[throwaway984393]

Well semantic web search is less about traditional web search and more about semantic relationships between terms using ontologies.

If you search for "pipe A500", the search engine would deconstruct that. It would see pipes have steel, and a coating for steel, and a grade of steel. It would see A500 is a grade of steel. Pipes don't have a grade A500, but tubes do, and tubes have the other classifications that pipes do. It may then conclude that while 'A500' and 'pipe' are not linked directly, a different term may be very similar and a more direct match ('tube'), and thus return 'A500 tube' results.

It seemed like the machine learning model was building relations between these different concepts and using them to improve the search, but without the intentional taxonomic mapping that semantic web uses. Semantic web is essentially more of a curated database of relationships, whereas the machine learning appears to be using another method to establish the relationships. I wonder if they're not doing basically the same thing.