You could use Lucene (or any text-search engine) to store your documents, combined with a custom stemmer to index your document text based on meaning (rather than word variations).
Normally, stemmers are used to convert all variations of a word to the base word stem. For example, although the document is stored and retrieved with text as-is, any of the words "sing, singing, sang, sung" would be indexed as "sing", so when a search is made using the search term "sing", you get a hit on all documents containing sing, singing, sang or sung.
Similarly, the search terms may also be stemmed, so searching for any of "sing, singing, sang or sung" will search as if "sing" is the search term.
Standard stemmers deal with the usual English variations of words, but you could create one that stems based on meaning. For example, you might create a stemmer that stems any of "problem, issue or complaint" to "problem", etc for all words you want to "link".
The advantage of using a stemmer is all the search-related heavy lifting is done for you by the text search engine (and besides, text search engines are incredibly fast!).
Wen it come to implementation, you could make the linkages data-driven, either generating the code for the stemmer based on data in a database, or make it dynamic and look up a database whenever a search/index operation is done, or somewhere in between - caching the values and refreshing them periodically.
