Help reducing a set of features
If I understand correctly you want to cluster urls by using the keywords extracted as features. As these features are really sparse, you can try to use dimensionality reductions methods to help you.

One way is to treat each URL keywords as a document. Then you can use document embeddings algorithms such as LDA or doc2vec that learn denser representations of your documents.

If you want to group keywords, you can try to use word embeddings methods that learn representations of words. Using this you can then measure the similarity between words and groups of words.
An example is the well-known word2vec. Recent methods like FastText can be alternative that take the morphology into account.