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One very simple thing it does is help you understand hierarchical semantic relationships. So, for example, you could have sibling => => brother
by zmmmmm 7y ago
One very simple thing it does is help you understand hierarchical semantic relationships. So, for example, you could have
sibling =>
=> brother
=> sister
Then you know when eg: parsing text that if you observe the symbol "sister" in one place and "sibling" in another, they could be the same thing. This kind of thing is used in the medical space where one doctor might discribe a problem using a general term and the next in a more specific way and you have to be able to match them.
- antpls 7y agoIsn'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?
- gewa 7y agoPlease 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.