Describing Wikimedia Commons images using Wikidata’s structured data enables a wide range of automation tasks, such as search and organization, as well as downstream tasks, such as labeling images or training machine learning models. However, there is currently a lack of structured data-labelled images on Wikimedia Commons.To close this gap, we propose the task of Visual Entity Linking (VEL) for Wikimedia Commons, in which we create new labels for Wikimedia Commons images from Wikidata items. VEL is a crucial tool for improving information retrieval, search, content understanding, cross-modal applications, and various machine-learning tasks. In this paper, we propose a method to create new labels for Wikimedia Commons images from Wikidata items. To this end, we create a novel dataset leveraging community-created structured data on Wikimedia Commons and fine-tuning pre-trained models based on the CLIP architecture. Although the best-performing models show promising results, the study also identifies key challenges of the data and the task.

In semantic typology, colexification refers to words with multiple meanings, either related (polysemy) or unrelated (homophony). Studies of cross-linguistic colexification have yielded insights into, e.g., psychology, historical linguistics and cognitive science (Xu et al., 2020; Brochhagen and Boleda, 2022; Schapper and Koptjevskaja-Tamm, 2022). While NLP research up until now has mainly focused on integrating syntactic typology (Naseem et al., 2012; Ponti et al., 2019; Chaudhary et al., 2019; Üstün et al., 2020; Ansell et al., 2021; Oncevay et al., 2022), we here investigate the potential of incorporating semantic typology, of which colexification is an example. We propose a framework for constructing a large-scale synset graph and learning language representations with node embedding algorithms. We demonstrate that cross-lingual colexification patterns provide a distinct signal for modelling language similarity and predicting typological features. Our representations achieve a 9.97% performance gain in predicting lexico-semantic typological features and expectantly contain a weaker syntactic signal. This study is the first attempt to learn language representations and model language similarities using semantic typology at a large scale, setting a new direction for multilingual NLP, especially for low-resource languages.