research-article

Ranking Knowledge Graphs By Capturing Knowledge about Languages and Labels

Authors:

Lucie-Aimée Kaffee,

Kemele M. Endris,

Maria-Esther VidalAuthors Info & Claims

K-CAP '19: Proceedings of the 10th International Conference on Knowledge Capture

Pages 21 - 28

https://doi.org/10.1145/3360901.3364443

Published: 23 September 2019 Publication History

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.

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Cited By

Russo MSawischa SVidal MHong JPark J(2024)Tracing the Impact of Bias in Link PredictionProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3635912(1626-1633)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3605098.3635912
Cui RAralikatte RLent HHershcovich D(2022)Compositional Generalization in Multilingual Semantic Parsing over WikidataTransactions of the Association for Computational Linguistics10.1162/tacl_a_0049910(937-955)Online publication date: 7-Sep-2022
https://doi.org/10.1162/tacl_a_00499
Jiang LUsbeck RAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Knowledge Graph Question Answering Datasets and Their GeneralizabilityProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531751(3209-3218)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531751

Index Terms

Ranking Knowledge Graphs By Capturing Knowledge about Languages and Labels
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Question answering
  2. World Wide Web
    1. Web applications
      1. Crowdsourcing
        Answer ranking
    2. Web data description languages
      1. Semantic web description languages
        Resource Description Framework (RDF)

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Published In

cover image ACM Conferences

K-CAP '19: Proceedings of the 10th International Conference on Knowledge Capture

September 2019

281 pages

ISBN:9781450370080

DOI:10.1145/3360901

General Chairs:
Mayank Kejriwal
University of Southern California Information Sciences Institute, USA
,
Pedro Szekely
University of Southern California Information Sciences Institute, USA
,
Program Chair:
Raphaël Troncy
EURECOM, France

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGAI: ACM Special Interest Group on Artificial Intelligence

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 September 2019

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K-CAP '19

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SIGAI

K-CAP '19: Knowledge Capture Conference

November 19 - 21, 2019

CA, Marina Del Rey, USA

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Overall Acceptance Rate 55 of 198 submissions, 28%

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Cited By

Russo MSawischa SVidal MHong JPark J(2024)Tracing the Impact of Bias in Link PredictionProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3635912(1626-1633)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3605098.3635912
Cui RAralikatte RLent HHershcovich D(2022)Compositional Generalization in Multilingual Semantic Parsing over WikidataTransactions of the Association for Computational Linguistics10.1162/tacl_a_0049910(937-955)Online publication date: 7-Sep-2022
https://doi.org/10.1162/tacl_a_00499
Jiang LUsbeck RAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Knowledge Graph Question Answering Datasets and Their GeneralizabilityProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531751(3209-3218)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531751

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