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.

This paper presents the integration of the Leibniz List, a concept list from the Concepticon project, into the LiLa Knowledge Base of Latin interoperable resources. The modeling experiment was conducted using W3C standards like Ontolex and SKOS. This work, which originated in a project for a university course, is limited to a short list of words, but it already enables interoperability between the Concepticon and the language resources in a LOD architecture like LiLa. The integration enriches the LiLa ecosystem, allowing users to explore Latin lexicon from an onomasiological perspective and links concepts to lexical entries from various dictionaries and corpus attestations. The work showcases how standard Semantic Web technologies can effectively model and connect historical concept lists within larger linguistic knowledge infrastructures and provides an example for further experiments with the Concepticon’s data.

This paper describes the release as Linguistic Linked Open Data of DynaMorphPro, a lexical resource recording loanwords, conversions and class-shifts from Latin to Old Italian. We show how existing vocabularies are reused and integrated to allow for a rich semantic representation of these data. Our main reference is the OntoLex-lemon model for lexical information, but classes and properties from many other ontologies are also reused to express other aspects. In particular, we identify the CIDOC Concept Reference Model as the ideal tool to convey chronological information on historical processes of lexical innovation and change, and describe how it can be integrated with OntoLex-lemon.

This paper describes the steps undertaken to include data from Antonio Velez’s bilingual Latin-Portuguese dictionary (Index Totius Artis, 1744) into the LiLa Knowledge Base of interoperable linguistic resources for Latin. The paper focuses on how the lexical and lexicographic information of the source dictionary was modelled by using respectively the Lexicon Model for Ontologies (OntoLex-lemon) and its lexicog module. The linking process of the dictionary entries with those of the LiLa collection of Latin lemmas is detailed, discussing issues in dealing with ambiguities and typographical errors found in the source. The result is the first Latin-Portuguese lexical resource made interoperable with the (meta)data of the other linguistic resources for Latin interlinked in the LiLa Knowledge Base, providing new ways of assessing the dictionary information or using its content as starting point to explore the connections with other interlinked linguistic resources. A couple of use case scenarios illustrate those possibilities.

The large availability of processable textual resources for Classical Latin has made it possible to study Latin literature through methods and tools that support distant reading. This paper describes a number of experiments carried out to test the possibility of investigating the thematic distribution of the Classical Latin corpus Opera Latina by means of topic modeling. For this purpose, we train, optimize and compare two neural models, Product-of-Experts LDA (ProdLDA) and Embedded Topic Model (ETM), opportunely revised to deal with the textual data from a Classical Latin corpus, to evaluate which one performs better both on the basis of topic diversity and topic coherence metrics, and from a human judgment point of view. Our results show that the topics extracted by neural models are coherent and interpretable and that they are significant from the perspective of a Latin scholar. The source code of the proposed model is available at https://github.com/MIND-Lab/LatinProdLDA.

Although the Universal Dependencies initiative today allows for cross-linguistically consistent annotation of morphology and syntax in treebanks for several languages, syntactically annotated corpora are not yet interoperable with many lexical resources that describe properties of the words that occur therein. In order to cope with such limitation, we propose to adopt the principles of the Linguistic Linked Open Data community, to describe and publish dependency treebanks as LLOD. In particular, this paper illustrates the approach pursued in the LiLa Knowledge Base, which enables interoperability between corpora and lexical resources for Latin, to publish as Linguistic Linked Open Data the annotation layers of two versions of a Medieval Latin treebank (the Index Thomisticus Treebank).