乐胖代购免代理版

{"id":"https://openalex.org/W2890216969","doi":"https://doi.org/10.18653/v1/d18-1222","title":"Differentiating Concepts and Instances for Knowledge Graph Embedding","display_name":"Differentiating Concepts and Instances for Knowledge Graph Embedding","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2890216969","doi":"https://doi.org/10.18653/v1/d18-1222","mag":"2890216969"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1222","pdf_url":"https://www.aclweb.org/anthology/D18-1222.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://www.aclweb.org/anthology/D18-1222.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101400717","display_name":"Xin Lv","orcid":"https://orcid.org/0000-0002-4843-7987"},"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":"Xin Lv","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China 100084"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China 100084","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060498828","display_name":"Lei Hou","orcid":"https://orcid.org/0000-0002-8907-3526"},"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":"Lei Hou","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China 100084"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China 100084","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003324011","display_name":"Juanzi Li","orcid":"https://orcid.org/0000-0002-6244-0664"},"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":"Juanzi Li","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China 100084"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China 100084","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100320723","display_name":"Zhiyuan Liu","orcid":"https://orcid.org/0000-0002-7709-2543"},"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":"Zhiyuan Liu","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China 100084"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China 100084","institution_ids":["https://openalex.org/I99065089"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.949,"has_fulltext":true,"fulltext_origin":"pdf","cited_by_count":93,"citation_normalized_percentile":{"value":0.857952,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":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":0.9986,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.979,"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/encode","display_name":"ENCODE","score":0.6069952},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.5354066},{"id":"https://openalex.org/keywords/representation","display_name":"Representation","score":0.42917818}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7493105},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.70958227},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.6069952},{"id":"https://openalex.org/C191399111","wikidata":"https://www.wikidata.org/wiki/Q64861","display_name":"Transitive relation","level":2,"score":0.58306473},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.5354066},{"id":"https://openalex.org/C13336665","wikidata":"https://www.wikidata.org/wiki/Q125977","display_name":"Vector space","level":2,"score":0.52492934},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.48597774},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.47821113},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.451968},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.43819723},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.42917818},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38564917},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.22704378},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19698048},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"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/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},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1222","pdf_url":"https://www.aclweb.org/anthology/D18-1222.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1811.04588","pdf_url":"https://arxiv.org/pdf/1811.04588","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1222","pdf_url":"https://www.aclweb.org/anthology/D18-1222.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":30,"referenced_works":["https://openalex.org/W1426956448","https://openalex.org/W1533230146","https://openalex.org/W2022166150","https://openalex.org/W205829674","https://openalex.org/W2073587810","https://openalex.org/W2081580037","https://openalex.org/W2094728533","https://openalex.org/W2123713131","https://openalex.org/W2127426251","https://openalex.org/W2127795553","https://openalex.org/W2145544171","https://openalex.org/W2153579005","https://openalex.org/W2184957013","https://openalex.org/W2250342289","https://openalex.org/W2274308990","https://openalex.org/W2283196293","https://openalex.org/W2296268288","https://openalex.org/W2432356473","https://openalex.org/W2433281745","https://openalex.org/W2499696929","https://openalex.org/W2514852614","https://openalex.org/W2563063592","https://openalex.org/W2571811098","https://openalex.org/W2759136286","https://openalex.org/W2949972983","https://openalex.org/W2963276152","https://openalex.org/W2963432357","https://openalex.org/W2963534707","https://openalex.org/W4294170691","https://openalex.org/W4386506836"],"related_works":["https://openalex.org/W4312527695","https://openalex.org/W3103476451","https://openalex.org/W3080107865","https://openalex.org/W2912859789","https://openalex.org/W2468279273","https://openalex.org/W2361167282","https://openalex.org/W2354198838","https://openalex.org/W2091342995","https://openalex.org/W1677394555","https://openalex.org/W1528932152"],"abstract_inverted_index":{"Concepts,":[0],"which":[1],"represent":[2],"a":[3,33,51,73,79],"group":[4],"of":[5],"different":[6],"instances":[7,98],"sharing":[8],"common":[9],"properties,":[10],"are":[11],"essential":[12],"information":[13],"in":[14,32,69,81],"knowledge":[15,19,53,70],"representation.":[16],"Most":[17],"conventional":[18],"embedding":[20,55],"methods":[21],"encode":[22],"both":[23,114],"entities":[24],"(concepts":[25],"and":[26,28,44,62,75,97,100,105,117,135,142,147],"instances)":[27],"relations":[29,94,102],"as":[30,72,78],"vectors":[31],"low":[34],"dimensional":[35],"semantic":[36,84,138],"space":[37],"equally,":[38],"ignoring":[39],"the":[40,82,88,93,101,122,137],"difference":[41],"between":[42,95,103],"concepts":[43,61,96,104],"instances.":[45,63],"In":[46],"this":[47],"paper,":[48],"we":[49],"propose":[50],"novel":[52],"graph":[54,71],"model":[56,92,112],"named":[57],"TransC":[58,65,131],"by":[59],"differentiating":[60],"Specifically,":[64],"encodes":[66],"each":[67,76],"concept":[68],"sphere":[74],"instance":[77],"vector":[80],"same":[83],"space.":[85],"We":[86,109],"use":[87],"relative":[89],"positions":[90],"to":[91],"(i.e.,instanceOf),":[99],"sub-concepts":[106],"(i.e.,":[107],"subClassOf).":[108],"evaluate":[110],"our":[111],"on":[113,121,125],"link":[115],"prediction":[116],"triple":[118],"classification":[119],"tasks":[120],"dataset":[123],"based":[124],"YAGO.":[126],"Experimental":[127],"results":[128],"show":[129],"that":[130],"outperforms":[132],"state-of-the-art":[133],"methods,":[134],"captures":[136],"transitivity":[139],"for":[140],"instanceOf":[141],"subClassOf":[143],"relation.":[144],"Our":[145],"codes":[146],"datasets":[148],"can":[149],"be":[150],"obtained":[151],"from":[152],"https://github.com/davidlvxin/TransC.":[153]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2890216969","counts_by_year":[{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":17},{"year":2021,"cited_by_count":22},{"year":2020,"cited_by_count":18},{"year":2019,"cited_by_count":9}],"updated_date":"2024-12-18T16:36:37.078838","created_date":"2018-09-27"}