Semantic search with NLP and elasticsearch
There may be several approaches with different implementation complexity.
The easiest one is to create list of topics (like plumbing), attach bag of words (like "pipe"), identify search request by majority of keywords and search only in specified topic (you can add field topic to your elastic search documents and set it as mandatory with + during search).
Of course, if you have lots of documents, manual creation of topic list and bag of words is very time expensive. You can use machine learning to automate some of tasks. Basically, it is enough to have distance measure between words and/or documents to automatically discover topics (e.g. by data clustering) and classify query to one of these topics. Mix of these techniques may also be a good choice (for example, you can manually create topics and assign initial documents to them, but use classification for query assignment). Take a look at Wikipedia's article on latent semantic analysis to better understand the idea. Also pay attention to the 2 linked articles on data clustering and document classification. And yes, Maui Indexer may become good helper tool this way.
Finally, you can try to build an engine that "understands" meaning of the phrase (not just uses terms frequency) and searches appropriate topics. Most probably, this will involve natural language processing and ontology-based knowledgebases. But in fact, this field is still in active research and without previous experience it will be very hard for you to implement something like this.
You may want to explore https://blog.conceptnet.io/2016/11/03/conceptnet-5-5-and-conceptnet-io/.
It combines semantic networks and distributional semantics.
When most developers need word embeddings, the first and possibly only place they look is word2vec, a neural net algorithm from Google that computes word embeddings from distributional semantics. That is, it learns to predict words in a sentence from the other words around them, and the embeddings are the representation of words that make the best predictions. But even after terabytes of text, there are aspects of word meanings that you just won’t learn from distributional semantics alone.
Some results
The ConceptNet Numberbatch word embeddings, built into ConceptNet 5.5, solve these SAT analogies better than any previous system. It gets 56.4% of the questions correct. The best comparable previous system, Turney’s SuperSim (2013), got 54.8%. And we’re getting ever closer to “human-level” performance on SAT analogies — while particularly smart humans can of course get a lot more questions right, the average college applicant gets 57.0%.