Large language models (LLMs) have revolutionized human-machine interaction with their ability to converse and perform various language tasks. This study investigates the potential of LLMs for knowledge formalization using well-defined vocabularies, specifically focusing on OntoLex-Lemon. As a preliminary exploration, we test four languages (English, Italian, Albanian, Romanian) and analyze the formalization quality of nine words with varying characteristics applying a multidimensional evaluation approach. While manual validation provided initial insights, it highlights the need for developing scalable evaluation methods for future large-scale experiments. This research aims to initiate a discussion on the potential and challenges of utilizing LLMs for knowledge formalization within the Semantic Web framework.

With advances in the field of Linked (Open) Data (LOD), language data on the LOD cloud has grown in number, size, and variety. With an increased volume and variety of language data, optimizations of methods for distributing, storing, and querying these data become more central. To this end, this position paper investigates use cases at the intersection of LLOD and Big Data, existing approaches to utilizing Big Data techniques within the context of linked data, and discusses the challenges and benefits of this union.

Understanding the relation between the meanings of words is an important part of comprehending natural language. Prior work has either focused on analysing lexical semantic relations in word embeddings or probing pretrained language models (PLMs), with some exceptions. Given the rarity of highly multilingual benchmarks, it is unclear to what extent PLMs capture relational knowledge and are able to transfer it across languages. To start addressing this question, we propose MultiLexBATS, a multilingual parallel dataset of lexical semantic relations adapted from BATS in 15 languages including low-resource languages, such as Bambara, Lithuanian, and Albanian. As experiment on cross-lingual transfer of relational knowledge, we test the PLMs’ ability to (1) capture analogies across languages, and (2) predict translation targets. We find considerable differences across relation types and languages with a clear preference for hypernymy and antonymy as well as romance languages.