{"id":"https://openalex.org/W3153917096","doi":"https://doi.org/10.1145/3442381.3449943","title":"Topic-enhanced knowledge-aware retrieval model for diverse relevance estimation","display_name":"Topic-enhanced knowledge-aware retrieval model for diverse relevance estimation","publication_year":2021,"publication_date":"2021-04-19","ids":{"openalex":"https://openalex.org/W3153917096","doi":"https://doi.org/10.1145/3442381.3449943","mag":"3153917096"},"language":"en","primary_location":{"id":"doi:10.1145/3442381.3449943","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449943","pdf_url":null,"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 Web Conference 2021","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3442381.3449943","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101511516","display_name":"Xiangsheng Li","orcid":"https://orcid.org/0009-0001-1683-7054"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangsheng Li","raw_affiliation_strings":["Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072119199","display_name":"Jiaxin Mao","orcid":"https://orcid.org/0000-0002-9257-5498"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaxin Mao","raw_affiliation_strings":["Renmin University of China, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Renmin University of China, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101524043","display_name":"Weizhi Ma","orcid":"https://orcid.org/0000-0001-5604-7527"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weizhi Ma","raw_affiliation_strings":["Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100668121","display_name":"Yiqun Liu","orcid":"https://orcid.org/0000-0002-0140-4512"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiqun Liu","raw_affiliation_strings":["Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100402896","display_name":"Min Zhang","orcid":"https://orcid.org/0000-0002-2478-428X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Zhang","raw_affiliation_strings":["Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100760812","display_name":"Shaoping Ma","orcid":"https://orcid.org/0000-0002-8762-8268"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaoping Ma","raw_affiliation_strings":["Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100655132","display_name":"Zhaowei Wang","orcid":"https://orcid.org/0000-0002-7797-3316"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaowei Wang","raw_affiliation_strings":["Noah's Ark Lab, Huawei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Noah's Ark Lab, Huawei, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083350101","display_name":"Xiuqiang He","orcid":"https://orcid.org/0000-0002-4115-8205"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiuqiang He","raw_affiliation_strings":["Noah's Ark Lab, Huawei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Noah's Ark Lab, Huawei, China","institution_ids":["https://openalex.org/I2250955327"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9795,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.80198525,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"756","last_page":"767"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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":0.9998000264167786,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9984999895095825,"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"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9970999956130981,"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/computer-science","display_name":"Computer science","score":0.8002850413322449},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.7803003787994385},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.6494622230529785},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.594250500202179},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.5011932849884033},{"id":"https://openalex.org/keywords/relevance-feedback","display_name":"Relevance feedback","score":0.45550039410591125},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42338353395462036},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.41686826944351196},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.2442237138748169},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.2441769242286682}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8002850413322449},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.7803003787994385},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.6494622230529785},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.594250500202179},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.5011932849884033},{"id":"https://openalex.org/C2779532271","wikidata":"https://www.wikidata.org/wiki/Q445558","display_name":"Relevance feedback","level":4,"score":0.45550039410591125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42338353395462036},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.41686826944351196},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.2442237138748169},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.2441769242286682},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3442381.3449943","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449943","pdf_url":null,"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 Web Conference 2021","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3442381.3449943","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449943","pdf_url":null,"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 Web Conference 2021","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5699999928474426}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1972594981","https://openalex.org/W2009593660","https://openalex.org/W2014415866","https://openalex.org/W2026784708","https://openalex.org/W2042980227","https://openalex.org/W2053182313","https://openalex.org/W2057744254","https://openalex.org/W2063904635","https://openalex.org/W2127840217","https://openalex.org/W2136189984","https://openalex.org/W2148350549","https://openalex.org/W2153579005","https://openalex.org/W2170738476","https://openalex.org/W2286300105","https://openalex.org/W2402441596","https://openalex.org/W2470694366","https://openalex.org/W2525575452","https://openalex.org/W2604950042","https://openalex.org/W2621133045","https://openalex.org/W2648699835","https://openalex.org/W2708418680","https://openalex.org/W2767754357","https://openalex.org/W2799037506","https://openalex.org/W2896308983","https://openalex.org/W2896363972","https://openalex.org/W2914263187","https://openalex.org/W2944307786","https://openalex.org/W2955013659","https://openalex.org/W2962756421","https://openalex.org/W2986273318","https://openalex.org/W3015369851","https://openalex.org/W3102654612","https://openalex.org/W4231510805","https://openalex.org/W4233135949","https://openalex.org/W4238169060","https://openalex.org/W4251326898","https://openalex.org/W4252222626","https://openalex.org/W4302322961"],"related_works":["https://openalex.org/W1921936017","https://openalex.org/W2001985945","https://openalex.org/W2009716188","https://openalex.org/W1518380457","https://openalex.org/W1971071004","https://openalex.org/W1973132420","https://openalex.org/W2460037195","https://openalex.org/W2142731558","https://openalex.org/W2077213532","https://openalex.org/W2134013435"],"abstract_inverted_index":{"Relevance":[0],"measures":[1],"the":[2,23,121],"relation":[3],"between":[4,96],"query":[5,97],"and":[6,30,42,90,98,111,147,200],"document":[7],"which":[8],"contains":[9],"several":[10],"different":[11,131],"dimensions,":[12],"e.g.,":[13],"semantic":[14,40,86,198],"similarity,":[15,87],"topical":[16,91,109,157],"relatedness,":[17,54],"cognitive":[18,43],"relevance":[19,44,89,95],"(the":[20],"relations":[21],"in":[22,66,149,164],"aspect":[24],"of":[25,115,133],"knowledge),":[26],"usefulness,":[27],"timeliness,":[28],"utility":[29],"so":[31],"on.":[32],"However,":[33],"existing":[34,67,180],"retrieval":[35,80,127,181,195],"models":[36],"mainly":[37],"focus":[38],"on":[39,169],"similarity":[41,199],"while":[45],"ignore":[46],"other":[47],"possible":[48],"dimensions":[49,132],"to":[50,59,93,107,129,140,154,192],"model":[51,106,128,161,196],"relevance.":[52,134,202],"Topical":[53],"as":[55],"an":[56,165],"important":[57],"dimension":[58],"measure":[60],"relevance,":[61],"is":[62,162,190],"not":[63],"well":[64],"studied":[65],"neural":[68,104],"information":[69,110],"retrieval.":[70],"In":[71],"this":[72],"paper,":[73],"we":[74,119,136],"propose":[75],"a":[76,103,116,125,150,170],"Topic":[77],"Enhanced":[78],"Knowledge-aware":[79],"Model":[81],"(TEKM)":[82],"that":[83,177],"jointly":[84],"learns":[85],"knowledge":[88,201],"relatedness":[92,189],"estimate":[94,130],"document.":[99],"We":[100],"first":[101],"construct":[102],"topic":[105,113,122,143,188],"learn":[108],"generate":[112,155],"embeddings":[114,123,144],"query.":[117],"Then":[118],"combine":[120],"with":[124,145,197],"knowledge-aware":[126],"Specifically,":[135],"exploit":[137],"kernel":[138],"pooling":[139],"soft":[141],"match":[142],"word":[146],"entity":[148],"unified":[151],"embedding":[152],"space":[153],"fine-grained":[156],"relatedness.":[158],"The":[159],"whole":[160],"trained":[163],"end-to-end":[166],"manner.":[167],"Experiments":[168],"large-scale":[171],"publicly":[172],"available":[173],"benchmark":[174],"dataset":[175],"show":[176],"TEKM":[178],"outperforms":[179],"models.":[182],"Further":[183],"analysis":[184],"also":[185],"shows":[186],"how":[187],"modeled":[191],"improve":[193],"traditional":[194]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
