Details
| Original language | English |
|---|---|
| Title of host publication | The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018 |
| Pages | 1297-1306 |
| Number of pages | 10 |
| ISBN (electronic) | 9781450356398 |
| Publication status | Published - 10 Apr 2018 |
| Event | 27th International World Wide Web, WWW 2018 - Lyon, France Duration: 23 Apr 2018 → 27 Apr 2018 |
Publication series
| Name | The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018 |
|---|
Abstract
Embedded markup of Web pages has seen widespread adoption throughout the past years driven by standards such as RDFa and Microdata and initiatives such as schema.org, where recent studies show an adoption by 39% of all Web pages already in 2016. While this constitutes an important information source for tasks such as Web search, Web page classification or knowledge graph augmentation, individual markup nodes are usually sparsely described and often lack essential information. For instance, from 26 million nodes describing events within the Common Crawl in 2016, 59% of nodes provide less than six statements and only 257,000 nodes (0.96%) are typed with more specific event subtypes. Nevertheless, given the scale and diversity of Web markup data, nodes that provide missing information can be obtained from the Web in large quantities, in particular for categorical properties. Such data constitutes potential training data for inferring missing information to significantly augment sparsely described nodes. In this work, we introduce a supervised approach for inferring missing categorical properties in Web markup. Our experiments, conducted on properties of events and movies, show a performance of 79% and 83% F1 score correspondingly, significantly outperforming existing baselines.
Keywords
- Information inferring, Supervised learning, Web markup
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Software
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The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018. 2018. p. 1297-1306 (The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Inferring Missing Categorical Information in Noisy and Sparse Web Markup
AU - Tempelmeier, Nicolas
AU - Demidova, Elena
AU - Dietze, Stefan
N1 - Funding Information: This work was partially funded by the European Commission ("AFEL" project, grant ID 687916) and the BMBF ("Data4UrbanMobility" project, grant ID 02K15A040).
PY - 2018/4/10
Y1 - 2018/4/10
N2 - Embedded markup of Web pages has seen widespread adoption throughout the past years driven by standards such as RDFa and Microdata and initiatives such as schema.org, where recent studies show an adoption by 39% of all Web pages already in 2016. While this constitutes an important information source for tasks such as Web search, Web page classification or knowledge graph augmentation, individual markup nodes are usually sparsely described and often lack essential information. For instance, from 26 million nodes describing events within the Common Crawl in 2016, 59% of nodes provide less than six statements and only 257,000 nodes (0.96%) are typed with more specific event subtypes. Nevertheless, given the scale and diversity of Web markup data, nodes that provide missing information can be obtained from the Web in large quantities, in particular for categorical properties. Such data constitutes potential training data for inferring missing information to significantly augment sparsely described nodes. In this work, we introduce a supervised approach for inferring missing categorical properties in Web markup. Our experiments, conducted on properties of events and movies, show a performance of 79% and 83% F1 score correspondingly, significantly outperforming existing baselines.
AB - Embedded markup of Web pages has seen widespread adoption throughout the past years driven by standards such as RDFa and Microdata and initiatives such as schema.org, where recent studies show an adoption by 39% of all Web pages already in 2016. While this constitutes an important information source for tasks such as Web search, Web page classification or knowledge graph augmentation, individual markup nodes are usually sparsely described and often lack essential information. For instance, from 26 million nodes describing events within the Common Crawl in 2016, 59% of nodes provide less than six statements and only 257,000 nodes (0.96%) are typed with more specific event subtypes. Nevertheless, given the scale and diversity of Web markup data, nodes that provide missing information can be obtained from the Web in large quantities, in particular for categorical properties. Such data constitutes potential training data for inferring missing information to significantly augment sparsely described nodes. In this work, we introduce a supervised approach for inferring missing categorical properties in Web markup. Our experiments, conducted on properties of events and movies, show a performance of 79% and 83% F1 score correspondingly, significantly outperforming existing baselines.
KW - Information inferring
KW - Supervised learning
KW - Web markup
UR - http://www.scopus.com/inward/record.url?scp=85075443220&partnerID=8YFLogxK
U2 - 10.1145/3178876.3186028
DO - 10.1145/3178876.3186028
M3 - Conference contribution
AN - SCOPUS:85075443220
T3 - The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018
SP - 1297
EP - 1306
BT - The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018
T2 - 27th International World Wide Web, WWW 2018
Y2 - 23 April 2018 through 27 April 2018
ER -