Details
| Original language | English |
|---|---|
| Title of host publication | JCDL 2022 - Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2022 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Number of pages | 5 |
| ISBN (electronic) | 9781450393454 |
| Publication status | Published - 20 Jun 2022 |
| Externally published | Yes |
| Event | 22nd ACM/IEEE Joint Conference on Digital Libraries, JCDL 2022 - Virtual, Online, Germany Duration: 20 Jun 2022 → 24 Jun 2022 |
Publication series
| Name | Proceedings of the ACM/IEEE Joint Conference on Digital Libraries |
|---|---|
| ISSN (Print) | 1552-5996 |
Abstract
As the number of published scholarly articles grows steadily each year, new methods are needed to organize scholarly knowledge so that it can be more efficiently discovered and used. Natural Language Processing (NLP) techniques are able to autonomously process scholarly articles at scale and to create machine readable representations of the article content. However, autonomous NLP methods are by far not sufficiently accurate to create a high-quality knowledge graph. Yet quality is crucial for the graph to be useful in practice. We present TinyGenius, a methodology to validate NLP-extracted scholarly knowledge statements using microtasks performed with crowdsourcing. The scholarly context in which the crowd workers operate has multiple challenges. The explainability of the employed NLP methods is crucial to provide context in order to support the decision process of crowd workers. We employed TinyGenius to populate a paper-centric knowledge graph, using five distinct NLP methods. In the end, the resulting knowledge graph serves as a digital library for scholarly articles.
Keywords
- Crowdsourcing Microtasks, Intelligent User Interfaces, Knowledge Graph Validation, Scholarly Knowledge Graphs
ASJC Scopus subject areas
- Engineering(all)
- General Engineering
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JCDL 2022 - Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2022. Institute of Electrical and Electronics Engineers Inc., 2022. 5 (Proceedings of the ACM/IEEE Joint Conference on Digital Libraries).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - TinyGenius
T2 - 22nd ACM/IEEE Joint Conference on Digital Libraries, JCDL 2022
AU - Oelen, Allard
AU - Stocker, Markus
AU - Auer, Sören
N1 - Funding Information: This work was co-funded by the European Research Council for the project ScienceGRAPH (Grant agreement ID: 819536) and the TIB Leibniz Information Centre for Science and Technology. We would like to thank Mohamad Yaser Jaradeh and Jennifer D’Souza for their contributions to this work.
PY - 2022/6/20
Y1 - 2022/6/20
N2 - As the number of published scholarly articles grows steadily each year, new methods are needed to organize scholarly knowledge so that it can be more efficiently discovered and used. Natural Language Processing (NLP) techniques are able to autonomously process scholarly articles at scale and to create machine readable representations of the article content. However, autonomous NLP methods are by far not sufficiently accurate to create a high-quality knowledge graph. Yet quality is crucial for the graph to be useful in practice. We present TinyGenius, a methodology to validate NLP-extracted scholarly knowledge statements using microtasks performed with crowdsourcing. The scholarly context in which the crowd workers operate has multiple challenges. The explainability of the employed NLP methods is crucial to provide context in order to support the decision process of crowd workers. We employed TinyGenius to populate a paper-centric knowledge graph, using five distinct NLP methods. In the end, the resulting knowledge graph serves as a digital library for scholarly articles.
AB - As the number of published scholarly articles grows steadily each year, new methods are needed to organize scholarly knowledge so that it can be more efficiently discovered and used. Natural Language Processing (NLP) techniques are able to autonomously process scholarly articles at scale and to create machine readable representations of the article content. However, autonomous NLP methods are by far not sufficiently accurate to create a high-quality knowledge graph. Yet quality is crucial for the graph to be useful in practice. We present TinyGenius, a methodology to validate NLP-extracted scholarly knowledge statements using microtasks performed with crowdsourcing. The scholarly context in which the crowd workers operate has multiple challenges. The explainability of the employed NLP methods is crucial to provide context in order to support the decision process of crowd workers. We employed TinyGenius to populate a paper-centric knowledge graph, using five distinct NLP methods. In the end, the resulting knowledge graph serves as a digital library for scholarly articles.
KW - Crowdsourcing Microtasks
KW - Intelligent User Interfaces
KW - Knowledge Graph Validation
KW - Scholarly Knowledge Graphs
UR - http://www.scopus.com/inward/record.url?scp=85133225808&partnerID=8YFLogxK
U2 - 10.1145/3529372.3533285
DO - 10.1145/3529372.3533285
M3 - Conference contribution
AN - SCOPUS:85133225808
T3 - Proceedings of the ACM/IEEE Joint Conference on Digital Libraries
BT - JCDL 2022 - Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 June 2022 through 24 June 2022
ER -