Freebase, after being bought by Google became the foundation of the Google Knowledge Graph (aka "things not links"). This kicked off an arms race between all the major search providers to build the largest and most complete knowledge graphs (or at least keep pace with Google [1]). Instead of waiting for folks to tag every single page, it turned out that simple patterns cross referenced across billions of pages were good enough to extract useful knowledge from unstructured text.
Some companies who had easier access to structured but dirty data (like LinkedIn and Facebook) were also able to utilize (and contribute to) all of that research by building their own knowledge graphs with names like the Social Graph and Economic Graph. Those in turn are helping to power a decent amount of their search and ad targeting capabilities as well as spawning some interesting work[2]
All those knowledge graphs became a major part of Siri, Alexa and Google Home's ability to answer a wide range of natural language queries. As well as being pretty fundamental to a lot of tech like semantic search, improved ecommerce search and a bunch of intent detection approaches for chatbots.
So yeah while the technology and associated research did turn out to be incredibly useful, adding fancier meta-tags to pages was not the direction that proved the most useful.
[1] https://ai.google/research/pubs/pub45634 [2] https://research.fb.com/publications/unicorn-a-system-for-se...