{"id":"https://openalex.org/W3094398869","doi":"https://doi.org/10.1145/3340531.3412073","title":"CGTR","display_name":"CGTR","publication_year":2020,"publication_date":"2020-10-19","ids":{"openalex":"https://openalex.org/W3094398869","doi":"https://doi.org/10.1145/3340531.3412073","mag":"3094398869"},"language":"en","primary_location":{"id":"doi:10.1145/3340531.3412073","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340531.3412073","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"},"type":"article","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":"https://openalex.org/A5101429088","display_name":"Yuanyuan Qi","orcid":"https://orcid.org/0000-0002-9212-6561"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuanyuan Qi","raw_affiliation_strings":["Beijing Unviersity of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Unviersity of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040333745","display_name":"Jiayue Zhang","orcid":"https://orcid.org/0000-0001-5811-7023"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiayue Zhang","raw_affiliation_strings":["Beijing University of Technology &amp; Ministry of Education China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Technology &amp; Ministry of Education China, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108004892","display_name":"Yansong Liu","orcid":"https://orcid.org/0000-0003-1858-5468"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yansong Liu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016651990","display_name":"Weiran Xu","orcid":"https://orcid.org/0000-0002-9416-7666"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiran Xu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100445470","display_name":"Jun Guo","orcid":"https://orcid.org/0000-0001-9045-1339"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Guo","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101429088"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.6628,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.76351924,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2173","last_page":"2176"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"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":1.0,"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":0.9998000264167786,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.995199978351593,"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":0.810926616191864},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6697256565093994},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4967802166938782},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4358232021331787},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4286659359931946},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.42292121052742004},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.41309767961502075},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.40231069922447205},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3885967433452606},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.323483407497406}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.810926616191864},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6697256565093994},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4967802166938782},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4358232021331787},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4286659359931946},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.42292121052742004},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.41309767961502075},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.40231069922447205},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3885967433452606},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.323483407497406},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","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:10.1145/3340531.3412073","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340531.3412073","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2519887557","https://openalex.org/W2536015822","https://openalex.org/W2782157559","https://openalex.org/W2783640434","https://openalex.org/W2788552534","https://openalex.org/W2912503608","https://openalex.org/W2923890923","https://openalex.org/W2940927814","https://openalex.org/W2945127593","https://openalex.org/W2962946486","https://openalex.org/W2963224980","https://openalex.org/W2963653811","https://openalex.org/W3040817388","https://openalex.org/W3099446234","https://openalex.org/W3102286003"],"related_works":["https://openalex.org/W2188500270","https://openalex.org/W2303858293","https://openalex.org/W2915512527","https://openalex.org/W51364034","https://openalex.org/W2793336762","https://openalex.org/W2091548507","https://openalex.org/W4321487865","https://openalex.org/W2368816706","https://openalex.org/W4313906399","https://openalex.org/W3159414774"],"abstract_inverted_index":{"Contextualized":[0],"neural":[1],"language":[2,125],"models":[3],"have":[4],"gained":[5],"much":[6],"attention":[7],"in":[8],"Information":[9],"Retrieval":[10],"(IR)":[11],"with":[12],"its":[13],"ability":[14],"to":[15,25,33,52,60,67,74,99,145],"achieve":[16,26,146],"better":[17,27],"text":[18],"understanding":[19],"by":[20],"capturing":[21],"contextual":[22,89,109,124],"structure.":[23,78],"However,":[24],"document":[28,59,63,73,137],"understanding,":[29],"it":[30],"is":[31,133,141],"necessary":[32],"involve":[34],"global":[35,54,77,111,131],"structure":[36,56,112,132],"of":[37,47,57,83],"a":[38,58,69,72,101],"document.":[39],"In":[40],"this":[41,105],"paper,":[42],"we":[43,92],"take":[44],"the":[45,76,84,97,122],"advantage":[46],"Graph":[48],"Convolutional":[49],"Networks":[50],"(GCN)":[51],"model":[53,75],"word-relation":[55],"improve":[61],"context-aware":[62],"ranking.":[64],"We":[65],"propose":[66],"build":[68],"graph":[70,85,94,98],"for":[71,135],"The":[79,114],"nodes":[80],"and":[81,104,110,139],"edges":[82],"are":[86],"constructed":[87],"from":[88],"embeddings.":[90],"Then":[91],"apply":[93],"convolution":[95],"on":[96],"learning":[100],"new":[102],"representation,":[103],"representation":[106],"covers":[107],"both":[108],"information.":[113],"experimental":[115],"results":[116],"show":[117],"that":[118,129],"our":[119],"method":[120],"outperforms":[121],"state-of-the-art":[123],"models,":[126],"which":[127],"demonstrate":[128],"incorporating":[130],"useful":[134],"improving":[136],"ranking":[138],"GCN":[140],"an":[142],"effective":[143],"way":[144],"it.":[147]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2020-10-29T00:00:00"}
