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Isn't it what word2vec does? The algorithm gives vectors to words in such way that words appearing in similar contexts have similar vectors. I guess new deep networks give even better results.

Unsupervised methods like w2v look more scalable than hand made ontologies. Maybe ontologies help refine the result of word embeddings in this case?

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Please remember, that these ontologies describe biological systems and are used as a standard for common nomenclature. It's orders of magnitudes harder to derive an ontology describing the localization of proteins in the cell by an algorithm, compared to w2v. Even then, there would be the fundamental issue that the ontology needs to be consistent over time in order to be useful.

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