34 The paradigm of neuro-symbolic Artificial Intelligence is receiving an increasing attention in the last years to improve the results of intelligent systems by combining symbolic and subsymbolic methods. For example, existing Large Language Models (LLMs) could be enriched by taking into account background knowledge encoded using semantic technologies, such as Linguistic Linked Data (LLD). In this paper, we claim that LLD can aid Large Language Models by providing the necessary information to compute the number of poetic syllables, which would help LLMs to correctly generate poems with a valid metric. To do so, we propose an encoding for syllabic structure based on an extension of RDF vocabularies widely used in the field: POSTDATA and OntoLex-Lemon.

The OntoLex-Lemon model provides a vocabulary to enrich ontologies with linguistic information that can be exploited by Natural Language Processing applications. The increasing uptake of Lemon illustrates the growing interest in combining linguistic information and Semantic Web technologies. In this paper, we present Fuzzy Lemon, an extension of Lemon that allows to assign an uncertainty degree to lexical semantic relations. Our approach is based on an OWL ontology that defines a hierarchy of data properties encoding different types of uncertainty. We also illustrate the usefulness of Fuzzy Lemon by showing that it can be used to represent the confidence degrees of automatically discovered translations between pairs of bilingual dictionaries from the Apertium family.