The growing impact of Large Language Models has highlighted the need for explicit, interpretable linguistic knowledge. Lexical resources respond to this need by offering structured representations that complement and constrain the implicit semantics of neural models. This paper presents an extension of CompL-it, currently the most comprehensive open computational lexicon of Italian. Building on the semantic layer inherited from LexicO—itself derived from the PAROLE-SIMPLE-CLIPS resource—the work enriches CompL-it with semantic traits and references to semantic types. Moreover, an experiment was conducted to generate missing definitions through an automatic process supported by LLMs. The resulting resource thus combines human-curated and machine-extended knowledge, ensuring both linguistic precision and scalability. This enriched semantic layer enhances CompL-it’s interoperability within the Linguistic Linked Data framework and strengthens its usability for NLP tasks such as word sense disambiguation, semantic role labelling, and knowledge grounding.

Semantic annotation of text requires the dynamic merging of linguistically structured information and a “world model”, usually represented as a domain-specific ontology. On the other hand, the process of engineering a domain-ontology through semi-automatic ontology learning system requires the availability of a considerable amount of semantically annotated documents. Facing this bootstrapping paradox requires an incremental process of annotation-acquisition-annotation, whereby domain-specific knowledge is acquired from linguistically-annotated texts and then projected back onto texts for extra linguistic information to be annotated and further knowledge layers to be extracted. The presented methodology is a first step in the direction of a full “virtuous” circle where the semantic annotation platform and the evolving ontology interact in symbiosis. As a case study we have chosen the semantic annotation of product catalogues. We propose a hybrid approach, combining pattern matching techniques to exploit the regular structure of product descriptions in catalogues, and Natural Language Processing techniques which are resorted to analyze natural language descriptions. The semantic annotation involves the access to the ontology, semi-automatically bootstrapped with an ontology learning tool from annotated collections of catalogues.

In this paper we present an original approach to natural language query interpretation which has been implemented withinthe FuLL (Fuzzy Logic and Language) Italian project of BC S.r.l. In particular, we discuss here the creation of linguisticand ontological resources, together with the exploitation of existing ones, for natural language-driven database access andretrieval. Both the database and the queries we experiment with are Italian, but the methodology we broach naturally extends to other languages.