Details
| Original language | English |
|---|---|
| Title of host publication | K-CAP 2019 |
| Subtitle of host publication | Proceedings of the 10th International Conference on Knowledge Capture |
| Place of Publication | New York |
| Pages | 21-28 |
| Number of pages | 8 |
| ISBN (electronic) | 9781450370080 |
| Publication status | Published - 23 Sept 2019 |
| Event | 10th International Conference on Knowledge Capture, K-CAP 2019 - Marina Del Rey, United States Duration: 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.
Keywords
- Knowledge graph, Multilinguality, Question answering, Ranking
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Computational Theory and Mathematics
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Software
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K-CAP 2019: Proceedings of the 10th International Conference on Knowledge Capture. New York, 2019. p. 21-28.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › 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 -