{"id":"https://openalex.org/W7140457844","doi":"https://doi.org/10.48550/arxiv.2603.23512","title":"S-Path-RAG: Semantic-Aware Shortest-Path Retrieval Augmented Generation for Multi-Hop Knowledge Graph Question Answering","display_name":"S-Path-RAG: Semantic-Aware Shortest-Path Retrieval Augmented Generation for Multi-Hop Knowledge Graph Question Answering","publication_year":2026,"publication_date":"2026-03-05","ids":{"openalex":"https://openalex.org/W7140457844","doi":"https://doi.org/10.48550/arxiv.2603.23512"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.23512","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23512","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.23512","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130702016","display_name":"Rong Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Fu, Rong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130701307","display_name":"Yemin Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yemin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130692184","display_name":"Tianxiang Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Tianxiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126467550","display_name":"Yongtai Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yongtai","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111252824","display_name":"Weizhi Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Weizhi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130632756","display_name":"Wangyu Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Wangyu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130715645","display_name":"Xiaowen Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Xiaowen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130669591","display_name":"Simon Fong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fong, Simon","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5130702016"],"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.3917999863624573,"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":0.3917999863624573,"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":0.33970001339912415,"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":0.19249999523162842,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.7001000046730042},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6545000076293945},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5460000038146973},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.54339998960495},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.5026000142097473},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.46880000829696655},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.3898000121116638}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8151999711990356},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.7001000046730042},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6545000076293945},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5460000038146973},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.54339998960495},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5060999989509583},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5026000142097473},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.46880000829696655},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.3898000121116638},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.38609999418258667},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3634999990463257},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35740000009536743},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3052999973297119},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.30140000581741333},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2847000062465668},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C184670325","wikidata":"https://www.wikidata.org/wiki/Q512604","display_name":"Loop (graph theory)","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C117619785","wikidata":"https://www.wikidata.org/wiki/Q6094414","display_name":"Iterative learning control","level":3,"score":0.26510000228881836},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2554999887943268}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.23512","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23512","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.23512","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23512","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":"article"},"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":{"We":[0,130,164],"present":[1],"S-Path-RAG,":[2],"a":[3,33,43,49,58,67,112],"semantic-aware":[4],"shortest-path":[5],"Retrieval-Augmented":[6],"Generation":[7],"framework":[8],"designed":[9],"to":[10,94,158],"improve":[11],"multi-hop":[12,135],"question":[13],"answering":[14],"over":[15],"large":[16],"knowledge":[17],"graphs.":[18],"S-Path-RAG":[19,132],"departs":[20],"from":[21],"one-shot,":[22],"text-heavy":[23],"retrieval":[24,103,113],"by":[25,88],"enumerating":[26],"bounded-length,":[27],"semantically":[28],"weighted":[29,35],"candidate":[30],"paths":[31],"using":[32],"hybrid":[34],"$k$-shortest,":[36],"beam,":[37],"and":[38,53,56,119,128,138,141,154,161,173,176,185],"constrained":[39,183],"random-walk":[40],"strategy,":[41],"learning":[42],"differentiable":[44],"path":[45,51,64],"scorer":[46],"together":[47],"with":[48],"contrastive":[50],"encoder":[52],"lightweight":[54],"verifier,":[55],"injecting":[57],"compact":[59],"soft":[60],"mixture":[61],"of":[62],"selected":[63],"latents":[65],"into":[66],"language":[68,90],"model":[69,91,106],"via":[70],"cross-attention.":[71],"The":[72,144],"system":[73],"runs":[74],"inside":[75],"an":[76],"iterative":[77,174],"Neural-Socratic":[78],"Graph":[79],"Dialogue":[80],"loop":[81],"in":[82,149],"which":[83],"concise":[84],"diagnostic":[85,142],"messages":[86],"produced":[87],"the":[89,105],"are":[92],"mapped":[93],"targeted":[95],"graph":[96],"edits":[97],"or":[98],"seed":[99],"expansions,":[100],"enabling":[101],"adaptive":[102],"when":[104],"expresses":[107],"uncertainty.":[108],"This":[109],"combination":[110],"yields":[111],"mechanism":[114],"that":[115],"is":[116],"both":[117],"token-efficient":[118],"topology-aware":[120],"while":[121],"preserving":[122],"interpretable":[123],"path-level":[124],"traces":[125],"for":[126,180],"diagnostics":[127],"intervention.":[129],"validate":[131],"on":[133],"standard":[134],"KGQA":[136],"benchmarks":[137],"through":[139],"ablations":[140],"analyses.":[143],"results":[145],"demonstrate":[146],"consistent":[147],"improvements":[148],"answer":[150],"accuracy,":[151],"evidence":[152],"coverage,":[153],"end-to-end":[155],"efficiency":[156],"compared":[157],"strong":[159],"graph-":[160],"LLM-based":[162],"baselines.":[163],"further":[165],"analyze":[166],"trade-offs":[167],"between":[168],"semantic":[169],"weighting,":[170],"verifier":[171],"filtering,":[172],"updates,":[175],"report":[177],"practical":[178],"recommendations":[179],"deployment":[181],"under":[182],"compute":[184],"token":[186],"budgets.":[187]},"counts_by_year":[],"updated_date":"2026-03-27T06:05:27.210665","created_date":"2026-03-27T00:00:00"}