{"id":"https://openalex.org/W2767271448","doi":"https://doi.org/10.1145/3132847.3133119","title":"Knowledge Graph Embedding with Triple Context","display_name":"Knowledge Graph Embedding with Triple Context","publication_year":2017,"publication_date":"2017-11-06","ids":{"openalex":"https://openalex.org/W2767271448","doi":"https://doi.org/10.1145/3132847.3133119","mag":"2767271448"},"language":"en","primary_location":{"id":"doi:10.1145/3132847.3133119","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3132847.3133119","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM on Conference on Information and 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/A5100690273","display_name":"Jun Shi","orcid":"https://orcid.org/0000-0003-1647-7762"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jun Shi","raw_affiliation_strings":["Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100752932","display_name":"Huan Gao","orcid":"https://orcid.org/0000-0002-2466-5547"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huan Gao","raw_affiliation_strings":["Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034606659","display_name":"Guilin Qi","orcid":"https://orcid.org/0000-0003-0150-7236"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guilin Qi","raw_affiliation_strings":["Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050566959","display_name":"Zhangquan Zhou","orcid":"https://orcid.org/0000-0002-3611-8938"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhangquan Zhou","raw_affiliation_strings":["Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100690273"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":1.9502,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.89733307,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2299","last_page":"2302"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","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/T11273","display_name":"Advanced Graph Neural Networks","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/T10028","display_name":"Topic Modeling","score":0.996399998664856,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9836999773979187,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.686452329158783},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.6422760486602783},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6411105990409851},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5971575975418091},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5727179050445557},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.42007189989089966},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34683138132095337}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.686452329158783},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.6422760486602783},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6411105990409851},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5971575975418091},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5727179050445557},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.42007189989089966},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34683138132095337},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3132847.3133119","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3132847.3133119","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W205829674","https://openalex.org/W1426956448","https://openalex.org/W1756422141","https://openalex.org/W2127426251","https://openalex.org/W2127795553","https://openalex.org/W2145544171","https://openalex.org/W2158028897","https://openalex.org/W2184957013","https://openalex.org/W2247119764","https://openalex.org/W2283196293","https://openalex.org/W2563063592","https://openalex.org/W2579831760","https://openalex.org/W2950133940"],"related_works":["https://openalex.org/W2604454537","https://openalex.org/W4206028705","https://openalex.org/W2808284704","https://openalex.org/W2883748392","https://openalex.org/W2897702399","https://openalex.org/W2757431232","https://openalex.org/W2954554213","https://openalex.org/W3200431764","https://openalex.org/W4288286922","https://openalex.org/W4206547516"],"abstract_inverted_index":{"Knowledge":[0],"graph":[1,21,35,44],"embedding,":[2],"which":[3,69,125,144],"aims":[4],"to":[5,72],"represent":[6],"entities":[7,108],"and":[8,106,112],"relations":[9,105],"in":[10,49,60,98,139,143,148],"vector":[11],"spaces,":[12],"has":[13],"shown":[14],"outstanding":[15],"performance":[16],"on":[17,29],"a":[18,33,37,67,79,119,140],"few":[19],"knowledge":[20,34,61,81],"completion":[22],"tasks.":[23],"Most":[24],"existing":[25],"methods":[26,164],"are":[27,93,137],"based":[28],"the":[30,50,99,103,113,130,162],"assumption":[31],"that":[32,158],"is":[36,102,115],"set":[38],"of":[39,58,90,109,121,124,129],"separate":[40],"triples,":[41],"ignoring":[42],"rich":[43],"features,":[45],"i.e.,":[46],"structural":[47,146],"information":[48,92,147],"graph.":[51],"In":[52],"this":[53],"paper,":[54],"we":[55,70],"take":[56],"advantages":[57],"structures":[59,65],"graphs,":[62],"especially":[63],"local":[64],"around":[66],"triple,":[68,87],"refer":[71],"as":[73,95],"triple":[74,149],"context.":[75],"We":[76],"then":[77],"propose":[78],"Triple-Context-based":[80],"Embedding":[82],"model":[83,160],"(TCE).":[84],"For":[85],"each":[86],"two":[88],"kinds":[89],"structure":[91],"considered":[94],"its":[96],"context":[97],"graph;":[100],"one":[101],"outgoing":[104],"neighboring":[107],"an":[110],"entity":[111],"other":[114],"relation":[116],"paths":[117],"between":[118],"pair":[120],"entities,":[122],"both":[123],"reflect":[126],"various":[127],"aspects":[128],"triple.":[131],"Triples":[132],"along":[133],"with":[134],"their":[135],"contexts":[136,150],"represented":[138],"unified":[141],"framework,":[142],"way":[145],"can":[151],"be":[152],"embodied.":[153],"The":[154],"experimental":[155],"results":[156],"show":[157],"our":[159],"outperforms":[161],"state-of-the-art":[163],"for":[165],"link":[166],"prediction.":[167]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}