  Concepts are used to solve the term-mismatch problem. However, we need an
effective similarity measure between concepts. Word embedding presents a
promising solution. We present in this study three approaches to build concepts
vectors based on words vectors. We use a vector-based measure to estimate
inter-concepts similarity. Our experiments show promising results. Furthermore,
words and concepts become comparable. This could be used to improve conceptual
indexing process.
