| Item type |
SIG Technical Reports(1) |
| 公開日 |
2021-07-08 |
| タイトル |
|
|
タイトル |
Generating RDF Metadata from Twitter Streams |
| タイトル |
|
|
言語 |
en |
|
タイトル |
Generating RDF Metadata from Twitter Streams |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
DC |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_18gh |
|
資源タイプ |
technical report |
| 著者所属 |
|
|
|
Graduate School of Science and Technology University of Tsukuba |
| 著者所属 |
|
|
|
Center for Computational Sciences University of Tsukuba |
| 著者所属(英) |
|
|
|
en |
|
|
Graduate School of Science and Technology University of Tsukuba |
| 著者所属(英) |
|
|
|
en |
|
|
Center for Computational Sciences University of Tsukuba |
| 著者名 |
Teklu, Aregawi Gidey
Toshiyuki, Amagasa
|
| 著者名(英) |
Teklu, Aregawi Gidey
Toshiyuki, Amagasa
|
| 論文抄録 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
In recent years, Social Network Service has gained much public attention as a source of information. Twitter is considered as most prominent SNS source. Most of previous studies on the content of tweets have focused on the detection of emerging topics and some have focused on creation of RDF. It is important to generate RDF metadata from tweets to put data in context via linking and semantic metadata providing a framework for data integration, analytics and sharing. However, in the previous studies, the generated RDFs were not from streaming tweets and the mentions interlinked with knowledge graphs did not show the location of the text in the tweet. In our experiment, we generated RDF metadata from streaming tweets using a hashtag 'Covid-19' and we located the exact position of the mentions in the tweet being linked to a knowledge graph, WikiPedia using TagMe entity linker. We expressed it as a URI which includes the URL of Twitter, the user name, the tweet id and index of the offset information. |
| 論文抄録(英) |
|
|
内容記述タイプ |
Other |
|
内容記述 |
In recent years, Social Network Service has gained much public attention as a source of information. Twitter is considered as most prominent SNS source. Most of previous studies on the content of tweets have focused on the detection of emerging topics and some have focused on creation of RDF. It is important to generate RDF metadata from tweets to put data in context via linking and semantic metadata providing a framework for data integration, analytics and sharing. However, in the previous studies, the generated RDFs were not from streaming tweets and the mentions interlinked with knowledge graphs did not show the location of the text in the tweet. In our experiment, we generated RDF metadata from streaming tweets using a hashtag 'Covid-19' and we located the exact position of the mentions in the tweet being linked to a knowledge graph, WikiPedia using TagMe entity linker. We expressed it as a URI which includes the URL of Twitter, the user name, the tweet id and index of the offset information. |
| 書誌レコードID |
|
|
収録物識別子タイプ |
NCID |
|
収録物識別子 |
AN10539261 |
| 書誌情報 |
研究報告ドキュメントコミュニケーション（DC）
巻 2021-DC-121,
号 7,
p. 1-8,
発行日 2021-07-08
|
| ISSN |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
2188-8892 |
| Notice |
|
|
|
SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
|
|
言語 |
ja |
|
出版者 |
情報処理学会 |
IPSJ-DC21121007.pdf (2.7 MB)
