Siamese networks vs Semantic similarity (may be gensim)
Siamese and semantic similarity are not same.
See example here: 
Siamese are effectively used for image classifications , but there is a paper publication by Cornell university for text classification as well.
Now for your question, we train siamese in a way such that one is positive and one is negative. 
Lets talk about job title called java developer , we train it with java developer as input and j2ee developer as output in one network. In another network we train java developer as input and QA engineer as output but we dont change weights of the network.

Since we train a neural network with positive and negative so that siamese networks learns the positives and hence its also called one shot learning etc..

Now coming back to semantic similarity- its a unsupervised learning wherein the network will place similar objects together, the cosine distance that we get from gensim is how close one object to the other.