{"id":"https://openalex.org/W4306317450","doi":"https://doi.org/10.1145/3511808.3557285","title":"Dense Retrieval with Entity Views","display_name":"Dense Retrieval with Entity Views","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4306317450","doi":"https://doi.org/10.1145/3511808.3557285"},"language":"en","primary_location":{"id":"doi:10.1145/3511808.3557285","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557285","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pure.uva.nl/ws/files/132871088/3511808.3557285.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018578491","display_name":"Hai Dang Tran","orcid":"https://orcid.org/0009-0002-8548-064X"},"institutions":[{"id":"https://openalex.org/I4210109712","display_name":"Max Planck Institute for Informatics","ror":"https://ror.org/01w19ak89","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210109712"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Hai Dang Tran","raw_affiliation_strings":["Max Planck Institute for Informatics, Saarbruecken, Germany"],"affiliations":[{"raw_affiliation_string":"Max Planck Institute for Informatics, Saarbruecken, Germany","institution_ids":["https://openalex.org/I4210109712"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059489981","display_name":"Andrew Yates","orcid":"https://orcid.org/0000-0002-5970-880X"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]},{"id":"https://openalex.org/I4210135670","display_name":"Amsterdam University of the Arts","ror":"https://ror.org/04dde1554","country_code":"NL","type":"education","lineage":["https://openalex.org/I4210135670"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Andrew Yates","raw_affiliation_strings":["University of Amsterdam, Amsterdam, Netherlands"],"affiliations":[{"raw_affiliation_string":"University of Amsterdam, Amsterdam, Netherlands","institution_ids":["https://openalex.org/I4210135670","https://openalex.org/I887064364"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5018578491"],"corresponding_institution_ids":["https://openalex.org/I4210109712"],"apc_list":null,"apc_paid":null,"fwci":0.524,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.64241043,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1955","last_page":"1964"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9979000091552734,"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":0.9976000189781189,"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":0.862481951713562},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.7596986293792725},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.7407426834106445},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6653457880020142},{"id":"https://openalex.org/keywords/cover","display_name":"Cover (algebra)","score":0.4684975743293762},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4644816815853119},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3976304531097412},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38134217262268066}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.862481951713562},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.7596986293792725},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.7407426834106445},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6653457880020142},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.4684975743293762},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4644816815853119},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3976304531097412},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38134217262268066},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3511808.3557285","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557285","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:dare.uva.nl:openaire_cris_publications/2cf83114-10c5-4a0c-ac1c-a9d27f46c13d","is_oa":true,"landing_page_url":"https://handle.uba.uva.nl/personal/pure/en/publications/dense-retrieval-with-entity-views(2cf83114-10c5-4a0c-ac1c-a9d27f46c13d).html","pdf_url":"https://pure.uva.nl/ws/files/132871088/3511808.3557285.pdf","source":{"id":"https://openalex.org/S4306400088","display_name":"UvA-DARE (University of Amsterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887064364","host_organization_name":"University of Amsterdam","host_organization_lineage":["https://openalex.org/I887064364"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Tran, H D & Yates, A 2022, Dense Retrieval with Entity Views. in CIKM '22 : proceedings of the 31st ACM International Conference on Information & Knowledge Management : October 17-21, 2022, Atlanta, GA, USA. New York, NY, pp. 1955\u20131964, 31st ACM International Conference on Information and Knowledge Management, CIKM 2022, Atlanta, United States, 17/10/22. https://doi.org/10.1145/3511808.3557285","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"pmh:oai:dare.uva.nl:openaire_cris_publications/2cf83114-10c5-4a0c-ac1c-a9d27f46c13d","is_oa":true,"landing_page_url":"https://handle.uba.uva.nl/personal/pure/en/publications/dense-retrieval-with-entity-views(2cf83114-10c5-4a0c-ac1c-a9d27f46c13d).html","pdf_url":"https://pure.uva.nl/ws/files/132871088/3511808.3557285.pdf","source":{"id":"https://openalex.org/S4306400088","display_name":"UvA-DARE (University of Amsterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887064364","host_organization_name":"University of Amsterdam","host_organization_lineage":["https://openalex.org/I887064364"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Tran, H D & Yates, A 2022, Dense Retrieval with Entity Views. in CIKM '22 : proceedings of the 31st ACM International Conference on Information & Knowledge Management : October 17-21, 2022, Atlanta, GA, USA. New York, NY, pp. 1955\u20131964, 31st ACM International Conference on Information and Knowledge Management, CIKM 2022, Atlanta, United States, 17/10/22. https://doi.org/10.1145/3511808.3557285","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4306317450.pdf","grobid_xml":"https://content.openalex.org/works/W4306317450.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W102708294","https://openalex.org/W2000411838","https://openalex.org/W2022166150","https://openalex.org/W2094728533","https://openalex.org/W2648699835","https://openalex.org/W2710956079","https://openalex.org/W2895158256","https://openalex.org/W2940927814","https://openalex.org/W2945127593","https://openalex.org/W2963855739","https://openalex.org/W2971209824","https://openalex.org/W2997200074","https://openalex.org/W3021397474","https://openalex.org/W3099446234","https://openalex.org/W3099700870","https://openalex.org/W3100107515","https://openalex.org/W3102286003","https://openalex.org/W3102937497","https://openalex.org/W3146844750","https://openalex.org/W3152887675","https://openalex.org/W3154670582","https://openalex.org/W3157758108","https://openalex.org/W3217305727","https://openalex.org/W4252076394","https://openalex.org/W4284685989","https://openalex.org/W4299585995","https://openalex.org/W4400530533"],"related_works":["https://openalex.org/W1989705153","https://openalex.org/W2118564381","https://openalex.org/W2357241418","https://openalex.org/W2789919619","https://openalex.org/W2086064646","https://openalex.org/W2163901716","https://openalex.org/W2152204162","https://openalex.org/W2547476605","https://openalex.org/W2359001871","https://openalex.org/W4287629333"],"abstract_inverted_index":{"Pre-trained":[0],"language":[1,35],"models":[2,36],"like":[3],"BERT":[4],"have":[5],"been":[6],"demonstrated":[7],"to":[8,88,139],"be":[9],"both":[10],"effective":[11],"and":[12,31,56,76,134,157],"efficient":[13],"ranking":[14],"methods":[15,51],"when":[16],"combined":[17],"with":[18,59],"approximate":[19],"nearest":[20],"neighbor":[21],"search,":[22],"which":[23,84,137],"can":[24],"quickly":[25],"match":[26],"dense":[27,54,81],"representations":[28,58,112,159],"of":[29,71,93,117,130,142],"queries":[30],"documents.":[32],"However,":[33],"pretrained":[34],"alone":[37],"do":[38],"not":[39],"fully":[40],"capture":[41],"information":[42,61],"about":[43,124],"uncommon":[44],"entities.":[45],"In":[46,145],"this":[47,161],"work,":[48,136],"we":[49,104,152],"investigate":[50],"for":[52,108],"enriching":[53,155],"query":[55,156],"document":[57,123,158],"entity":[60,111],"from":[62],"an":[63,146],"external":[64],"source.":[65],"Our":[66],"proposed":[67],"method":[68],"identifies":[69],"groups":[70],"entities":[72],"in":[73,160,166],"a":[74,80,106,118,122,125],"text":[75],"encodes":[77],"them":[78],"into":[79],"vector":[82,91],"representation,":[83],"is":[85],"then":[86],"used":[87],"enrich":[89],"BERT's":[90],"representation":[92],"the":[94,143],"text.":[95],"To":[96],"handle":[97],"documents":[98],"that":[99,113,154],"contain":[100],"many":[101],"loosely-related":[102],"entities,":[103],"devise":[105],"strategy":[107],"creating":[109],"multiple":[110],"reflect":[114],"different":[115,140],"views":[116,141],"document.":[119],"For":[120],"example,":[121],"scientist":[126],"may":[127],"cover":[128],"aspects":[129],"her":[131],"personal":[132],"life":[133],"recent":[135],"correspond":[138],"entity.":[144],"evaluation":[147],"on":[148],"MS":[149],"MARCO":[150],"benchmarks,":[151],"find":[153],"way":[162],"yields":[163],"substantial":[164],"increases":[165],"effectiveness.":[167]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}
