Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Data Integration in the Life Sciences |
| Untertitel | 13th International Conference, DILS 2018, Hannover, Germany, November 20-21, 2018, Proceedings |
| Herausgeber/-innen | Maria-Esther Vidal, Sören Auer |
| Herausgeber (Verlag) | Springer |
| Seiten | 44-49 |
| Seitenumfang | 6 |
| Auflage | 1. |
| ISBN (elektronisch) | 978-3-030-06016-9 |
| ISBN (Print) | 978-3-030-06015-2 |
| Publikationsstatus | Veröffentlicht - 30 Dez. 2018 |
| Veranstaltung | 13th International Conference on Data Integration in the Life Sciences, DILS 2018 - Hannover, Deutschland Dauer: 20 Nov. 2018 → 21 Nov. 2018 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (LNCS) |
|---|---|
| Band | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
| Name | Lecture Notes in Bioinformatics (LNBI) |
|---|---|
| Band | 11371 |
| ISSN (Print) | 2366-6331 |
| ISSN (elektronisch) | 2366-6323 |
Abstract
Big biomedical data has grown exponentially during the last decades, as well as the applications that demand the understanding and discovery of the knowledge encoded in available big data. In order to address these requirements while scaling up to the dominant dimensions of big biomedical data –volume, variety, and veracity– novel data integration techniques need to be defined. In this paper, we devise a knowledge-driven approach that relies on Semantic Web technologies such as ontologies, mapping languages, linked data, to generate a knowledge graph that integrates big data. Furthermore, query processing and knowledge discovery methods are implemented on top of the knowledge graph for enabling exploration and pattern uncovering. We report on the results of applying the proposed knowledge-driven approach in the EU funded project iASiS (http://project-iasis.eu). in order to transform big data into actionable knowledge, paying thus the way for precision medicine and health policy making.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
Ziele für nachhaltige Entwicklung
Zitieren
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- Harvard
- Apa
- Vancouver
- BibTex
- RIS
Data Integration in the Life Sciences: 13th International Conference, DILS 2018, Hannover, Germany, November 20-21, 2018, Proceedings. Hrsg. / Maria-Esther Vidal; Sören Auer. 1. Aufl. Springer, 2018. S. 44-49 (Lecture Notes in Computer Science (LNCS); Band 0302-9743), (Lecture Notes in Bioinformatics (LNBI); Band 11371).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - A knowledge-driven pipeline for transforming big data into actionable knowledge
AU - Vidal, Maria Esther
AU - Endris, Kemele M.
AU - Jozashoori, Samaneh
AU - Palma, Guillermo
PY - 2018/12/30
Y1 - 2018/12/30
N2 - Big biomedical data has grown exponentially during the last decades, as well as the applications that demand the understanding and discovery of the knowledge encoded in available big data. In order to address these requirements while scaling up to the dominant dimensions of big biomedical data –volume, variety, and veracity– novel data integration techniques need to be defined. In this paper, we devise a knowledge-driven approach that relies on Semantic Web technologies such as ontologies, mapping languages, linked data, to generate a knowledge graph that integrates big data. Furthermore, query processing and knowledge discovery methods are implemented on top of the knowledge graph for enabling exploration and pattern uncovering. We report on the results of applying the proposed knowledge-driven approach in the EU funded project iASiS (http://project-iasis.eu). in order to transform big data into actionable knowledge, paying thus the way for precision medicine and health policy making.
AB - Big biomedical data has grown exponentially during the last decades, as well as the applications that demand the understanding and discovery of the knowledge encoded in available big data. In order to address these requirements while scaling up to the dominant dimensions of big biomedical data –volume, variety, and veracity– novel data integration techniques need to be defined. In this paper, we devise a knowledge-driven approach that relies on Semantic Web technologies such as ontologies, mapping languages, linked data, to generate a knowledge graph that integrates big data. Furthermore, query processing and knowledge discovery methods are implemented on top of the knowledge graph for enabling exploration and pattern uncovering. We report on the results of applying the proposed knowledge-driven approach in the EU funded project iASiS (http://project-iasis.eu). in order to transform big data into actionable knowledge, paying thus the way for precision medicine and health policy making.
UR - http://www.scopus.com/inward/record.url?scp=85059666826&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-06016-9_4
DO - 10.1007/978-3-030-06016-9_4
M3 - Conference contribution
AN - SCOPUS:85059666826
SN - 978-3-030-06015-2
T3 - Lecture Notes in Computer Science (LNCS)
SP - 44
EP - 49
BT - Data Integration in the Life Sciences
A2 - Vidal, Maria-Esther
A2 - Auer, Sören
PB - Springer
T2 - 13th International Conference on Data Integration in the Life Sciences, DILS 2018
Y2 - 20 November 2018 through 21 November 2018
ER -