Details
| Original language | English |
|---|---|
| Title of host publication | Mathematical Software |
| Subtitle of host publication | ICMS 2020 - 7th International Conference, Proceedings |
| Editors | Anna Maria Bigatti, Jacques Carette, James H. Davenport, Michael Joswig, Timo de Wolff |
| Publisher | Springer Nature |
| Pages | 327-334 |
| Number of pages | 8 |
| ISBN (print) | 9783030521998 |
| Publication status | Published - 2020 |
| Externally published | Yes |
| Event | 7th International Congress on Mathematical Software, ICMS 2020 - Braunschweig, Germany Duration: 13 Jul 2020 → 16 Jul 2020 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 12097 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
The Open Research Knowledge Graph (ORKG) provides machine-actionable access to scholarly literature that habitually is written in prose. Following the FAIR principles, the ORKG makes traditional, human-coded knowledge findable, accessible, interoperable, and reusable in a structured manner in accordance with the Linked Open Data paradigm. At the moment, in ORKG papers are described manually, but in the long run the semantic depth of the literature at scale needs automation. Operational Research is a suitable test case for this vision because the mathematical field and, hence, its publication habits are highly structured: A mundane problem is formulated as a mathematical model, solved or approximated numerically, and evaluated systematically. We study the existing literature with respect to the Assembly Line Balancing Problem and derive a semantic description in accordance with the ORKG. Eventually, selected papers are ingested to test the semantic description and refine it further.
Keywords
- Knowledge graph, Mathematical knowledge management, Operational research literature, Operations research literature
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
Cite this
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Mathematical Software: ICMS 2020 - 7th International Conference, Proceedings. ed. / Anna Maria Bigatti; Jacques Carette; James H. Davenport; Michael Joswig; Timo de Wolff. Springer Nature, 2020. p. 327-334 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12097 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Operational Research Literature as a Use Case for the Open Research Knowledge Graph
AU - Runnwerth, Mila
AU - Stocker, Markus
AU - Auer, Sören
N1 - Publisher Copyright: © 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - The Open Research Knowledge Graph (ORKG) provides machine-actionable access to scholarly literature that habitually is written in prose. Following the FAIR principles, the ORKG makes traditional, human-coded knowledge findable, accessible, interoperable, and reusable in a structured manner in accordance with the Linked Open Data paradigm. At the moment, in ORKG papers are described manually, but in the long run the semantic depth of the literature at scale needs automation. Operational Research is a suitable test case for this vision because the mathematical field and, hence, its publication habits are highly structured: A mundane problem is formulated as a mathematical model, solved or approximated numerically, and evaluated systematically. We study the existing literature with respect to the Assembly Line Balancing Problem and derive a semantic description in accordance with the ORKG. Eventually, selected papers are ingested to test the semantic description and refine it further.
AB - The Open Research Knowledge Graph (ORKG) provides machine-actionable access to scholarly literature that habitually is written in prose. Following the FAIR principles, the ORKG makes traditional, human-coded knowledge findable, accessible, interoperable, and reusable in a structured manner in accordance with the Linked Open Data paradigm. At the moment, in ORKG papers are described manually, but in the long run the semantic depth of the literature at scale needs automation. Operational Research is a suitable test case for this vision because the mathematical field and, hence, its publication habits are highly structured: A mundane problem is formulated as a mathematical model, solved or approximated numerically, and evaluated systematically. We study the existing literature with respect to the Assembly Line Balancing Problem and derive a semantic description in accordance with the ORKG. Eventually, selected papers are ingested to test the semantic description and refine it further.
KW - Knowledge graph
KW - Mathematical knowledge management
KW - Operational research literature
KW - Operations research literature
UR - http://www.scopus.com/inward/record.url?scp=85088521521&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2006.13733
DO - 10.48550/arXiv.2006.13733
M3 - Conference contribution
AN - SCOPUS:85088521521
SN - 9783030521998
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 327
EP - 334
BT - Mathematical Software
A2 - Bigatti, Anna Maria
A2 - Carette, Jacques
A2 - Davenport, James H.
A2 - Joswig, Michael
A2 - de Wolff, Timo
PB - Springer Nature
T2 - 7th International Congress on Mathematical Software, ICMS 2020
Y2 - 13 July 2020 through 16 July 2020
ER -