We actually cover this somewhat in Relevant Search. (dm me if you'd like a discount). We discuss the basics of concept search, and demonstrate solutions where you have a Taxonomy of ideas.
What your describing it sounds like you want to figure out how often say "tata" cooccures with "jaguar" and discern some relationship automatically. You want to learn that jaguar has a high affinity to tata. Elasticsearch doesn't do this out-of-the-box. But, it sounds like what you're looking at is some kind of concept search. Specifically, you may want to look into the general field of topic modeling.
You want to group related concepts together. I've written about Latent Semantic Analysis. But this is a rather old technique, the two other big techniques are word2vec and latent Dirichlet allocation. These, in my experience, give better general purpose results.
There's a couple ways of viewing the process. The output of such a process can be a set of term affinities for a document. In a sense they help "fill out" the semantic space of the document. You can stuff this into another field if you like.
There's a lot of considerations for good topic modeling/concept search, if you're serious about it I'd seriously considering talking to an NLP expert, picking up a good book, and doing lots of research. In my experience bad concept search can actually make things worse, so proceed carefully.