  An exciting frontier in natural language understanding (NLU) and generation
(NLG) calls for (vision-and-) language models that can efficiently access
external structured knowledge repositories. However, many existing knowledge
bases only cover limited domains, or suffer from noisy data, and most of all
are typically hard to integrate into neural language pipelines. To fill this
gap, we release VisualSem: a high-quality knowledge graph (KG) which includes
nodes with multilingual glosses, multiple illustrative images, and visually
relevant relations. We also release a neural multi-modal retrieval model that
can use images or sentences as inputs and retrieves entities in the KG. This
multi-modal retrieval model can be integrated into any (neural network) model
pipeline. We encourage the research community to use VisualSem for data
augmentation and/or as a source of grounding, among other possible uses.
VisualSem as well as the multi-modal retrieval models are publicly available
and can be downloaded in this URL: https://github.com/iacercalixto/visualsem
