{"id":"https://openalex.org/W3007162611","doi":"https://doi.org/10.1109/bigdata47090.2019.9005462","title":"Revisiting Text and Knowledge Graph Joint Embeddings: The Amount of Shared Information Matters!","display_name":"Revisiting Text and Knowledge Graph Joint Embeddings: The Amount of Shared Information Matters!","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3007162611","doi":"https://doi.org/10.1109/bigdata47090.2019.9005462","mag":"3007162611"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9005462","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005462","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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/A5103045601","display_name":"Paolo Rosso","orcid":"https://orcid.org/0009-0008-7739-5668"},"institutions":[{"id":"https://openalex.org/I154338468","display_name":"University of Fribourg","ror":"https://ror.org/022fs9h90","country_code":"CH","type":"education","lineage":["https://openalex.org/I154338468"]}],"countries":["CH"],"is_corresponding":true,"raw_author_name":"Paolo Rosso","raw_affiliation_strings":["eXascale Infolab, University of Fribourg, Fribourg, Switzerland"],"affiliations":[{"raw_affiliation_string":"eXascale Infolab, University of Fribourg, Fribourg, Switzerland","institution_ids":["https://openalex.org/I154338468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026625771","display_name":"Dingqi Yang","orcid":"https://orcid.org/0000-0002-6831-0422"},"institutions":[{"id":"https://openalex.org/I154338468","display_name":"University of Fribourg","ror":"https://ror.org/022fs9h90","country_code":"CH","type":"education","lineage":["https://openalex.org/I154338468"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Dingqi Yang","raw_affiliation_strings":["eXascale Infolab, University of Fribourg, Fribourg, Switzerland"],"affiliations":[{"raw_affiliation_string":"eXascale Infolab, University of Fribourg, Fribourg, Switzerland","institution_ids":["https://openalex.org/I154338468"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028454093","display_name":"Philippe Cudr\u00e9-Mauroux","orcid":"https://orcid.org/0000-0003-2588-4212"},"institutions":[{"id":"https://openalex.org/I154338468","display_name":"University of Fribourg","ror":"https://ror.org/022fs9h90","country_code":"CH","type":"education","lineage":["https://openalex.org/I154338468"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Philippe Cudre-Mauroux","raw_affiliation_strings":["eXascale Infolab, University of Fribourg, Fribourg, Switzerland"],"affiliations":[{"raw_affiliation_string":"eXascale Infolab, University of Fribourg, Fribourg, Switzerland","institution_ids":["https://openalex.org/I154338468"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103045601"],"corresponding_institution_ids":["https://openalex.org/I154338468"],"apc_list":null,"apc_paid":null,"fwci":0.5772,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.77186825,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"3","issue":null,"first_page":"2465","last_page":"2473"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","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/T11273","display_name":"Advanced Graph Neural Networks","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/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/T10203","display_name":"Recommender Systems and Techniques","score":0.9933000206947327,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7248518466949463},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.5835074782371521},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.5202397108078003},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.48667770624160767},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4189281165599823},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3952440619468689},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3391685485839844},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06911507248878479}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7248518466949463},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.5835074782371521},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5202397108078003},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.48667770624160767},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4189281165599823},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3952440619468689},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3391685485839844},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06911507248878479},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9005462","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005462","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"mag:3087126803","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002222591478427","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","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":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6700000166893005}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":69,"referenced_works":["https://openalex.org/W205829674","https://openalex.org/W1491206589","https://openalex.org/W1533230146","https://openalex.org/W1540618313","https://openalex.org/W1604644367","https://openalex.org/W1614298861","https://openalex.org/W1986321089","https://openalex.org/W2003684386","https://openalex.org/W2094728533","https://openalex.org/W2107598941","https://openalex.org/W2118020653","https://openalex.org/W2127426251","https://openalex.org/W2127795553","https://openalex.org/W2153579005","https://openalex.org/W2158028897","https://openalex.org/W2158139315","https://openalex.org/W2158899491","https://openalex.org/W2184957013","https://openalex.org/W2250342289","https://openalex.org/W2250539671","https://openalex.org/W2250635077","https://openalex.org/W2250807343","https://openalex.org/W2250930514","https://openalex.org/W2251079237","https://openalex.org/W2251960799","https://openalex.org/W2283196293","https://openalex.org/W2432356473","https://openalex.org/W2509435444","https://openalex.org/W2517640113","https://openalex.org/W2604314403","https://openalex.org/W2613223139","https://openalex.org/W2728059831","https://openalex.org/W2759136286","https://openalex.org/W2788798739","https://openalex.org/W2888572441","https://openalex.org/W2891522705","https://openalex.org/W2918636532","https://openalex.org/W2951006102","https://openalex.org/W2951723246","https://openalex.org/W2952230511","https://openalex.org/W2963203544","https://openalex.org/W2963432357","https://openalex.org/W2963855739","https://openalex.org/W2964116313","https://openalex.org/W2964279602","https://openalex.org/W2998704965","https://openalex.org/W4285719527","https://openalex.org/W4288916574","https://openalex.org/W4294170691","https://openalex.org/W4297814433","https://openalex.org/W6608344535","https://openalex.org/W6629506918","https://openalex.org/W6631964550","https://openalex.org/W6636320352","https://openalex.org/W6636510571","https://openalex.org/W6638311095","https://openalex.org/W6678830454","https://openalex.org/W6678846912","https://openalex.org/W6680532216","https://openalex.org/W6682691769","https://openalex.org/W6683557909","https://openalex.org/W6683738474","https://openalex.org/W6686133869","https://openalex.org/W6691633450","https://openalex.org/W6695596964","https://openalex.org/W6718112784","https://openalex.org/W6740216407","https://openalex.org/W6753562260","https://openalex.org/W6754461253"],"related_works":["https://openalex.org/W1996130883","https://openalex.org/W2748574964","https://openalex.org/W2888483922","https://openalex.org/W2367747139","https://openalex.org/W4391102217","https://openalex.org/W2566187525","https://openalex.org/W2566334511","https://openalex.org/W2367150592","https://openalex.org/W2378889330","https://openalex.org/W2054026175"],"abstract_inverted_index":{"Jointly":[0],"learning":[1,57,62,94,99,173,210,235],"embeddings":[2,14],"from":[3,212],"text":[4,47,121,138],"and":[5,12,24,122,140,201,228],"a":[6,118,137,145,213],"Knowledge":[7,29,40,123,146],"Graph":[8,41,124],"benefits":[9],"both":[10,19],"word":[11],"entity/relation":[13],"by":[15],"taking":[16],"advantage":[17],"of":[18,83,111,157,190,215],"large-scale":[20],"unstructured":[21],"content":[22],"(text)":[23],"high-quality":[25],"structured":[26],"data":[27,89,163],"(the":[28],"Graph).":[30],"Current":[31],"techniques":[32,72,207],"leverage":[33],"anchors":[34,50],"to":[35,42,54,79,153,205,224],"associate":[36],"entities":[37,143],"in":[38,45,67,136,144],"the":[39,46,60,81,87,92,97,109,151,155,161,188],"corresponding":[43],"words":[44,135],"corpus;":[48],"these":[49],"are":[51],"then":[52],"used":[53],"generate":[55,171],"additional":[56,98,172,209],"samples":[58,100,211],"during":[59,91],"embedding":[61,125],"process.":[63,95],"However,":[64],"we":[65,115],"show":[66],"this":[68],"paper":[69],"that":[70],"such":[71,112],"yield":[73],"suboptimal":[74],"results,":[75],"as":[76],"they":[77],"fail":[78],"control":[80,154],"amount":[82,156],"shared":[84,159],"information":[85,158],"between":[86,134,142,160],"two":[88,162],"sources":[90,164],"joint":[93,113,120],"Moreover,":[96],"often":[101],"incur":[102],"significant":[103],"computational":[104,231],"overhead.":[105],"Aiming":[106],"at":[107],"releasing":[108],"power":[110],"embeddings,":[114],"propose":[116],"JOINER,":[117],"new":[119],"method":[126,168,218],"using":[127],"regularization.":[128,166],"JOINER":[129,191],"not":[130,170],"only":[131],"preserves":[132],"co-occurrence":[133],"corpus":[139],"relations":[141],"Graph,":[147],"it":[148,177],"also":[149],"provides":[150],"flexibility":[152],"via":[165],"Our":[167,180],"does":[169],"samples,":[174],"which":[175],"makes":[176],"computationally":[178],"efficient.":[179],"extensive":[181],"empirical":[182],"evaluation":[183,194],"on":[184],"real":[185],"datasets":[186],"shows":[187],"superiority":[189],"across":[192],"different":[193],"tasks,":[195],"including":[196],"analogical":[197],"reasoning,":[198],"link":[199],"prediction,":[200],"relation":[202],"extraction.":[203],"Compared":[204],"state-of-the-art":[206],"generating":[208],"set":[214],"anchors,":[216],"our":[217],"yields":[219],"better":[220],"results":[221],"(with":[222],"up":[223],"4.3%":[225],"absolute":[226],"improvement)":[227],"significantly":[229],"less":[230,234],"overhead":[232],"(76%":[233],"time":[236],"overhead).":[237]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}