PREMOVE in LiLa: Integrating Latin Preverbed Motion Verbs with WordNet and VerbNet

PREMOVE is a diachronic dataset of Ancient Greek and Latin PREverbed MOtion VErbs, providing manually curated morphological, syntactic, and semantic annotations for almost three thousand verbal occurrences. This paper presents the integration of PREMOVE into the LiLa Knowledge Base of Latin, linking its semantic annotations to WordNet (WN) and VerbNet (VN). We describe the RDF conversion using OntoLex-Lemon and FrAC, enabling explicit modelling of token-level attestations and dataset-level provenance. The resulting linked resource achieves full FAIR compliance and supports complex SPARQL queries, allowing users to explore motion semantics across lexical, textual, and semantic layers. Example SPARQL queries demonstrate how researchers can retrieve attested forms for specific WN synsets or VN classes, supporting reproducible linguistic research and cross-resource exploration of motion semantics in ancient languages.

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Publication Details

Published
2026-04-30
DOI
https://doi.org/10.63317/3ifm66wvmf86
Primary Topic
Natural Language Processing Techniques
Type
article
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article

PREMOVE in LiLa: Integrating Latin Preverbed Motion Verbs with WordNet and VerbNet

Francesco Mambrini, Eleonora Litta, Marco Passarotti, Matteo Pellegrini et al.
Natural Language Processing Techniques
article

PREMOVE in LiLa: Integrating Latin Preverbed Motion Verbs with WordNet and VerbNet

Francesco Mambrini, Eleonora Litta, Marco Passarotti, Matteo Pellegrini, Giovanni Moretti, Andrea Alejandra Fariña
article en

Abstract

PREMOVE is a diachronic dataset of Ancient Greek and Latin PREverbed MOtion VErbs, providing manually curated morphological, syntactic, and semantic annotations for almost three thousand verbal occurrences. This paper presents the integration of PREMOVE into the LiLa Knowledge Base of Latin, linking its semantic annotations to WordNet (WN) and VerbNet (VN). We describe the RDF conversion using OntoLex-Lemon and FrAC, enabling explicit modelling of token-level attestations and dataset-level provenance. The resulting linked resource achieves full FAIR compliance and supports complex SPARQL queries, allowing users to explore motion semantics across lexical, textual, and semantic layers. Example SPARQL queries demonstrate how researchers can retrieve attested forms for specific WN synsets or VN classes, supporting reproducible linguistic research and cross-resource exploration of motion semantics in ancient languages.

Openalex Percentile: Top 8%
Natural Language Processing Techniques
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