How to build a conceptual search engine?
First , write a piece of python code that will return you pineapple , orange , papaya when you input apple. By focusing on "is" relation of semantic network. Then continue with has a relationship and so on.
I think at the end , you might get a fairly sufficient piece of code for a school project.
I would like to build an internal search engine (I have a very large collection of thousands of XML files) that is able to map queries to concepts.  For example, if I search for "big cats", I would want highly ranked results to return documents with "large cats" as well.  But I may also be interested in having it return "huge animals", albeit at a much lower relevancy score.  
I'm currently reading through the Natural Language Processing in Python book, and it seems WordNet has some word mappings that might prove useful, though I'm not sure how to integrate that into a search engine.  Could I use Lucene to do this? How?
From further research, it seems "latent semantic analysis" is relevant to what I'm looking for but I'm not sure how to implement it.  
Any advice on how to get this done?