{"id":"https://openalex.org/W7134229284","doi":"https://doi.org/10.48550/arxiv.2603.05698","title":"Towards Robust Retrieval-Augmented Generation Based on Knowledge Graph: A Comparative Analysis","display_name":"Towards Robust Retrieval-Augmented Generation Based on Knowledge Graph: A Comparative Analysis","publication_year":2026,"publication_date":"2026-03-05","ids":{"openalex":"https://openalex.org/W7134229284","doi":"https://doi.org/10.48550/arxiv.2603.05698"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.05698","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.05698","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.05698","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120006172","display_name":"Hazem Amamou","orcid":"https://orcid.org/0009-0009-7998-9096"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Amamou, Hazem","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079136654","display_name":"St\u00e9phane Gagnon","orcid":"https://orcid.org/0000-0001-7732-9849"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gagnon, St\u00e9phane","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055388091","display_name":"Alan Davoust","orcid":"https://orcid.org/0000-0002-3423-0942"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Davoust, Alan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5009407218","display_name":"Anderson R. Avila","orcid":"https://orcid.org/0000-0002-3088-5116"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Avila, Anderson R.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.6431000232696533,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.6431000232696533,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.08630000054836273,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.05389999970793724,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7211999893188477},{"id":"https://openalex.org/keywords/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.6377000212669373},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5644999742507935},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5418999791145325},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.44110000133514404},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.32019999623298645}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7211999893188477},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.6377000212669373},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6290000081062317},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.578499972820282},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5644999742507935},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5418999791145325},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.44110000133514404},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43720000982284546},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.32019999623298645},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.301800012588501},{"id":"https://openalex.org/C2776035688","wikidata":"https://www.wikidata.org/wiki/Q1606558","display_name":"Affect (linguistics)","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2750999927520752},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.2669999897480011}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.05698","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.05698","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.05698","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.05698","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Retrieval-Augmented":[0,56],"Generation":[1,57],"(RAG)":[2],"was":[3,60],"introduced":[4,61],"to":[5,39,62,80,118],"enhance":[6],"the":[7,64,77,101,125],"capabilities":[8],"of":[9,29,66],"Large":[10],"Language":[11],"Models":[12],"(LLMs)":[13],"beyond":[14],"their":[15],"encoded":[16],"prior":[17],"knowledge.":[18],"This":[19],"is":[20],"achieved":[21],"by":[22],"providing":[23],"LLMs":[24,82],"with":[25],"an":[26],"external":[27],"source":[28],"knowledge,":[30],"which":[31],"helps":[32],"reduce":[33],"factual":[34],"hallucinations":[35],"and":[36,92,105,128],"enables":[37],"access":[38],"new":[40],"information":[41,49,88],"not":[42],"available":[43],"during":[44],"pretraining.":[45],"However,":[46],"inconsistent":[47],"retrieved":[48],"can":[50],"negatively":[51],"affect":[52],"LLM":[53],"responses.":[54],"The":[55],"Benchmark":[58],"(RGB)":[59],"evaluate":[63,81],"robustness":[65],"RAG":[67,103,135],"systems":[68,136],"under":[69],"such":[70],"conditions.":[71],"In":[72],"this":[73],"work,":[74],"we":[75],"use":[76],"RGB":[78,102,126],"corpus":[79],"in":[83],"four":[84],"scenarios:":[85],"noise":[86],"robustness,":[87],"integration,":[89],"negative":[90],"rejection,":[91],"counterfactual":[93],"robustness.":[94,120],"We":[95,113],"perform":[96],"a":[97,107],"comparative":[98],"analysis":[99],"between":[100],"baseline":[104,127],"GraphRAG,":[106],"knowledge":[108],"graph":[109],"based":[110],"retrieval":[111],"system.":[112],"test":[114],"three":[115],"GraphRAG":[116],"customizations":[117],"improve":[119],"Results":[121],"show":[122],"improvements":[123],"over":[124],"provide":[129],"insights":[130],"for":[131,137],"designing":[132],"more":[133],"reliable":[134],"real":[138],"world":[139],"scenarios.":[140]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-10T00:00:00"}