Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, SAC 2022 |
| Herausgeber (Verlag) | Association for Computing Machinery (ACM) |
| Seiten | 1908-1916 |
| Seitenumfang | 9 |
| ISBN (elektronisch) | 9781450387132 |
| Publikationsstatus | Veröffentlicht - 25 Apr. 2022 |
| Veranstaltung | 37th ACM/SIGAPP Symposium on Applied Computing, SAC 2022 - Virtual, Online Dauer: 25 Apr. 2022 → 29 Apr. 2022 |
Publikationsreihe
| Name | Proceedings of the ACM Symposium on Applied Computing |
|---|
Abstract
Despite encoding enormous amount of rich and valuable data, existing data sources are mostly created independently, being a significant challenge to their integration. Mapping languages, e.g., RML and R2RML, facilitate declarative specification of the process of applying meta-data and integrating data into a knowledge graph. Mapping rules can also include knowledge extraction functions in addition to expressing correspondences among data sources and a unified schema. Combining mapping rules and functions represents a powerful formalism to specify pipelines for integrating data into a knowledge graph transparently. Surprisingly, these formalisms are not fully adapted, and many knowledge graphs are created by executing ad-hoc programs to pre-process and integrate data. In this paper, we present EABlock, an approach integrating Entity Alignment (EA) as part of RML mapping rules. EABlock includes a block of functions performing entity recognition from textual attributes and link the recognized entities to the corresponding resources in Wikidata, DBpedia, and domain specific thesaurus, e.g., UMLS. EABlock provides agnostic and efficient techniques to evaluate the functions and transfer the mappings to facilitate its application in any RML-compliant engine. We have empirically evaluated EABlock performance, and results indicate that EABlock speeds up knowledge graph creation pipelines that require entity recognition and linking in state-of-the-art RML-compliant engines. EABlock is also publicly available as a tool through a GitHub repository and a DOI.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
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Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, SAC 2022. Association for Computing Machinery (ACM), 2022. S. 1908-1916 (Proceedings of the ACM Symposium on Applied Computing).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - EABlock
T2 - 37th ACM/SIGAPP Symposium on Applied Computing, SAC 2022
AU - Jozashoori, Samaneh
AU - Sakor, Ahmad
AU - Iglesias, Enrique
AU - Vidal, Maria Esther
N1 - Funding Information: This work has been partially supported by the EU H2020 RIA funded projects: CLARIFY with grant agreement (GA) No. 875160, P4-LUCAT with GA No. 53000015, and PLATOON with GA No. 872592.
PY - 2022/4/25
Y1 - 2022/4/25
N2 - Despite encoding enormous amount of rich and valuable data, existing data sources are mostly created independently, being a significant challenge to their integration. Mapping languages, e.g., RML and R2RML, facilitate declarative specification of the process of applying meta-data and integrating data into a knowledge graph. Mapping rules can also include knowledge extraction functions in addition to expressing correspondences among data sources and a unified schema. Combining mapping rules and functions represents a powerful formalism to specify pipelines for integrating data into a knowledge graph transparently. Surprisingly, these formalisms are not fully adapted, and many knowledge graphs are created by executing ad-hoc programs to pre-process and integrate data. In this paper, we present EABlock, an approach integrating Entity Alignment (EA) as part of RML mapping rules. EABlock includes a block of functions performing entity recognition from textual attributes and link the recognized entities to the corresponding resources in Wikidata, DBpedia, and domain specific thesaurus, e.g., UMLS. EABlock provides agnostic and efficient techniques to evaluate the functions and transfer the mappings to facilitate its application in any RML-compliant engine. We have empirically evaluated EABlock performance, and results indicate that EABlock speeds up knowledge graph creation pipelines that require entity recognition and linking in state-of-the-art RML-compliant engines. EABlock is also publicly available as a tool through a GitHub repository and a DOI.
AB - Despite encoding enormous amount of rich and valuable data, existing data sources are mostly created independently, being a significant challenge to their integration. Mapping languages, e.g., RML and R2RML, facilitate declarative specification of the process of applying meta-data and integrating data into a knowledge graph. Mapping rules can also include knowledge extraction functions in addition to expressing correspondences among data sources and a unified schema. Combining mapping rules and functions represents a powerful formalism to specify pipelines for integrating data into a knowledge graph transparently. Surprisingly, these formalisms are not fully adapted, and many knowledge graphs are created by executing ad-hoc programs to pre-process and integrate data. In this paper, we present EABlock, an approach integrating Entity Alignment (EA) as part of RML mapping rules. EABlock includes a block of functions performing entity recognition from textual attributes and link the recognized entities to the corresponding resources in Wikidata, DBpedia, and domain specific thesaurus, e.g., UMLS. EABlock provides agnostic and efficient techniques to evaluate the functions and transfer the mappings to facilitate its application in any RML-compliant engine. We have empirically evaluated EABlock performance, and results indicate that EABlock speeds up knowledge graph creation pipelines that require entity recognition and linking in state-of-the-art RML-compliant engines. EABlock is also publicly available as a tool through a GitHub repository and a DOI.
KW - entity alignment
KW - functional mappings
KW - knowledge graph creation
KW - mapping rules
KW - semantic data integration
UR - http://www.scopus.com/inward/record.url?scp=85130376029&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2112.07493
DO - 10.48550/arXiv.2112.07493
M3 - Conference contribution
AN - SCOPUS:85130376029
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 1908
EP - 1916
BT - Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, SAC 2022
PB - Association for Computing Machinery (ACM)
Y2 - 25 April 2022 through 29 April 2022
ER -