Temporal Knowledge Graph Representation Learning
As a structured form of human knowledge,knowledge graphs have played a great supportive role in supporting the semantic intercommunication of massive,multi-source,heterogeneous data,and effectively support tasks such as data analysis,attracting the attention of academia and industry.At present,most...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Chinesisch |
| Erschienen: |
2022 |
|---|
| Schlagwörter: |
knowledge graph|deep learning|representation learning|temporal information|dynamic process |
|---|
| Übergeordnetes Werk: |
In: Jisuanji kexue - Editorial office of Computer Science, 2021, 49(2022), 9, Seite 162-171 |
|---|---|
| Übergeordnetes Werk: |
volume:49 ; year:2022 ; number:9 ; pages:162-171 |
| Links: |
|---|
| DOI / URN: |
10.11896/jsjkx.220500204 |
|---|
| Katalog-ID: |
DOAJ08919540X |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | DOAJ08919540X | ||
| 003 | DE-627 | ||
| 005 | 20230505020055.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 230505s2022 xx |||||o 00| ||chi c | ||
| 024 | 7 | |a 10.11896/jsjkx.220500204 |2 doi | |
| 035 | |a (DE-627)DOAJ08919540X | ||
| 035 | |a (DE-599)DOAJ7708422af1f7485d99343ec4f6322886 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a chi | ||
| 050 | 0 | |a QA76.75-76.765 | |
| 050 | 0 | |a T1-995 | |
| 100 | 0 | |a XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Temporal Knowledge Graph Representation Learning |
| 264 | 1 | |c 2022 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a As a structured form of human knowledge,knowledge graphs have played a great supportive role in supporting the semantic intercommunication of massive,multi-source,heterogeneous data,and effectively support tasks such as data analysis,attracting the attention of academia and industry.At present,most knowledge graphs are constructed based on non-real-time static data,without considering the temporal characteristics of entities and relationships.However,data in application scenarios such as social network communication,financial trade,and epidemic spreading network are highly dynamic and exhibit complex temporal properties.How to use time series data to build knowledge graphs and effectively model them is a challenging problem.Recently,numerous studies use temporal information in time series data to enrich the characteristics of knowledge graphs,endowing know-ledge graphs with dynamic features,expanding fact triples into quadruple representation(head entity,relationship,tail entity,time).The knowledge graph which utilizes time-related quadruples to represent knowledge are collectively referred to as temporal knowledge graph.This paper summarizes the research work of temporal knowledge graph representation learning by sorting out and analyzing the corresponding literature.Specifically,it first briefly introduce the background and definition of temporal know-ledge graph.Next,it summarizes the advantages of the temporal knowledge graph representation learning method compared with the traditional knowledge graph representation learning method.Then it elaborates on the recent method of temporal knowledge graph representation learning from the perspective of the method modeling facts,introduces the dataset used by the above method and summarizes the main challenges of this technology.Finally,the future research direction is prospected. | ||
| 650 | 4 | |a knowledge graph|deep learning|representation learning|temporal information|dynamic process | |
| 653 | 0 | |a Computer software | |
| 653 | 0 | |a Technology (General) | |
| 773 | 0 | 8 | |i In |t Jisuanji kexue |d Editorial office of Computer Science, 2021 |g 49(2022), 9, Seite 162-171 |w (DE-627)DOAJ078619254 |x 1002137X |7 nnns |
| 773 | 1 | 8 | |g volume:49 |g year:2022 |g number:9 |g pages:162-171 |
| 856 | 4 | 0 | |u https://doi.org/10.11896/jsjkx.220500204 |z kostenfrei |
| 856 | 4 | 0 | |u https://doaj.org/article/7708422af1f7485d99343ec4f6322886 |z kostenfrei |
| 856 | 4 | 0 | |u https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-9-162.pdf |z kostenfrei |
| 856 | 4 | 2 | |u https://doaj.org/toc/1002-137X |y Journal toc |z kostenfrei |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_DOAJ | ||
| 951 | |a AR | ||
| 952 | |d 49 |j 2022 |e 9 |h 162-171 | ||
| author_variant |
y x z j f w y s x b y k x yxzjfwysxbyk yxzjfwysxbykx |
|---|---|
| matchkey_str |
article:1002137X:2022----::eprlnweggahersna |
| hierarchy_sort_str |
2022 |
| callnumber-subject-code |
QA |
| publishDate |
2022 |
| allfields |
10.11896/jsjkx.220500204 doi (DE-627)DOAJ08919540X (DE-599)DOAJ7708422af1f7485d99343ec4f6322886 DE-627 ger DE-627 rakwb chi QA76.75-76.765 T1-995 XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai verfasserin aut Temporal Knowledge Graph Representation Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a structured form of human knowledge,knowledge graphs have played a great supportive role in supporting the semantic intercommunication of massive,multi-source,heterogeneous data,and effectively support tasks such as data analysis,attracting the attention of academia and industry.At present,most knowledge graphs are constructed based on non-real-time static data,without considering the temporal characteristics of entities and relationships.However,data in application scenarios such as social network communication,financial trade,and epidemic spreading network are highly dynamic and exhibit complex temporal properties.How to use time series data to build knowledge graphs and effectively model them is a challenging problem.Recently,numerous studies use temporal information in time series data to enrich the characteristics of knowledge graphs,endowing know-ledge graphs with dynamic features,expanding fact triples into quadruple representation(head entity,relationship,tail entity,time).The knowledge graph which utilizes time-related quadruples to represent knowledge are collectively referred to as temporal knowledge graph.This paper summarizes the research work of temporal knowledge graph representation learning by sorting out and analyzing the corresponding literature.Specifically,it first briefly introduce the background and definition of temporal know-ledge graph.Next,it summarizes the advantages of the temporal knowledge graph representation learning method compared with the traditional knowledge graph representation learning method.Then it elaborates on the recent method of temporal knowledge graph representation learning from the perspective of the method modeling facts,introduces the dataset used by the above method and summarizes the main challenges of this technology.Finally,the future research direction is prospected. knowledge graph|deep learning|representation learning|temporal information|dynamic process Computer software Technology (General) In Jisuanji kexue Editorial office of Computer Science, 2021 49(2022), 9, Seite 162-171 (DE-627)DOAJ078619254 1002137X nnns volume:49 year:2022 number:9 pages:162-171 https://doi.org/10.11896/jsjkx.220500204 kostenfrei https://doaj.org/article/7708422af1f7485d99343ec4f6322886 kostenfrei https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-9-162.pdf kostenfrei https://doaj.org/toc/1002-137X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 49 2022 9 162-171 |
| spelling |
10.11896/jsjkx.220500204 doi (DE-627)DOAJ08919540X (DE-599)DOAJ7708422af1f7485d99343ec4f6322886 DE-627 ger DE-627 rakwb chi QA76.75-76.765 T1-995 XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai verfasserin aut Temporal Knowledge Graph Representation Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a structured form of human knowledge,knowledge graphs have played a great supportive role in supporting the semantic intercommunication of massive,multi-source,heterogeneous data,and effectively support tasks such as data analysis,attracting the attention of academia and industry.At present,most knowledge graphs are constructed based on non-real-time static data,without considering the temporal characteristics of entities and relationships.However,data in application scenarios such as social network communication,financial trade,and epidemic spreading network are highly dynamic and exhibit complex temporal properties.How to use time series data to build knowledge graphs and effectively model them is a challenging problem.Recently,numerous studies use temporal information in time series data to enrich the characteristics of knowledge graphs,endowing know-ledge graphs with dynamic features,expanding fact triples into quadruple representation(head entity,relationship,tail entity,time).The knowledge graph which utilizes time-related quadruples to represent knowledge are collectively referred to as temporal knowledge graph.This paper summarizes the research work of temporal knowledge graph representation learning by sorting out and analyzing the corresponding literature.Specifically,it first briefly introduce the background and definition of temporal know-ledge graph.Next,it summarizes the advantages of the temporal knowledge graph representation learning method compared with the traditional knowledge graph representation learning method.Then it elaborates on the recent method of temporal knowledge graph representation learning from the perspective of the method modeling facts,introduces the dataset used by the above method and summarizes the main challenges of this technology.Finally,the future research direction is prospected. knowledge graph|deep learning|representation learning|temporal information|dynamic process Computer software Technology (General) In Jisuanji kexue Editorial office of Computer Science, 2021 49(2022), 9, Seite 162-171 (DE-627)DOAJ078619254 1002137X nnns volume:49 year:2022 number:9 pages:162-171 https://doi.org/10.11896/jsjkx.220500204 kostenfrei https://doaj.org/article/7708422af1f7485d99343ec4f6322886 kostenfrei https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-9-162.pdf kostenfrei https://doaj.org/toc/1002-137X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 49 2022 9 162-171 |
| allfields_unstemmed |
10.11896/jsjkx.220500204 doi (DE-627)DOAJ08919540X (DE-599)DOAJ7708422af1f7485d99343ec4f6322886 DE-627 ger DE-627 rakwb chi QA76.75-76.765 T1-995 XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai verfasserin aut Temporal Knowledge Graph Representation Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a structured form of human knowledge,knowledge graphs have played a great supportive role in supporting the semantic intercommunication of massive,multi-source,heterogeneous data,and effectively support tasks such as data analysis,attracting the attention of academia and industry.At present,most knowledge graphs are constructed based on non-real-time static data,without considering the temporal characteristics of entities and relationships.However,data in application scenarios such as social network communication,financial trade,and epidemic spreading network are highly dynamic and exhibit complex temporal properties.How to use time series data to build knowledge graphs and effectively model them is a challenging problem.Recently,numerous studies use temporal information in time series data to enrich the characteristics of knowledge graphs,endowing know-ledge graphs with dynamic features,expanding fact triples into quadruple representation(head entity,relationship,tail entity,time).The knowledge graph which utilizes time-related quadruples to represent knowledge are collectively referred to as temporal knowledge graph.This paper summarizes the research work of temporal knowledge graph representation learning by sorting out and analyzing the corresponding literature.Specifically,it first briefly introduce the background and definition of temporal know-ledge graph.Next,it summarizes the advantages of the temporal knowledge graph representation learning method compared with the traditional knowledge graph representation learning method.Then it elaborates on the recent method of temporal knowledge graph representation learning from the perspective of the method modeling facts,introduces the dataset used by the above method and summarizes the main challenges of this technology.Finally,the future research direction is prospected. knowledge graph|deep learning|representation learning|temporal information|dynamic process Computer software Technology (General) In Jisuanji kexue Editorial office of Computer Science, 2021 49(2022), 9, Seite 162-171 (DE-627)DOAJ078619254 1002137X nnns volume:49 year:2022 number:9 pages:162-171 https://doi.org/10.11896/jsjkx.220500204 kostenfrei https://doaj.org/article/7708422af1f7485d99343ec4f6322886 kostenfrei https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-9-162.pdf kostenfrei https://doaj.org/toc/1002-137X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 49 2022 9 162-171 |
| allfieldsGer |
10.11896/jsjkx.220500204 doi (DE-627)DOAJ08919540X (DE-599)DOAJ7708422af1f7485d99343ec4f6322886 DE-627 ger DE-627 rakwb chi QA76.75-76.765 T1-995 XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai verfasserin aut Temporal Knowledge Graph Representation Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a structured form of human knowledge,knowledge graphs have played a great supportive role in supporting the semantic intercommunication of massive,multi-source,heterogeneous data,and effectively support tasks such as data analysis,attracting the attention of academia and industry.At present,most knowledge graphs are constructed based on non-real-time static data,without considering the temporal characteristics of entities and relationships.However,data in application scenarios such as social network communication,financial trade,and epidemic spreading network are highly dynamic and exhibit complex temporal properties.How to use time series data to build knowledge graphs and effectively model them is a challenging problem.Recently,numerous studies use temporal information in time series data to enrich the characteristics of knowledge graphs,endowing know-ledge graphs with dynamic features,expanding fact triples into quadruple representation(head entity,relationship,tail entity,time).The knowledge graph which utilizes time-related quadruples to represent knowledge are collectively referred to as temporal knowledge graph.This paper summarizes the research work of temporal knowledge graph representation learning by sorting out and analyzing the corresponding literature.Specifically,it first briefly introduce the background and definition of temporal know-ledge graph.Next,it summarizes the advantages of the temporal knowledge graph representation learning method compared with the traditional knowledge graph representation learning method.Then it elaborates on the recent method of temporal knowledge graph representation learning from the perspective of the method modeling facts,introduces the dataset used by the above method and summarizes the main challenges of this technology.Finally,the future research direction is prospected. knowledge graph|deep learning|representation learning|temporal information|dynamic process Computer software Technology (General) In Jisuanji kexue Editorial office of Computer Science, 2021 49(2022), 9, Seite 162-171 (DE-627)DOAJ078619254 1002137X nnns volume:49 year:2022 number:9 pages:162-171 https://doi.org/10.11896/jsjkx.220500204 kostenfrei https://doaj.org/article/7708422af1f7485d99343ec4f6322886 kostenfrei https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-9-162.pdf kostenfrei https://doaj.org/toc/1002-137X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 49 2022 9 162-171 |
| allfieldsSound |
10.11896/jsjkx.220500204 doi (DE-627)DOAJ08919540X (DE-599)DOAJ7708422af1f7485d99343ec4f6322886 DE-627 ger DE-627 rakwb chi QA76.75-76.765 T1-995 XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai verfasserin aut Temporal Knowledge Graph Representation Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a structured form of human knowledge,knowledge graphs have played a great supportive role in supporting the semantic intercommunication of massive,multi-source,heterogeneous data,and effectively support tasks such as data analysis,attracting the attention of academia and industry.At present,most knowledge graphs are constructed based on non-real-time static data,without considering the temporal characteristics of entities and relationships.However,data in application scenarios such as social network communication,financial trade,and epidemic spreading network are highly dynamic and exhibit complex temporal properties.How to use time series data to build knowledge graphs and effectively model them is a challenging problem.Recently,numerous studies use temporal information in time series data to enrich the characteristics of knowledge graphs,endowing know-ledge graphs with dynamic features,expanding fact triples into quadruple representation(head entity,relationship,tail entity,time).The knowledge graph which utilizes time-related quadruples to represent knowledge are collectively referred to as temporal knowledge graph.This paper summarizes the research work of temporal knowledge graph representation learning by sorting out and analyzing the corresponding literature.Specifically,it first briefly introduce the background and definition of temporal know-ledge graph.Next,it summarizes the advantages of the temporal knowledge graph representation learning method compared with the traditional knowledge graph representation learning method.Then it elaborates on the recent method of temporal knowledge graph representation learning from the perspective of the method modeling facts,introduces the dataset used by the above method and summarizes the main challenges of this technology.Finally,the future research direction is prospected. knowledge graph|deep learning|representation learning|temporal information|dynamic process Computer software Technology (General) In Jisuanji kexue Editorial office of Computer Science, 2021 49(2022), 9, Seite 162-171 (DE-627)DOAJ078619254 1002137X nnns volume:49 year:2022 number:9 pages:162-171 https://doi.org/10.11896/jsjkx.220500204 kostenfrei https://doaj.org/article/7708422af1f7485d99343ec4f6322886 kostenfrei https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-9-162.pdf kostenfrei https://doaj.org/toc/1002-137X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 49 2022 9 162-171 |
| language |
Chinese |
| source |
In Jisuanji kexue 49(2022), 9, Seite 162-171 volume:49 year:2022 number:9 pages:162-171 |
| sourceStr |
In Jisuanji kexue 49(2022), 9, Seite 162-171 volume:49 year:2022 number:9 pages:162-171 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
knowledge graph|deep learning|representation learning|temporal information|dynamic process Computer software Technology (General) |
| isfreeaccess_bool |
true |
| container_title |
Jisuanji kexue |
| authorswithroles_txt_mv |
XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai @@aut@@ |
| publishDateDaySort_date |
2022-01-01T00:00:00Z |
| hierarchy_top_id |
DOAJ078619254 |
| id |
DOAJ08919540X |
| language_de |
chinesisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ08919540X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505020055.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230505s2022 xx |||||o 00| ||chi c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.11896/jsjkx.220500204</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ08919540X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ7708422af1f7485d99343ec4f6322886</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">chi</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA76.75-76.765</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">T1-995</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Temporal Knowledge Graph Representation Learning</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">As a structured form of human knowledge,knowledge graphs have played a great supportive role in supporting the semantic intercommunication of massive,multi-source,heterogeneous data,and effectively support tasks such as data analysis,attracting the attention of academia and industry.At present,most knowledge graphs are constructed based on non-real-time static data,without considering the temporal characteristics of entities and relationships.However,data in application scenarios such as social network communication,financial trade,and epidemic spreading network are highly dynamic and exhibit complex temporal properties.How to use time series data to build knowledge graphs and effectively model them is a challenging problem.Recently,numerous studies use temporal information in time series data to enrich the characteristics of knowledge graphs,endowing know-ledge graphs with dynamic features,expanding fact triples into quadruple representation(head entity,relationship,tail entity,time).The knowledge graph which utilizes time-related quadruples to represent knowledge are collectively referred to as temporal knowledge graph.This paper summarizes the research work of temporal knowledge graph representation learning by sorting out and analyzing the corresponding literature.Specifically,it first briefly introduce the background and definition of temporal know-ledge graph.Next,it summarizes the advantages of the temporal knowledge graph representation learning method compared with the traditional knowledge graph representation learning method.Then it elaborates on the recent method of temporal knowledge graph representation learning from the perspective of the method modeling facts,introduces the dataset used by the above method and summarizes the main challenges of this technology.Finally,the future research direction is prospected.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">knowledge graph|deep learning|representation learning|temporal information|dynamic process</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Computer software</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology (General)</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Jisuanji kexue</subfield><subfield code="d">Editorial office of Computer Science, 2021</subfield><subfield code="g">49(2022), 9, Seite 162-171</subfield><subfield code="w">(DE-627)DOAJ078619254</subfield><subfield code="x">1002137X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:49</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:9</subfield><subfield code="g">pages:162-171</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.11896/jsjkx.220500204</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/7708422af1f7485d99343ec4f6322886</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-9-162.pdf</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1002-137X</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">49</subfield><subfield code="j">2022</subfield><subfield code="e">9</subfield><subfield code="h">162-171</subfield></datafield></record></collection>
|
| callnumber-first |
Q - Science |
| author |
XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai |
| spellingShingle |
XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai misc QA76.75-76.765 misc T1-995 misc knowledge graph|deep learning|representation learning|temporal information|dynamic process misc Computer software misc Technology (General) Temporal Knowledge Graph Representation Learning |
| authorStr |
XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)DOAJ078619254 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
DOAJ |
| remote_str |
true |
| callnumber-label |
QA76 |
| illustrated |
Not Illustrated |
| issn |
1002137X |
| topic_title |
QA76.75-76.765 T1-995 Temporal Knowledge Graph Representation Learning knowledge graph|deep learning|representation learning|temporal information|dynamic process |
| topic |
misc QA76.75-76.765 misc T1-995 misc knowledge graph|deep learning|representation learning|temporal information|dynamic process misc Computer software misc Technology (General) |
| topic_unstemmed |
misc QA76.75-76.765 misc T1-995 misc knowledge graph|deep learning|representation learning|temporal information|dynamic process misc Computer software misc Technology (General) |
| topic_browse |
misc QA76.75-76.765 misc T1-995 misc knowledge graph|deep learning|representation learning|temporal information|dynamic process misc Computer software misc Technology (General) |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Jisuanji kexue |
| hierarchy_parent_id |
DOAJ078619254 |
| hierarchy_top_title |
Jisuanji kexue |
| isfreeaccess_txt |
true |
| familylinks_str_mv |
(DE-627)DOAJ078619254 |
| title |
Temporal Knowledge Graph Representation Learning |
| ctrlnum |
(DE-627)DOAJ08919540X (DE-599)DOAJ7708422af1f7485d99343ec4f6322886 |
| title_full |
Temporal Knowledge Graph Representation Learning |
| author_sort |
XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai |
| journal |
Jisuanji kexue |
| journalStr |
Jisuanji kexue |
| callnumber-first-code |
Q |
| lang_code |
chi |
| isOA_bool |
true |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
txt |
| container_start_page |
162 |
| author_browse |
XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai |
| container_volume |
49 |
| class |
QA76.75-76.765 T1-995 |
| format_se |
Elektronische Aufsätze |
| author-letter |
XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai |
| doi_str_mv |
10.11896/jsjkx.220500204 |
| title_sort |
temporal knowledge graph representation learning |
| callnumber |
QA76.75-76.765 |
| title_auth |
Temporal Knowledge Graph Representation Learning |
| abstract |
As a structured form of human knowledge,knowledge graphs have played a great supportive role in supporting the semantic intercommunication of massive,multi-source,heterogeneous data,and effectively support tasks such as data analysis,attracting the attention of academia and industry.At present,most knowledge graphs are constructed based on non-real-time static data,without considering the temporal characteristics of entities and relationships.However,data in application scenarios such as social network communication,financial trade,and epidemic spreading network are highly dynamic and exhibit complex temporal properties.How to use time series data to build knowledge graphs and effectively model them is a challenging problem.Recently,numerous studies use temporal information in time series data to enrich the characteristics of knowledge graphs,endowing know-ledge graphs with dynamic features,expanding fact triples into quadruple representation(head entity,relationship,tail entity,time).The knowledge graph which utilizes time-related quadruples to represent knowledge are collectively referred to as temporal knowledge graph.This paper summarizes the research work of temporal knowledge graph representation learning by sorting out and analyzing the corresponding literature.Specifically,it first briefly introduce the background and definition of temporal know-ledge graph.Next,it summarizes the advantages of the temporal knowledge graph representation learning method compared with the traditional knowledge graph representation learning method.Then it elaborates on the recent method of temporal knowledge graph representation learning from the perspective of the method modeling facts,introduces the dataset used by the above method and summarizes the main challenges of this technology.Finally,the future research direction is prospected. |
| abstractGer |
As a structured form of human knowledge,knowledge graphs have played a great supportive role in supporting the semantic intercommunication of massive,multi-source,heterogeneous data,and effectively support tasks such as data analysis,attracting the attention of academia and industry.At present,most knowledge graphs are constructed based on non-real-time static data,without considering the temporal characteristics of entities and relationships.However,data in application scenarios such as social network communication,financial trade,and epidemic spreading network are highly dynamic and exhibit complex temporal properties.How to use time series data to build knowledge graphs and effectively model them is a challenging problem.Recently,numerous studies use temporal information in time series data to enrich the characteristics of knowledge graphs,endowing know-ledge graphs with dynamic features,expanding fact triples into quadruple representation(head entity,relationship,tail entity,time).The knowledge graph which utilizes time-related quadruples to represent knowledge are collectively referred to as temporal knowledge graph.This paper summarizes the research work of temporal knowledge graph representation learning by sorting out and analyzing the corresponding literature.Specifically,it first briefly introduce the background and definition of temporal know-ledge graph.Next,it summarizes the advantages of the temporal knowledge graph representation learning method compared with the traditional knowledge graph representation learning method.Then it elaborates on the recent method of temporal knowledge graph representation learning from the perspective of the method modeling facts,introduces the dataset used by the above method and summarizes the main challenges of this technology.Finally,the future research direction is prospected. |
| abstract_unstemmed |
As a structured form of human knowledge,knowledge graphs have played a great supportive role in supporting the semantic intercommunication of massive,multi-source,heterogeneous data,and effectively support tasks such as data analysis,attracting the attention of academia and industry.At present,most knowledge graphs are constructed based on non-real-time static data,without considering the temporal characteristics of entities and relationships.However,data in application scenarios such as social network communication,financial trade,and epidemic spreading network are highly dynamic and exhibit complex temporal properties.How to use time series data to build knowledge graphs and effectively model them is a challenging problem.Recently,numerous studies use temporal information in time series data to enrich the characteristics of knowledge graphs,endowing know-ledge graphs with dynamic features,expanding fact triples into quadruple representation(head entity,relationship,tail entity,time).The knowledge graph which utilizes time-related quadruples to represent knowledge are collectively referred to as temporal knowledge graph.This paper summarizes the research work of temporal knowledge graph representation learning by sorting out and analyzing the corresponding literature.Specifically,it first briefly introduce the background and definition of temporal know-ledge graph.Next,it summarizes the advantages of the temporal knowledge graph representation learning method compared with the traditional knowledge graph representation learning method.Then it elaborates on the recent method of temporal knowledge graph representation learning from the perspective of the method modeling facts,introduces the dataset used by the above method and summarizes the main challenges of this technology.Finally,the future research direction is prospected. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ |
| container_issue |
9 |
| title_short |
Temporal Knowledge Graph Representation Learning |
| url |
https://doi.org/10.11896/jsjkx.220500204 https://doaj.org/article/7708422af1f7485d99343ec4f6322886 https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-9-162.pdf https://doaj.org/toc/1002-137X |
| remote_bool |
true |
| ppnlink |
DOAJ078619254 |
| callnumber-subject |
QA - Mathematics |
| mediatype_str_mv |
c |
| isOA_txt |
true |
| hochschulschrift_bool |
false |
| doi_str |
10.11896/jsjkx.220500204 |
| callnumber-a |
QA76.75-76.765 |
| up_date |
2024-07-03T21:48:17.408Z |
| _version_ |
1803596120624988160 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ08919540X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505020055.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230505s2022 xx |||||o 00| ||chi c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.11896/jsjkx.220500204</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ08919540X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ7708422af1f7485d99343ec4f6322886</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">chi</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA76.75-76.765</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">T1-995</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Temporal Knowledge Graph Representation Learning</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">As a structured form of human knowledge,knowledge graphs have played a great supportive role in supporting the semantic intercommunication of massive,multi-source,heterogeneous data,and effectively support tasks such as data analysis,attracting the attention of academia and industry.At present,most knowledge graphs are constructed based on non-real-time static data,without considering the temporal characteristics of entities and relationships.However,data in application scenarios such as social network communication,financial trade,and epidemic spreading network are highly dynamic and exhibit complex temporal properties.How to use time series data to build knowledge graphs and effectively model them is a challenging problem.Recently,numerous studies use temporal information in time series data to enrich the characteristics of knowledge graphs,endowing know-ledge graphs with dynamic features,expanding fact triples into quadruple representation(head entity,relationship,tail entity,time).The knowledge graph which utilizes time-related quadruples to represent knowledge are collectively referred to as temporal knowledge graph.This paper summarizes the research work of temporal knowledge graph representation learning by sorting out and analyzing the corresponding literature.Specifically,it first briefly introduce the background and definition of temporal know-ledge graph.Next,it summarizes the advantages of the temporal knowledge graph representation learning method compared with the traditional knowledge graph representation learning method.Then it elaborates on the recent method of temporal knowledge graph representation learning from the perspective of the method modeling facts,introduces the dataset used by the above method and summarizes the main challenges of this technology.Finally,the future research direction is prospected.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">knowledge graph|deep learning|representation learning|temporal information|dynamic process</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Computer software</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology (General)</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Jisuanji kexue</subfield><subfield code="d">Editorial office of Computer Science, 2021</subfield><subfield code="g">49(2022), 9, Seite 162-171</subfield><subfield code="w">(DE-627)DOAJ078619254</subfield><subfield code="x">1002137X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:49</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:9</subfield><subfield code="g">pages:162-171</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.11896/jsjkx.220500204</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/7708422af1f7485d99343ec4f6322886</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-9-162.pdf</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1002-137X</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">49</subfield><subfield code="j">2022</subfield><subfield code="e">9</subfield><subfield code="h">162-171</subfield></datafield></record></collection>
|
| score |
7.4009867 |