How can I get semantic word embneddings for compound terms?
If you want an exact answer, please provide a precise question i.e. define what data you have, and what you exactly wants. 

This said, in a general manner, you need a dataset of texts that contain these compound terms. How to treat compound terms is a whole scientific field in itself, but since you're talking about semantic word embeddings, I suggest you take a look at the article Distributed Representations of Words and Phrases
and their Compositionality. The same guys who introduced word2vec describe here a simple method to go from word representation to phrase representation, giving btw a way to merge compound terms in single terms. The words "microsoft excel" become "microsoft_excel" and get their own unique embedding. 

If you want a python implementation for that, take a look at the gensim.models.phrase class. This does the same work as presented in the previous article.