Towards a Comprehensive English Wordnet-Wikidata Mapping

John P. McCrae, Johann Bergh, Krasimir Angelov


Abstract
In this study, we present a comprehensive investigation into the mapping of English Wordnet to Wikidata, focusing on the existing mappings created by different projects. We systematically analyze the current mapping methodologies and their effectiveness, highlighting the strengths and limitations of each approach. Through a comparative analysis, we identified overlaps and discrepancies among the mappings, revealing insights into the relationships between the data sets. Our findings underscore the need for a more unified dataset that consolidates disparate mappings into a comprehensive unified Wordnet-Wikidata mapping. We propose a novel construction methodology for this unified data set, taking advantage of existing mappings while addressing their shortcomings. In addition, we discuss future perspectives and advanced techniques for mapping the remaining unmapped records, such as machine learning algorithms. This work not only contributes to the enhancement of data interoperability between Wordnet and Wikidata but also sets the stage for future research aimed at refining mapping techniques and expanding coverage.
Anthology ID:
2026.lrec-main.933
Volume:
Proceedings of the Fifteenth Language Resources and Evaluation Conference
Month:
May
Year:
2026
Address:
Palma de Mallorca, Spain
Editors:
Stelios Piperidis, Núria Bel, Henk van den Heuvel, Nancy Ide, Simon Krek, Antonio Toral
Venue:
LREC
SIG:
Publisher:
ELRA Language Resource Association
Note:
Pages:
11916–11925
Language:
URL:
https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.933/
DOI:
Bibkey:
Cite (ACL):
John P. McCrae, Johann Bergh, and Krasimir Angelov. 2026. Towards a Comprehensive English Wordnet-Wikidata Mapping. International Conference on Language Resources and Evaluation, main:11916–11925.
Cite (Informal):
Towards a Comprehensive English Wordnet-Wikidata Mapping (McCrae et al., LREC 2026)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.933.pdf