{"id":"https://openalex.org/W7160416407","doi":"https://doi.org/10.1162/tacl.a.615","title":"Dissecting GraphRAG: A Modular Analysis of Knowledge Structuring for Factoid Question Answering","display_name":"Dissecting GraphRAG: A Modular Analysis of Knowledge Structuring for Factoid Question Answering","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7160416407","doi":"https://doi.org/10.1162/tacl.a.615"},"language":"en","primary_location":{"id":"doi:10.1162/tacl.a.615","is_oa":true,"landing_page_url":"https://doi.org/10.1162/tacl.a.615","pdf_url":null,"source":{"id":"https://openalex.org/S2729999759","display_name":"Transactions of the Association for Computational Linguistics","issn_l":"2307-387X","issn":["2307-387X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320244","host_organization_name":"Association for Computational Linguistics","host_organization_lineage":["https://openalex.org/P4310320244"],"host_organization_lineage_names":["Association for Computational Linguistics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Transactions of the Association for Computational Linguistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1162/tacl.a.615","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005456292","display_name":"Noriki Nishida","orcid":"https://orcid.org/0000-0002-5851-7681"},"institutions":[{"id":"https://openalex.org/I4210162022","display_name":"RIKEN Nishina Center","ror":"https://ror.org/05tqx4s13","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652","https://openalex.org/I4210162022"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Noriki Nishida","raw_affiliation_strings":["RIKEN, Japan. noriki.nishida@riken.jp"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN, Japan. noriki.nishida@riken.jp","institution_ids":["https://openalex.org/I4210162022"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068189929","display_name":"Rumana Ferdous Munne","orcid":null},"institutions":[{"id":"https://openalex.org/I4210110652","display_name":"RIKEN","ror":"https://ror.org/01sjwvz98","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Rumana Ferdous Munne","raw_affiliation_strings":["RIKEN, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN, Japan","institution_ids":["https://openalex.org/I4210110652"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135461660","display_name":"Shanshan Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210110652","display_name":"RIKEN","ror":"https://ror.org/01sjwvz98","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shanshan Liu","raw_affiliation_strings":["RIKEN, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN, Japan","institution_ids":["https://openalex.org/I4210110652"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039540666","display_name":"Narumi Tokunaga","orcid":null},"institutions":[{"id":"https://openalex.org/I4210110652","display_name":"RIKEN","ror":"https://ror.org/01sjwvz98","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Narumi Tokunaga","raw_affiliation_strings":["RIKEN, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN, Japan","institution_ids":["https://openalex.org/I4210110652"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067757315","display_name":"Yuki Yamagata","orcid":"https://orcid.org/0000-0002-9673-1283"},"institutions":[{"id":"https://openalex.org/I4210110652","display_name":"RIKEN","ror":"https://ror.org/01sjwvz98","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuki Yamagata","raw_affiliation_strings":["RIKEN, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN, Japan","institution_ids":["https://openalex.org/I4210110652"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135463833","display_name":"Fei Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Fei Cheng","raw_affiliation_strings":["Kyoto University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kyoto University, Japan","institution_ids":["https://openalex.org/I22299242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051448264","display_name":"Kouji Kozaki","orcid":"https://orcid.org/0000-0003-4578-4980"},"institutions":[{"id":"https://openalex.org/I189513530","display_name":"Osaka Electro-Communication University","ror":"https://ror.org/056bksm23","country_code":"JP","type":"education","lineage":["https://openalex.org/I189513530"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kouji Kozaki","raw_affiliation_strings":["Osaka Electro-Communication University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Osaka Electro-Communication University, Japan","institution_ids":["https://openalex.org/I189513530"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072032804","display_name":"Y\u016bji Matsumoto","orcid":"https://orcid.org/0000-0003-4946-9574"},"institutions":[{"id":"https://openalex.org/I4210110652","display_name":"RIKEN","ror":"https://ror.org/01sjwvz98","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuji Matsumoto","raw_affiliation_strings":["RIKEN, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN, Japan","institution_ids":["https://openalex.org/I4210110652"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.54486229,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"627","last_page":"655"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.6074000000953674,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.6074000000953674,"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/T10028","display_name":"Topic Modeling","score":0.2759999930858612,"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/T13274","display_name":"Expert finding and Q&A systems","score":0.05290000140666962,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.7003999948501587},{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.694599986076355},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.6629999876022339},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.645799994468689},{"id":"https://openalex.org/keywords/structuring","display_name":"Structuring","score":0.5914000272750854},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.4767000079154968},{"id":"https://openalex.org/keywords/knowledge-extraction","display_name":"Knowledge extraction","score":0.45089998841285706},{"id":"https://openalex.org/keywords/downstream","display_name":"Downstream (manufacturing)","score":0.3984000086784363}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.9053000211715698},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.7003999948501587},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.694599986076355},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.6629999876022339},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.645799994468689},{"id":"https://openalex.org/C2775945657","wikidata":"https://www.wikidata.org/wiki/Q381442","display_name":"Structuring","level":2,"score":0.5914000272750854},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.508899986743927},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.4767000079154968},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.45089998841285706},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.3984000086784363},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.3935000002384186},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3846000134944916},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.37369999289512634},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.37059998512268066},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.36149999499320984},{"id":"https://openalex.org/C2984563274","wikidata":"https://www.wikidata.org/wiki/Q2586354","display_name":"Knowledge flow","level":2,"score":0.33329999446868896},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.328000009059906},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.32089999318122864},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3131999969482422},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.296099990606308},{"id":"https://openalex.org/C29804473","wikidata":"https://www.wikidata.org/wiki/Q2025711","display_name":"Open Knowledge Base Connectivity","level":4,"score":0.29030001163482666},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C84685590","wikidata":"https://www.wikidata.org/wiki/Q1540472","display_name":"Knowledge engineering","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.26649999618530273}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1162/tacl.a.615","is_oa":true,"landing_page_url":"https://doi.org/10.1162/tacl.a.615","pdf_url":null,"source":{"id":"https://openalex.org/S2729999759","display_name":"Transactions of the Association for Computational Linguistics","issn_l":"2307-387X","issn":["2307-387X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320244","host_organization_name":"Association for Computational Linguistics","host_organization_lineage":["https://openalex.org/P4310320244"],"host_organization_lineage_names":["Association for Computational Linguistics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Transactions of the Association for Computational Linguistics","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1162/tacl.a.615","is_oa":true,"landing_page_url":"https://doi.org/10.1162/tacl.a.615","pdf_url":null,"source":{"id":"https://openalex.org/S2729999759","display_name":"Transactions of the Association for Computational Linguistics","issn_l":"2307-387X","issn":["2307-387X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320244","host_organization_name":"Association for Computational Linguistics","host_organization_lineage":["https://openalex.org/P4310320244"],"host_organization_lineage_names":["Association for Computational Linguistics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Transactions of the Association for Computational Linguistics","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.41050201654434204,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W2153225416","https://openalex.org/W2346452181","https://openalex.org/W2889787757","https://openalex.org/W2897249806","https://openalex.org/W3035375600","https://openalex.org/W3097986428","https://openalex.org/W3104748221","https://openalex.org/W3176175717","https://openalex.org/W3185341429","https://openalex.org/W3201233724","https://openalex.org/W3213460052","https://openalex.org/W4287111051","https://openalex.org/W4320813768","https://openalex.org/W4384640053","https://openalex.org/W4385570610","https://openalex.org/W4385570777","https://openalex.org/W4385571451","https://openalex.org/W4385574107","https://openalex.org/W4386443326","https://openalex.org/W4389519091","https://openalex.org/W4390682215","https://openalex.org/W4391376033","https://openalex.org/W4392487838","https://openalex.org/W4393147129","https://openalex.org/W4402671091","https://openalex.org/W4402671443","https://openalex.org/W4402671983","https://openalex.org/W4404783040","https://openalex.org/W4411119292","https://openalex.org/W4411119460","https://openalex.org/W4412888154","https://openalex.org/W4412889733","https://openalex.org/W4412889887","https://openalex.org/W4412944572","https://openalex.org/W7133215177"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"We":[1,67],"present":[2],"a":[3,12,62,83,90],"systematic":[4],"analysis":[5],"of":[6,55,73,138],"module-level":[7],"design":[8,137],"choices":[9],"in":[10,123],"GraphRAG,":[11],"retrieval-augmented":[13,139],"generation":[14],"framework":[15],"that":[16,44,70],"integrates":[17],"structured":[18],"knowledge":[19,58,75],"graphs":[20],"into":[21],"question":[22],"answering.":[23],"Focusing":[24],"on":[25,86],"triple":[26,46],"extraction,":[27],"community":[28,80],"clustering,":[29,81],"and":[30,53,95,106,109,126],"report":[31],"generation,":[32],"we":[33],"evaluate":[34],"multiple":[35],"strategies":[36],"across":[37],"two":[38],"knowledge-intensive":[39],"benchmarks.":[40],"Our":[41],"results":[42],"show":[43],"high-quality":[45],"extraction":[47],"is":[48,101],"critical,":[49],"as":[50,77],"the":[51,56,71,134],"accuracy":[52,125],"coverage":[54],"resulting":[57],"graph":[59],"can":[60],"become":[61],"bottleneck":[63],"for":[64,133],"downstream":[65,87],"reasoning.":[66,114],"also":[68],"find":[69],"granularity":[72],"fundamental":[74],"units,":[76],"determined":[78],"by":[79],"has":[82],"significant":[84],"impact":[85],"performance:":[88],"Achieving":[89],"balance":[91],"between":[92],"factual":[93],"detail":[94],"topical":[96],"coherence":[97],"within":[98],"each":[99],"unit":[100],"important":[102],"to":[103,110],"enable":[104],"precise":[105],"comprehensive":[107],"retrieval":[108],"facilitate":[111],"effective":[112],"multi-hop":[113],"In":[115],"addition,":[116],"simple":[117],"template-based":[118],"reporting":[119],"outperforms":[120],"LLM-based":[121],"summarization":[122],"both":[124],"efficiency.":[127],"These":[128],"findings":[129],"provide":[130],"practical":[131],"guidance":[132],"structure-":[135],"aware":[136],"systems.":[140]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-05-07T00:00:00"}