Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | On the Move to Meaningful Internet Systems |
| Untertitel | OTM 2019 Conferences - Confederated International Conferences: CoopIS, ODBASE, C and TC 2019, Rhodes, Greece, October 21–25, 2019, Proceedings |
| Herausgeber/-innen | Hervé Panetto, Christophe Debruyne, Dave Lewis, Martin Hepp, Claudio Agostino Ardagna, Robert Meersman |
| Seiten | 58-75 |
| Seitenumfang | 18 |
| Auflage | 1. |
| ISBN (elektronisch) | 978-3-030-33246-4 |
| Publikationsstatus | Veröffentlicht - 11 Okt. 2019 |
| Veranstaltung | Confederated International Conferences on Cooperative Information Systems, CoopIS 2019, Ontologies, Databases, and Applications of Semantics, ODBASE 2019, and Cloud and Trusted Computing, C and TC, held as part of OTM 2019 - Rhodes, Griechenland Dauer: 21 Okt. 2019 → 25 Okt. 2019 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 11877 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
Semantic web technologies have significantly contributed with effective solutions for the problems of data integration and knowledge graph creation. However, with the rapid growth of big data in diverse domains, different interoperability issues still demand to be addressed, being scalability one of the main challenges. In this paper, we address the problem of knowledge graph creation at scale and provide MapSDI, a mapping rule-based framework for optimizing semantic data integration into knowledge graphs. MapSDI allows for the semantic enrichment of large-sized, heterogeneous, and potentially low-quality data efficiently. The input of MapSDI is a set of data sources and mapping rules being generated by a mapping language such as RML. First, MapSDI pre-processes the sources based on semantic information extracted from mapping rules, by performing basic database operators; it projects out required attributes, eliminates duplicates, and selects relevant entries. All these operators are defined based on the knowledge encoded by the mapping rules which will be then used by the semantification engine (or RDFizer) to produce a knowledge graph. We have empirically studied the impact of MapSDI on existing RDFizers, and observed that knowledge graph creation time can be reduced on average in one order of magnitude. It is also shown, theoretically, that the sources and rules transformations provided by MapSDI are data-lossless.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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On the Move to Meaningful Internet Systems: OTM 2019 Conferences - Confederated International Conferences: CoopIS, ODBASE, C and TC 2019, Rhodes, Greece, October 21–25, 2019, Proceedings. Hrsg. / Hervé Panetto; Christophe Debruyne; Dave Lewis; Martin Hepp; Claudio Agostino Ardagna; Robert Meersman. 1. Aufl. 2019. S. 58-75 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 11877 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - MapSDI
T2 - Confederated International Conferences on Cooperative Information Systems, CoopIS 2019, Ontologies, Databases, and Applications of Semantics, ODBASE 2019, and Cloud and Trusted Computing, C and TC, held as part of OTM 2019
AU - Jozashoori, Samaneh
AU - Vidal, Maria Esther
N1 - Funding information: This work has been partially funded by the EU H2020 Program for the Project No. 727658 (IASIS).
PY - 2019/10/11
Y1 - 2019/10/11
N2 - Semantic web technologies have significantly contributed with effective solutions for the problems of data integration and knowledge graph creation. However, with the rapid growth of big data in diverse domains, different interoperability issues still demand to be addressed, being scalability one of the main challenges. In this paper, we address the problem of knowledge graph creation at scale and provide MapSDI, a mapping rule-based framework for optimizing semantic data integration into knowledge graphs. MapSDI allows for the semantic enrichment of large-sized, heterogeneous, and potentially low-quality data efficiently. The input of MapSDI is a set of data sources and mapping rules being generated by a mapping language such as RML. First, MapSDI pre-processes the sources based on semantic information extracted from mapping rules, by performing basic database operators; it projects out required attributes, eliminates duplicates, and selects relevant entries. All these operators are defined based on the knowledge encoded by the mapping rules which will be then used by the semantification engine (or RDFizer) to produce a knowledge graph. We have empirically studied the impact of MapSDI on existing RDFizers, and observed that knowledge graph creation time can be reduced on average in one order of magnitude. It is also shown, theoretically, that the sources and rules transformations provided by MapSDI are data-lossless.
AB - Semantic web technologies have significantly contributed with effective solutions for the problems of data integration and knowledge graph creation. However, with the rapid growth of big data in diverse domains, different interoperability issues still demand to be addressed, being scalability one of the main challenges. In this paper, we address the problem of knowledge graph creation at scale and provide MapSDI, a mapping rule-based framework for optimizing semantic data integration into knowledge graphs. MapSDI allows for the semantic enrichment of large-sized, heterogeneous, and potentially low-quality data efficiently. The input of MapSDI is a set of data sources and mapping rules being generated by a mapping language such as RML. First, MapSDI pre-processes the sources based on semantic information extracted from mapping rules, by performing basic database operators; it projects out required attributes, eliminates duplicates, and selects relevant entries. All these operators are defined based on the knowledge encoded by the mapping rules which will be then used by the semantification engine (or RDFizer) to produce a knowledge graph. We have empirically studied the impact of MapSDI on existing RDFizers, and observed that knowledge graph creation time can be reduced on average in one order of magnitude. It is also shown, theoretically, that the sources and rules transformations provided by MapSDI are data-lossless.
KW - Data integration system
KW - Knowledge graph creation
KW - Semantic data integration
KW - Transformation rules
UR - http://www.scopus.com/inward/record.url?scp=85077889555&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-33246-4_4
DO - 10.1007/978-3-030-33246-4_4
M3 - Conference contribution
AN - SCOPUS:85077889555
SN - 978-3-030-33245-7
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 58
EP - 75
BT - On the Move to Meaningful Internet Systems
A2 - Panetto, Hervé
A2 - Debruyne, Christophe
A2 - Lewis, Dave
A2 - Hepp, Martin
A2 - Ardagna, Claudio Agostino
A2 - Meersman, Robert
Y2 - 21 October 2019 through 25 October 2019
ER -