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``Classical'' word embeddings, such as Word2Vec, have been shown to capturethe semantics of words based on their distributional properties. However, theirability to represent the different meanings that a word may have is limited.Such approaches also do not explicitly encode relations between entities, asdenoted by words. Embeddings of knowledge bases (KB) capture the explicitrelations between entities denoted by words, but are not able to directlycapture the syntagmatic properties of these words. To our knowledge, recentresearch have focused on representation learning that augment the strengths ofone with the other. In this work, we begin exploring another approach usingcontextual and KB embeddings jointly at the same level and propose two tasks --an entity typing and a relation typing task -- that evaluate the performance ofcontextual and KB embeddings. We also evaluated a concatenated model ofcontextual and KB embeddings with these two tasks, and obtain conclusiveresults on the first task. We hope our work may contribute as a basis formodels and datasets that develop in the direction of this approach.

Exploring the Combination of Contextual Word Embeddings and Knowledge Graph Embeddings