{"id":"https://openalex.org/W4416017062","doi":"https://doi.org/10.1145/3746252.3761000","title":"LGC-CR: Few-shot Knowledge Graph Completion via Local Global Contrastive Learning and LLM-Guided Refinement","display_name":"LGC-CR: Few-shot Knowledge Graph Completion via Local Global Contrastive Learning and LLM-Guided Refinement","publication_year":2025,"publication_date":"2025-11-07","ids":{"openalex":"https://openalex.org/W4416017062","doi":"https://doi.org/10.1145/3746252.3761000"},"language":null,"primary_location":{"id":"doi:10.1145/3746252.3761000","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761000","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yiming Xu","orcid":"https://orcid.org/0009-0003-0918-4037"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiming Xu","raw_affiliation_strings":["University of Science and Technology of China, Hefei, Anhui, China"],"raw_orcid":"https://orcid.org/0009-0003-0918-4037","affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, Anhui, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100748969","display_name":"Qi Song","orcid":"https://orcid.org/0000-0002-1726-7858"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Song","raw_affiliation_strings":["University of Science and Technology of China, Hefei, Anhui, China"],"raw_orcid":"https://orcid.org/0000-0002-1726-7858","affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, Anhui, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006897113","display_name":"Y. Wang","orcid":"https://orcid.org/0000-0002-6164-5713"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yihan Wang","raw_affiliation_strings":["University of Science and Technology of China, Hefei, Anhui, China"],"raw_orcid":"https://orcid.org/0000-0002-6164-5713","affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, Anhui, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101583468","display_name":"Wangqiu Zhou","orcid":"https://orcid.org/0000-0002-2915-4324"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wangqiu Zhou","raw_affiliation_strings":["Hefei University of Technology, Hefei, Anhui, China"],"raw_orcid":"https://orcid.org/0000-0002-2915-4324","affiliations":[{"raw_affiliation_string":"Hefei University of Technology, Hefei, Anhui, China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"last","author":{"id":null,"display_name":"Junli Liang","orcid":"https://orcid.org/0009-0001-6112-7908"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junli Liang","raw_affiliation_strings":["University of Science and Technology of China, Hefei, Anhui, China"],"raw_orcid":"https://orcid.org/0009-0001-6112-7908","affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, Anhui, China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":"3688","last_page":"3697"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9588000178337097,"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":0.9588000178337097,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.01080000028014183,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.007300000172108412,"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/spurious-relationship","display_name":"Spurious relationship","score":0.6485999822616577},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5175999999046326},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.47099998593330383},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4431000053882599},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.399399995803833},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.3732999861240387},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.32499998807907104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7799000144004822},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.6485999822616577},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5175999999046326},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4950000047683716},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.47099998593330383},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4431000053882599},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.399399995803833},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3871000111103058},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.3732999861240387},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.32510000467300415},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.30809998512268066},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.29269999265670776},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2705000042915344},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.2624000012874603}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746252.3761000","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761000","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2080133951","https://openalex.org/W2581102923","https://openalex.org/W2972771540","https://openalex.org/W2997738974","https://openalex.org/W3091993229","https://openalex.org/W3155775551","https://openalex.org/W4290989497","https://openalex.org/W4306317255","https://openalex.org/W4316661764","https://openalex.org/W4362466093","https://openalex.org/W4385562686","https://openalex.org/W4387747499","https://openalex.org/W4388144380","https://openalex.org/W4396746984","https://openalex.org/W4401754921","https://openalex.org/W4403577326","https://openalex.org/W4403582381","https://openalex.org/W4403582550","https://openalex.org/W4403582639","https://openalex.org/W4404729928","https://openalex.org/W4406367901"],"related_works":[],"abstract_inverted_index":{"Recent":[0],"years":[1],"have":[2],"witnessed":[3],"increasing":[4],"interest":[5],"in":[6],"few-shot":[7,20],"knowledge":[8,164],"graph":[9],"completion":[10],"(FKGC),":[11],"which":[12,154],"aims":[13],"to":[14,48,68,116,141,158,166,180],"infer":[15],"novel":[16,90],"query":[17],"triples":[18],"for":[19],"relations":[21],"from":[22,79],"limited":[23],"references.":[24],"Despite":[25],"promising":[26],"progress,":[27],"existing":[28],"methods":[29,45,61],"face":[30],"two":[31,134],"key":[32],"challenges:":[33],"(1)":[34],"They":[35],"often":[36],"overlook":[37],"rich":[38],"higher-order":[39],"neighbors,":[40],"while":[41,127],"traditional":[42],"high-order":[43,129],"aggregation":[44],"are":[46],"prone":[47],"introducing":[49],"noise":[50],"and":[51,81,122,161,173,195,208,213],"lack":[52],"effective":[53],"alignment":[54],"across":[55],"multi-view":[56],"neighborhood":[57],"information.":[58],"(2)":[59],"Meta-learning":[60],"over-rely":[62],"on":[63,171,210],"embeddings,":[64],"making":[65],"them":[66],"susceptible":[67],"spurious":[69],"relational":[70],"patterns.":[71],"Meanwhile,":[72],"LLM-based":[73],"methods,":[74],"despite":[75],"their":[76],"potential,":[77],"suffer":[78],"hallucinations":[80],"input":[82],"constraints.":[83],"To":[84],"this":[85],"end,":[86],"we":[87,110,148],"propose":[88],"a":[89,97,112,137,163],"framework":[91],"that":[92,191],"combines":[93],"meta-learning,":[94],"enhanced":[95],"via":[96],"Local-Global":[98],"Contrastive":[99],"network,":[100],"with":[101,202],"LLM-guided":[102],"Contextual":[103],"Refinement":[104],"(LGC-CR).":[105],"At":[106,144],"the":[107,145,182],"data":[108],"level,":[109,147],"design":[111],"local-global":[113],"contrastive":[114],"network":[115],"jointly":[117],"aggregate":[118],"relevant":[119,156],"local":[120],"features":[121],"capture":[123],"stable":[124],"global":[125],"representations":[126],"filtering":[128],"noise,":[130],"then":[131],"align":[132],"these":[133],"views":[135],"through":[136],"dual":[138],"contrast":[139],"module":[140],"ensure":[142],"consistency.":[143],"model":[146],"employ":[149],"an":[150],"LLM":[151],"refinement":[152],"module,":[153],"retrieves":[155],"contexts":[157],"construct":[159],"prompts":[160],"applies":[162],"selector":[165],"identify":[167],"high-quality":[168],"facts":[169],"based":[170],"diversity":[172],"centrality,":[174],"enabling":[175],"efficient":[176],"fine-tuning":[177],"of":[178,185,205],"LLMs":[179],"refine":[181],"preliminary":[183],"predictions":[184],"meta-learning.":[186],"The":[187],"experimental":[188],"results":[189],"demonstrate":[190],"LGC-CR":[192],"delivers":[193],"better":[194],"more":[196],"robust":[197],"performance":[198],"than":[199],"state-of-the-art":[200],"baselines,":[201],"Hit@1":[203],"improvements":[204],"8.1%,":[206],"21.7%,":[207],"20.6%":[209],"NELL,":[211],"Wiki,":[212],"FB15K,":[214],"respectively.":[215]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-08T00:00:00"}
