Details
| Original language | English |
|---|---|
| Title of host publication | Data Integration in the Life Sciences |
| Subtitle of host publication | 13th International Conference, DILS 2018, Proceedings |
| Editors | Maria-Esther Vidal, Sören Auer |
| Publisher | Springer Verlag |
| Pages | 91-96 |
| Number of pages | 6 |
| ISBN (print) | 9783030060152 |
| Publication status | E-pub ahead of print - 30 Dec 2018 |
| Event | 13th International Conference on Data Integration in the Life Sciences, DILS 2018 - Hannover, Germany Duration: 20 Nov 2018 → 21 Nov 2018 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11371 LNBI |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
In recent years, following FAIR and open data principles, the number of available big data including biomedical data has been increased exponentially. In order to extract knowledge, these data should be curated, integrated, and semantically described. Accordingly, several semantic integration techniques have been developed; albeit effective, they may suffer from scalability in terms of different properties of big data. Even scaled-up approaches may be highly costly due to performing tasks of semantification, curation, and integration independently. To overcome these issues, we devise ConMap, a semantic integration approach which exploits knowledge encoded in ontologies to describe mapping rules in a way that performs all these tasks at the same time. The empirical evaluation of ConMap performed on different data sets shows that ConMap can significantly reduce the time required for knowledge graph creation by up to 70% of the time that is consumed following a traditional approach. Accordingly, the experimental results suggest that ConMap can be a semantic data integration solution that embody FAIR principles specifically in terms of interoperability.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Data Integration in the Life Sciences: 13th International Conference, DILS 2018, Proceedings. ed. / Maria-Esther Vidal; Sören Auer. Springer Verlag, 2018. p. 91-96 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11371 LNBI).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Poster paper data integration for supporting biomedical knowledge graph creation at large-scale
AU - Jozashoori, Samaneh
AU - Novikova, Tatiana
AU - Vidal, Maria Esther
PY - 2018/12/30
Y1 - 2018/12/30
N2 - In recent years, following FAIR and open data principles, the number of available big data including biomedical data has been increased exponentially. In order to extract knowledge, these data should be curated, integrated, and semantically described. Accordingly, several semantic integration techniques have been developed; albeit effective, they may suffer from scalability in terms of different properties of big data. Even scaled-up approaches may be highly costly due to performing tasks of semantification, curation, and integration independently. To overcome these issues, we devise ConMap, a semantic integration approach which exploits knowledge encoded in ontologies to describe mapping rules in a way that performs all these tasks at the same time. The empirical evaluation of ConMap performed on different data sets shows that ConMap can significantly reduce the time required for knowledge graph creation by up to 70% of the time that is consumed following a traditional approach. Accordingly, the experimental results suggest that ConMap can be a semantic data integration solution that embody FAIR principles specifically in terms of interoperability.
AB - In recent years, following FAIR and open data principles, the number of available big data including biomedical data has been increased exponentially. In order to extract knowledge, these data should be curated, integrated, and semantically described. Accordingly, several semantic integration techniques have been developed; albeit effective, they may suffer from scalability in terms of different properties of big data. Even scaled-up approaches may be highly costly due to performing tasks of semantification, curation, and integration independently. To overcome these issues, we devise ConMap, a semantic integration approach which exploits knowledge encoded in ontologies to describe mapping rules in a way that performs all these tasks at the same time. The empirical evaluation of ConMap performed on different data sets shows that ConMap can significantly reduce the time required for knowledge graph creation by up to 70% of the time that is consumed following a traditional approach. Accordingly, the experimental results suggest that ConMap can be a semantic data integration solution that embody FAIR principles specifically in terms of interoperability.
UR - http://www.scopus.com/inward/record.url?scp=85059684912&partnerID=8YFLogxK
U2 - 10.48550/arXiv.1811.01660
DO - 10.48550/arXiv.1811.01660
M3 - Conference contribution
AN - SCOPUS:85059684912
SN - 9783030060152
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 91
EP - 96
BT - Data Integration in the Life Sciences
A2 - Vidal, Maria-Esther
A2 - Auer, Sören
PB - Springer Verlag
T2 - 13th International Conference on Data Integration in the Life Sciences, DILS 2018
Y2 - 20 November 2018 through 21 November 2018
ER -