty in addressing scenarios where the knowledge graph lacks necessary information. https://neverblink.eu/ontologies/llm-kg/methods#LiEtAl2023KBBINDER http://www.w3.org/2000/01/rdf-schema#label KB-BINDER https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg/provenance https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg/assertion http://www.w3.org/ns/prov#wasAttributedTo https://neverblink.eu/ontologies/llm-kg/agent https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg/assertion http://www.w3.org/ns/prov#wasDerivedFrom https://doi.org/10.48550/arXiv.2406.17231 https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg/pubinfo https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg http://purl.org/dc/terms/created 2026-03-13T16:06:15.779Z https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg http://purl.org/dc/terms/creator https://neverblink.eu/ontologies/llm-kg/agent https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg http://purl.org/nanopub/x/hasNanopubType https://neverblink.eu/ontologies/llm-kg/PaperAssessmentResult https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg http://purl.org/nanopub/x/supersedes https://w3id.org/np/RAwysc5gfug22cLl-lDA5v2MPpltkBjEifpt7Y2YeJHRo https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg http://www.w3.org/2000/01/rdf-schema#label LLM-KG assessment for paper 10.48550/arXiv.2406.17231 https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg/sig http://purl.org/nanopub/x/hasAlgorithm RSA https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg/sig http://purl.org/nanopub/x/hasPublicKey MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAwNz2QK3SEifno78S7+48zUB0xpTex3mAzW73ZimHqNcdEMU5/apslrGrTHGFAt/Chocgo++r6JQp5ygY7NyJHGWdaIqnt85pjX4PbNfLAvapyUO00qZP34fY61w4eZ9UMtleWEsmZKRtQPyJ8ODl46i/rfPuZlcJGpM9Nmy5mpGWuepqIEvF4a/t7pLVeCEDFSYXT+yaiygt6ynIK5f7TtEDhZpeUf/Q74WhMPJXm4yTU/hqOX4IW+50kWHNArGGZwUaXwzyG6M3Zd6UMModryGkLqS4H/MSE3ZA1Ylnms7BfWLEXhMWlaKi6HRV4nGRDLhxVSi9LSRi3LWKLhNIIQIDAQAB https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg/sig http://purl.org/nanopub/x/hasSignature nNgAj5yhVOfW1OzgPJxqmv7Ta1G88JhnpQf2D0x0liYSV1fQjgB89eL5a5Cxv8ZaFBzzNFBgnwL/cX/4sfOI+DdE3hp0H0854dm3rrLeGXVuXMoox1+GYMkiNFa53XyGcvbNqgmOXCG/J3LvrSY76sO+aTG3OlveSc7n5f7Bhgg+Vo0B9rsmsJxB/He3xOG3bl3lq15zL59huU1zfpp2J/fxpqIFqGdOIBclE9iczdN0zYz1ZrIT2HQWfr6XQ4Hl0dQ9fYLgcq+YCrXfCymuBRN2XwDrlfSf1Pui/IujhDdWeqOYsJk9qtMyWshuNEt5buCsJWuaDqUGBbIlFefYEA== https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg/sig http://purl.org/nanopub/x/hasSignatureTarget https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg https://w3id.org/np/RALgZ9z8JxiJ1y3hhTAFs3VMu8By-cdlEmFOgcgFoa6zg/sig http://purl.org/nanopub/x/signedBy https://neverblink.eu/ontologies/llm-kg/agent