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        <title>Towards Effective Correction Methods Using WordNet Meronymy Relations</title>
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        <namePart type="given">Javier</namePart>
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    <abstract>In this paper, we analyse and compare several correction methods of knowledge resources with the purpose of improving the abilities of systems that require commonsense reasoning with the least possible human-effort. To this end, we cross-check the WordNet meronymy relation member against the knowledge encoded in a SUMO-based first-order logic ontology on the basis of the mapping between WordNet and SUMO. In particular, we focus on the knowledge in WordNet regarding the taxonomy of animals and plants. Despite being created manually, these knowledge resources — WordNet, SUMO and their mapping — are not free of errors and discrepancies. Thus, we propose three correction methods by semi-automatically improving the alignment between WordNet and SUMO, by performing some few corrections in SUMO and by combining the above two strategies. The evaluation of each method includes the required human-effort and the achieved improvement on unseen data from the WebChild project, that is tested using first-order logic automated theorem provers.</abstract>
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%0 Conference Proceedings
%T Towards Effective Correction Methods Using WordNet Meronymy Relations
%A Álvez, Javier
%A Gonzalez-Dios, Itziar
%A Rigau, German
%Y Rigau, German
%Y Bond, Francis
%Y Rademaker, Alexandre
%S Proceedings of the 12th Global Wordnet Conference
%D 2023
%8 January
%I Global Wordnet Association
%C University of the Basque Country, Donostia - San Sebastian, Basque Country
%F alvez-etal-2023-towards
%X In this paper, we analyse and compare several correction methods of knowledge resources with the purpose of improving the abilities of systems that require commonsense reasoning with the least possible human-effort. To this end, we cross-check the WordNet meronymy relation member against the knowledge encoded in a SUMO-based first-order logic ontology on the basis of the mapping between WordNet and SUMO. In particular, we focus on the knowledge in WordNet regarding the taxonomy of animals and plants. Despite being created manually, these knowledge resources — WordNet, SUMO and their mapping — are not free of errors and discrepancies. Thus, we propose three correction methods by semi-automatically improving the alignment between WordNet and SUMO, by performing some few corrections in SUMO and by combining the above two strategies. The evaluation of each method includes the required human-effort and the achieved improvement on unseen data from the WebChild project, that is tested using first-order logic automated theorem provers.
%U https://aclanthology.org/2023.gwc-1.4
%P 31-40

Markdown (Informal)

[Towards Effective Correction Methods Using WordNet Meronymy Relations](https://aclanthology.org/2023.gwc-1.4) (Álvez et al., GWC 2023)

• Towards Effective Correction Methods Using WordNet Meronymy Relations (Álvez et al., GWC 2023)

ACL

• Javier Álvez, Itziar Gonzalez-Dios, and German Rigau. 2023. Towards Effective Correction Methods Using WordNet Meronymy Relations. In Proceedings of the 12th Global Wordnet Conference, pages 31–40, University of the Basque Country, Donostia - San Sebastian, Basque Country. Global Wordnet Association.