Details
| Original language | English |
|---|---|
| Title of host publication | Digital Libraries at Times of Massive Societal Transition |
| Subtitle of host publication | 22nd International Conferenceon Asia-Pacific Digital Libraries, ICADL 2020Kyoto, Japan, November 30–December 1, 2020 Proceedings |
| Editors | Emi Ishita, Natalie Lee Pang, Lihong Zhou |
| Publisher | Springer Nature Switzerland AG |
| Pages | 373-389 |
| Number of pages | 17 |
| ISBN (print) | 9783030644512 |
| Publication status | Published - 2020 |
| Event | 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020 - Kyoto, Japan Duration: 30 Nov 2020 → 1 Dec 2020 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 12504 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Due to the lack of structure, scholarly knowledge remains hardly accessible for machines. Scholarly knowledge graphs have been proposed as a solution. Creating such a knowledge graph requires manual effort and domain experts, and is therefore time-consuming and cumbersome. In this work, we present a human-in-the-loop methodology used to build a scholarly knowledge graph leveraging literature survey articles. Survey articles often contain manually curated and high-quality tabular information that summarizes findings published in the scientific literature. Consequently, survey articles are an excellent resource for generating a scholarly knowledge graph. The presented methodology consists of five steps, in which tables and references are extracted from PDF articles, tables are formatted and finally ingested into the knowledge graph. To evaluate the methodology, 92 survey articles, containing 160 survey tables, have been imported in the graph. In total, 2626 papers have been added to the knowledge graph using the presented methodology. The results demonstrate the feasibility of our approach, but also indicate that manual effort is required and thus underscore the important role of human experts.
Keywords
- Scholarly communication, Scholarly knowledge graphs, Tabular data extraction
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Digital Libraries at Times of Massive Societal Transition : 22nd International Conferenceon Asia-Pacific Digital Libraries, ICADL 2020Kyoto, Japan, November 30–December 1, 2020 Proceedings. ed. / Emi Ishita; Natalie Lee Pang; Lihong Zhou. Springer Nature Switzerland AG, 2020. p. 373-389 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12504 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Creating a Scholarly Knowledge Graph from Survey Article Tables
AU - Oelen, Allard
AU - Stocker, Markus
AU - Auer, Sören
N1 - Funding Information: Acknowledgements. 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 want to thank our colleagues Mohamad Yaser Jaradeh and Kheir Eddine Farfar for their contributions to this work.
PY - 2020
Y1 - 2020
N2 - Due to the lack of structure, scholarly knowledge remains hardly accessible for machines. Scholarly knowledge graphs have been proposed as a solution. Creating such a knowledge graph requires manual effort and domain experts, and is therefore time-consuming and cumbersome. In this work, we present a human-in-the-loop methodology used to build a scholarly knowledge graph leveraging literature survey articles. Survey articles often contain manually curated and high-quality tabular information that summarizes findings published in the scientific literature. Consequently, survey articles are an excellent resource for generating a scholarly knowledge graph. The presented methodology consists of five steps, in which tables and references are extracted from PDF articles, tables are formatted and finally ingested into the knowledge graph. To evaluate the methodology, 92 survey articles, containing 160 survey tables, have been imported in the graph. In total, 2626 papers have been added to the knowledge graph using the presented methodology. The results demonstrate the feasibility of our approach, but also indicate that manual effort is required and thus underscore the important role of human experts.
AB - Due to the lack of structure, scholarly knowledge remains hardly accessible for machines. Scholarly knowledge graphs have been proposed as a solution. Creating such a knowledge graph requires manual effort and domain experts, and is therefore time-consuming and cumbersome. In this work, we present a human-in-the-loop methodology used to build a scholarly knowledge graph leveraging literature survey articles. Survey articles often contain manually curated and high-quality tabular information that summarizes findings published in the scientific literature. Consequently, survey articles are an excellent resource for generating a scholarly knowledge graph. The presented methodology consists of five steps, in which tables and references are extracted from PDF articles, tables are formatted and finally ingested into the knowledge graph. To evaluate the methodology, 92 survey articles, containing 160 survey tables, have been imported in the graph. In total, 2626 papers have been added to the knowledge graph using the presented methodology. The results demonstrate the feasibility of our approach, but also indicate that manual effort is required and thus underscore the important role of human experts.
KW - Scholarly communication
KW - Scholarly knowledge graphs
KW - Tabular data extraction
UR - http://www.scopus.com/inward/record.url?scp=85097564412&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-64452-9_35
DO - 10.1007/978-3-030-64452-9_35
M3 - Conference contribution
AN - SCOPUS:85097564412
SN - 9783030644512
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 373
EP - 389
BT - Digital Libraries at Times of Massive Societal Transition
A2 - Ishita, Emi
A2 - Pang, Natalie Lee
A2 - Zhou, Lihong
PB - Springer Nature Switzerland AG
T2 - 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020
Y2 - 30 November 2020 through 1 December 2020
ER -