Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | K-CAP 2019 |
| Untertitel | Proceedings of the 10th International Conference on Knowledge Capture |
| Erscheinungsort | New York |
| Seiten | 21-28 |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 9781450370080 |
| Publikationsstatus | Veröffentlicht - 23 Sept. 2019 |
| Veranstaltung | 10th International Conference on Knowledge Capture, K-CAP 2019 - Marina Del Rey, USA / Vereinigte Staaten Dauer: 19 Nov. 2019 → 21 Nov. 2019 |
Abstract
Capturing knowledge about the mulitilinguality of a knowledge graph is of supreme importance to understand its applicability across multiple languages. Several metrics have been proposed for describing mulitilinguality at the level of a whole knowledge graph. Albeit enabling the understanding of the ecosystem of knowledge graphs in terms of the utilized languages, they are unable to capture a fine-grained description of the languages in which the different entities and properties of the knowledge graph are represented. This lack of representation prevents the comparison of existing knowledge graphs in order to decide which are the most appropriate for a multilingual application. In this work, we approach the problem of ranking knowledge graphs based on their language features and propose LINGVO, a framework able to capture mulitilinguality at different levels of granularity. Grounded in knowledge graph descriptions, LINGVO is, additionally, able to solve the problem of ranking knowledge graphs according to a degree of mulitilinguality of the represented entities. We have empirically studied the effectiveness of LINGVO in a benchmark of queries to be executed against existing knowledge graphs. The observed results provide evidence that LINGVO captures the mulitilinguality of the studied knowledge graphs similarly than a crowd-sourced gold standard.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Information systems
- Informatik (insg.)
- Theoretische Informatik und Mathematik
- Informatik (insg.)
- Angewandte Informatik
- Informatik (insg.)
- Software
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- Apa
- Vancouver
- BibTex
- RIS
K-CAP 2019: Proceedings of the 10th International Conference on Knowledge Capture. New York, 2019. S. 21-28.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Ranking knowledge graphs by capturing knowledge about languages and labels
AU - Kaffee, Lucie Aimée
AU - Endris, Kemele Muhammed
AU - Simperl, Elena
AU - Vidal, Maria Esther
N1 - Funding information: This research was supported by EU H2020 Program for the Project No. 727658 (IASIS) and DSTL under grant number DSTLX-1000094186.
PY - 2019/9/23
Y1 - 2019/9/23
N2 - Capturing knowledge about the mulitilinguality of a knowledge graph is of supreme importance to understand its applicability across multiple languages. Several metrics have been proposed for describing mulitilinguality at the level of a whole knowledge graph. Albeit enabling the understanding of the ecosystem of knowledge graphs in terms of the utilized languages, they are unable to capture a fine-grained description of the languages in which the different entities and properties of the knowledge graph are represented. This lack of representation prevents the comparison of existing knowledge graphs in order to decide which are the most appropriate for a multilingual application. In this work, we approach the problem of ranking knowledge graphs based on their language features and propose LINGVO, a framework able to capture mulitilinguality at different levels of granularity. Grounded in knowledge graph descriptions, LINGVO is, additionally, able to solve the problem of ranking knowledge graphs according to a degree of mulitilinguality of the represented entities. We have empirically studied the effectiveness of LINGVO in a benchmark of queries to be executed against existing knowledge graphs. The observed results provide evidence that LINGVO captures the mulitilinguality of the studied knowledge graphs similarly than a crowd-sourced gold standard.
AB - Capturing knowledge about the mulitilinguality of a knowledge graph is of supreme importance to understand its applicability across multiple languages. Several metrics have been proposed for describing mulitilinguality at the level of a whole knowledge graph. Albeit enabling the understanding of the ecosystem of knowledge graphs in terms of the utilized languages, they are unable to capture a fine-grained description of the languages in which the different entities and properties of the knowledge graph are represented. This lack of representation prevents the comparison of existing knowledge graphs in order to decide which are the most appropriate for a multilingual application. In this work, we approach the problem of ranking knowledge graphs based on their language features and propose LINGVO, a framework able to capture mulitilinguality at different levels of granularity. Grounded in knowledge graph descriptions, LINGVO is, additionally, able to solve the problem of ranking knowledge graphs according to a degree of mulitilinguality of the represented entities. We have empirically studied the effectiveness of LINGVO in a benchmark of queries to be executed against existing knowledge graphs. The observed results provide evidence that LINGVO captures the mulitilinguality of the studied knowledge graphs similarly than a crowd-sourced gold standard.
KW - Knowledge graph
KW - Multilinguality
KW - Question answering
KW - Ranking
UR - http://www.scopus.com/inward/record.url?scp=85077247863&partnerID=8YFLogxK
U2 - 10.1145/3360901.3364443
DO - 10.1145/3360901.3364443
M3 - Conference contribution
AN - SCOPUS:85077247863
SP - 21
EP - 28
BT - K-CAP 2019
CY - New York
T2 - 10th International Conference on Knowledge Capture, K-CAP 2019
Y2 - 19 November 2019 through 21 November 2019
ER -