How to get related topics from a present wikipedia article?
You can scrape the categories if you want. If you're working with python, you can read the wikitext directly from their API, and use mwlib to parse the article and find the links.
A more interesting but harder to implement approach would be to create clusters of related terms, and given the list of terms extracted from an article, find the closest terms to them.
I am writing a user-app that takes input from the user as the current open wikipedia page. I have written a piece of code that takes this as input to my module and generates a list of keywords related to that particular article using webscraping and natural language processing.
I want to expand the functionality of the app by providing in addition to the keywords that i have identified, a set of related topics that may be of interest to the user. Is there any API that wikipedia provides that will do the trick. If there isn't, Can anybody Point me to what i should be looking into (incase i have to write code from scratch). Also i will appreciate any pointers in identifying any algorithm that will train the machine to identify topic maps. I am not seeking any paper but rather a practical implementation of something basic
so to summarize, 

I need a way to find topics related to current article in wikipedia (categories will also do)
I will also appreciate a sample algorithm for training a machine to identify topics that usually are related and clustered.

ps. please be specific because i have researched through a number of obvious possibilities
 appreciate it thank you