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Deep learning currently dominates the benchmarks for various NLP tasks and,at the basis of such systems, words are frequently represented as embeddings--vectors in a low dimensional space-- learned from large text corpora andvarious algorithms have been proposed to learn both word and conceptembeddings. One of the claimed benefits of such embeddings is that they captureknowledge about semantic relations. Such embeddings are most often evaluatedthrough tasks such as predicting human-rated similarity and analogy which onlytest a few, often ill-defined, relations. In this paper, we propose a methodfor (i) reliably generating word and concept pair datasets for a wide number ofrelations by using a knowledge graph and (ii) evaluating to what extentpre-trained embeddings capture those relations. We evaluate the approachagainst a proprietary and a public knowledge graph and analyze the results,showing which lexico-semantic relational knowledge is captured by currentembedding learning approaches.

Assessing the Lexico-Semantic Relational Knowledge Captured by Word and Concept Embeddings