{"id":"https://openalex.org/W4416037044","doi":"https://doi.org/10.18653/v1/2025.emnlp-main.317","title":"PropRAG: Guiding Retrieval with Beam Search over Proposition Paths","display_name":"PropRAG: Guiding Retrieval with Beam Search over Proposition Paths","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416037044","doi":"https://doi.org/10.18653/v1/2025.emnlp-main.317"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.emnlp-main.317","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-main.317","pdf_url":"https://aclanthology.org/2025.emnlp-main.317.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.emnlp-main.317.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036380421","display_name":"Jingjin Wang","orcid":"https://orcid.org/0009-0003-5363-1893"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jingjin Wang","raw_affiliation_strings":["Siebel School of Computing and Data Science , University of Illinois Urbana-Champaign"],"affiliations":[{"raw_affiliation_string":"Siebel School of Computing and Data Science , University of Illinois Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019539533","display_name":"Jiawei Han","orcid":"https://orcid.org/0000-0002-3629-2696"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiawei Han","raw_affiliation_strings":["Siebel School of Computing and Data Science , University of Illinois Urbana-Champaign"],"affiliations":[{"raw_affiliation_string":"Siebel School of Computing and Data Science , University of Illinois Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5036380421"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17468581,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6223","last_page":"6238"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.4699999988079071,"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":0.4699999988079071,"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":0.24050000309944153,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.1573999971151352,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/proposition","display_name":"Proposition","score":0.7473000288009644},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6603999733924866},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6147000193595886},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.607699990272522},{"id":"https://openalex.org/keywords/knowledge-representation-and-reasoning","display_name":"Knowledge representation and reasoning","score":0.5091999769210815},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.3840000033378601},{"id":"https://openalex.org/keywords/beam-search","display_name":"Beam search","score":0.3562000095844269},{"id":"https://openalex.org/keywords/query-language","display_name":"Query language","score":0.3278999924659729}],"concepts":[{"id":"https://openalex.org/C2777152325","wikidata":"https://www.wikidata.org/wiki/Q108163","display_name":"Proposition","level":2,"score":0.7473000288009644},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6603999733924866},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6477000117301941},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6147000193595886},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.607699990272522},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.5091999769210815},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4390999972820282},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.3840000033378601},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.364300012588501},{"id":"https://openalex.org/C19889080","wikidata":"https://www.wikidata.org/wiki/Q2835852","display_name":"Beam search","level":3,"score":0.3562000095844269},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.34599998593330383},{"id":"https://openalex.org/C192028432","wikidata":"https://www.wikidata.org/wiki/Q845739","display_name":"Query language","level":2,"score":0.3278999924659729},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C62360110","wikidata":"https://www.wikidata.org/wiki/Q96777007","display_name":"Circumscription","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.25429999828338623},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.18653/v1/2025.emnlp-main.317","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-main.317","pdf_url":"https://aclanthology.org/2025.emnlp-main.317.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2504.18070","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.18070","pdf_url":"https://arxiv.org/pdf/2504.18070","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.emnlp-main.317","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-main.317","pdf_url":"https://aclanthology.org/2025.emnlp-main.317.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2063335192","display_name":null,"funder_award_id":"2019897","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6445402324","display_name":null,"funder_award_id":"IIS-19-56151","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416037044.pdf","grobid_xml":"https://content.openalex.org/works/W4416037044.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Retrieval":[0],"Augmented":[1],"Generation":[2],"(RAG)":[3],"has":[4],"become":[5],"the":[6,29,55,64,67],"standard":[7,18],"approach":[8],"for":[9,35],"equipping":[10],"Large":[11],"Language":[12],"Models":[13],"(LLMs)":[14],"with":[15,104],"up-todate":[16],"knowledge.However,":[17],"RAG,":[19],"relying":[20],"on":[21,116],"independent":[22],"passage":[23],"retrieval,":[24],"often":[25],"fails":[26],"to":[27,43,80,94],"capture":[28],"interconnected":[30],"nature":[31],"of":[32,59,66],"information":[33],"required":[34],"complex,":[36],"multi-hop":[37],"reasoning.While":[38],"structured":[39],"RAG":[40,74],"methods":[41],"attempt":[42],"address":[44],"this":[45],"using":[46],"knowledge":[47,68,102,123],"graphs":[48],"built":[49],"from":[50,78],"triples,":[51],"we":[52],"argue":[53],"that":[54,76],"inherent":[56],"context":[57],"loss":[58],"triples":[60,79],"(context":[61],"collapse)":[62],"limits":[63],"fidelity":[65],"representation.We":[69],"introduce":[70],"PropRAG,":[71],"a":[72,100],"novel":[73],"framework":[75],"shifts":[77],"context-rich":[81],"propositions":[82],"and":[83,113,119,132],"introduces":[84],"an":[85],"efficient,":[86],"LLM-free":[87],"online":[88],"beam":[89],"search":[90],"over":[91],"proposition":[92],"paths":[93],"discover":[95],"multistep":[96],"reasoning":[97,134],"chains.By":[98],"coupling":[99],"higherfidelity":[101],"representation":[103,131],"explicit":[105],"path":[106,135],"discovery,":[107],"PropRAG":[108],"achieves":[109],"state-ofthe-art":[110],"zero-shot":[111],"Recall@5":[112],"F1":[114],"scores":[115],"2Wiki,":[117],"HotpotQA,":[118],"MuSiQue,":[120],"advancing":[121],"non-parametric":[122],"integration":[124],"by":[125],"improving":[126],"evidence":[127],"retrieval":[128],"through":[129],"richer":[130],"efficient":[133],"discovery.":[136]},"counts_by_year":[],"updated_date":"2026-04-14T08:04:32.555800","created_date":"2025-10-10T00:00:00"}