{"id":"https://openalex.org/W4404782541","doi":"https://doi.org/10.18653/v1/2024.emnlp-main.394","title":"TimeR4 : Time-aware Retrieval-Augmented Large Language Models for Temporal Knowledge Graph Question Answering","display_name":"TimeR4 : Time-aware Retrieval-Augmented Large Language Models for Temporal Knowledge Graph Question Answering","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4404782541","doi":"https://doi.org/10.18653/v1/2024.emnlp-main.394"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2024.emnlp-main.394","doi":"https://doi.org/10.18653/v1/2024.emnlp-main.394","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.emnlp-main.394","pdf_url":"https://aclanthology.org/2024.emnlp-main.394.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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 2024 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://aclanthology.org/2024.emnlp-main.394.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101393756","display_name":"Xinying Qian","orcid":"https://orcid.org/0009-0002-8299-8275","observed_orcids":["https://orcid.org/0009-0002-8299-8275"]},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinying Qian","raw_affiliation_strings":["College of Computer Science , VCIP , TMCC, TBI Center , DISSec , Nankai University , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science , VCIP , TMCC, TBI Center , DISSec , Nankai University , China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100386001","display_name":"Ying Zhang","orcid":"https://orcid.org/0000-0001-5246-2141","observed_orcids":["https://orcid.org/0000-0001-5246-2141"]},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Zhang","raw_affiliation_strings":["College of Computer Science , VCIP , TMCC, TBI Center , DISSec , Nankai University , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science , VCIP , TMCC, TBI Center , DISSec , Nankai University , China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100433766","display_name":"Yu Zhao","orcid":"https://orcid.org/0000-0002-0326-7152","observed_orcids":["https://orcid.org/0000-0002-0326-7152"]},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Zhao","raw_affiliation_strings":["College of Computer Science , VCIP , TMCC, TBI Center , DISSec , Nankai University , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science , VCIP , TMCC, TBI Center , DISSec , Nankai University , China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049796187","display_name":"Baohang Zhou","orcid":"https://orcid.org/0000-0002-7577-6204","observed_orcids":["https://orcid.org/0000-0002-7577-6204"]},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baohang Zhou","raw_affiliation_strings":["College of Computer Science , VCIP , TMCC, TBI Center , DISSec , Nankai University , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science , VCIP , TMCC, TBI Center , DISSec , Nankai University , China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039273423","display_name":"Xuhui Sui","orcid":"https://orcid.org/0000-0001-5386-9912","observed_orcids":["https://orcid.org/0000-0001-5386-9912"]},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuhui Sui","raw_affiliation_strings":["College of Computer Science , VCIP , TMCC, TBI Center , DISSec , Nankai University , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science , VCIP , TMCC, TBI Center , DISSec , Nankai University , China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Li Zhang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Zhang","raw_affiliation_strings":["College of Computer Science , VCIP , TMCC, TBI Center , DISSec , Nankai University , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science , VCIP , TMCC, TBI Center , DISSec , Nankai University , China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"last","author":{"id":null,"display_name":"Kehui Song","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I198091727","display_name":"Tiangong University","ror":"https://ror.org/00xsr9m91","country_code":"CN","type":"education","lineage":["https://openalex.org/I198091727"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kehui Song","raw_affiliation_strings":["School of Software , Tiangong University , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software , Tiangong University , China","institution_ids":["https://openalex.org/I198091727"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.6496,"has_fulltext":true,"cited_by_count":37,"citation_normalized_percentile":{"value":0.96976472,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"6942","last_page":"6952"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.5990999937057495,"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":0.5990999937057495,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.34130001068115234,"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":0.016899999231100082,"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/temporal-knowledge-graphs","display_name":"temporal knowledge graphs","score":0.9319999814033508},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.9700000286102295},{"id":"https://openalex.org/keywords/temporal-reasoning","display_name":"temporal reasoning","score":0.7919999957084656},{"id":"https://openalex.org/keywords/retrieval-augmented-generation","display_name":"retrieval-augmented generation","score":0.8309999704360962},{"id":"https://openalex.org/keywords/question-rewriting","display_name":"question rewriting","score":0.6890000104904175},{"id":"https://openalex.org/keywords/contrastive-learning","display_name":"contrastive learning","score":0.8809999823570251},{"id":"https://openalex.org/keywords/knowledge-graph-question-answering","display_name":"knowledge graph question answering","score":0.5640000104904175}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8246479034423828},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.7834287881851196},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.5622424483299255},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5039340853691101},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.48937100172042847},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4392201900482178},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4351496398448944},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.12842443585395813}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2024.emnlp-main.394","doi":"https://doi.org/10.18653/v1/2024.emnlp-main.394","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.emnlp-main.394","pdf_url":"https://aclanthology.org/2024.emnlp-main.394.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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 2024 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2024.emnlp-main.394","doi":"https://doi.org/10.18653/v1/2024.emnlp-main.394","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.emnlp-main.394","pdf_url":"https://aclanthology.org/2024.emnlp-main.394.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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 2024 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G1519172500","display_name":null,"funder_award_id":"23JCYBJC01230","funder_id":"https://openalex.org/F4320323993","funder_display_name":"Natural Science Foundation of Tianjin City"},{"id":"https://openalex.org/G1887853828","display_name":null,"funder_award_id":"22JCQNJC01580","funder_id":"https://openalex.org/F4320323993","funder_display_name":"Natural Science Foundation of Tianjin City"},{"id":"https://openalex.org/G1950476548","display_name":null,"funder_award_id":"62302243","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5625188348","display_name":null,"funder_award_id":"62272250","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7756913708","display_name":null,"funder_award_id":"22JCJQJC00150","funder_id":"https://openalex.org/F4320323993","funder_display_name":"Natural Science Foundation of Tianjin City"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320323021","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75"},{"id":"https://openalex.org/F4320323993","display_name":"Natural Science Foundation of Tianjin City","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4404782541.pdf","grobid_xml":"https://content.openalex.org/works/W4404782541.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W2889782235","https://openalex.org/W2890961898","https://openalex.org/W2896457183","https://openalex.org/W2971155257","https://openalex.org/W2975059944","https://openalex.org/W3168292164","https://openalex.org/W3195153845","https://openalex.org/W4313483544","https://openalex.org/W4384918448","https://openalex.org/W4385570025","https://openalex.org/W4385570744","https://openalex.org/W4385571689","https://openalex.org/W4385572149","https://openalex.org/W4386566692","https://openalex.org/W4387322659","https://openalex.org/W4389520117","https://openalex.org/W4389520779","https://openalex.org/W4392669753","https://openalex.org/W4392828212","https://openalex.org/W4402671650","https://openalex.org/W4415800738","https://openalex.org/W7094459024","https://openalex.org/W7133224126"],"related_works":["https://openalex.org/W4416023945","https://openalex.org/W7124763919","https://openalex.org/W7130372872","https://openalex.org/W4402670350","https://openalex.org/W4416808686","https://openalex.org/W7167884800","https://openalex.org/W4411120186","https://openalex.org/W7138221809","https://openalex.org/W7166889782","https://openalex.org/W4415276837"],"abstract_inverted_index":{"Temporal":[0,14],"Knowledge":[1,15],"Graph":[2],"Question":[3],"Answering":[4],"(TKGQA)":[5],"aims":[6,74],"to":[7,27,36,75,94,105,139],"answer":[8],"temporal":[9,69,77,96,141,175],"questions":[10,107],"using":[11,108,149],"knowledge":[12,30,56,78,110],"in":[13,55,98,112,172],"Graphs":[16],"(TKGs).Previous":[17],"works":[18],"employ":[19],"pre-trained":[20],"TKG":[21],"embeddings":[22],"or":[23],"graph":[24,57],"neural":[25],"networks":[26],"incorporate":[28],"the":[29,39,113,134,140,150,174],"of":[31,43,162,178],"TKGs.However,":[32],"these":[33],"methods":[34],"fail":[35],"fully":[37],"understand":[38],"complex":[40],"semantic":[41,61],"information":[42],"time":[44,118],"constraints.In":[45],"contrast,":[46],"Large":[47],"Language":[48],"Models":[49],"(LLMs)":[50],"have":[51],"shown":[52],"exceptional":[53],"performance":[54],"reasoning,":[58],"unifying":[59],"both":[60],"understanding":[62],"and":[63,129,136,164],"structural":[64],"reasoning.To":[65],"further":[66],"enhance":[67],"LLMs'":[68],"reasoning":[70,176],"ability,":[71],"this":[72],"paper":[73],"integrate":[76],"from":[79,133],"TKGs":[80,135],"into":[81],"LLMs":[82],"through":[83],"a":[84,102,122,147],"Time-aware":[85],"Retrieve-Rewrite-Retrieve-Rerank":[86],"framework,":[87],"which":[88],"we":[89,100,120,145],"named":[90],"TimeR":[91],"4":[92],".Specifically,":[93],"reduce":[95],"hallucination":[97],"LLMs,":[99],"propose":[101],"retrieve-rewrite":[103],"module":[104,124],"rewrite":[106],"background":[109],"stored":[111],"TKGs,":[114],"thereby":[115],"acquiring":[116],"explicit":[117],"constraints.Then,":[119],"implement":[121],"retrievererank":[123],"aimed":[125],"at":[126,183],"retrieving":[127],"semantically":[128],"temporally":[130],"relevant":[131],"facts":[132],"reranking":[137],"according":[138],"constraints.To":[142],"achieve":[143],"this,":[144],"fine-tune":[146],"retriever":[148],"contrastive":[151],"time-aware":[152],"learning":[153],"framework.Our":[154],"approach":[155],"achieves":[156],"great":[157],"improvements,":[158],"with":[159],"relative":[160],"gains":[161],"47.8%":[163],"22.5%":[165],"on":[166],"two":[167],"datasets,":[168],"underscoring":[169],"its":[170],"effectiveness":[171],"boosting":[173],"abilities":[177],"LLMs.Our":[179],"code":[180],"is":[181],"available":[182],"https://github.com/qianxinying/TimeR4":[184],".":[185]},"counts_by_year":[{"year":2026,"cited_by_count":29},{"year":2025,"cited_by_count":8}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2024-11-28T00:00:00"}
