I'm always surprised when articles like these don't talk about the meaning behind the original phrase. The semantic web came to be because there existed a need for computers to understand the contents of webpages, which were invariably human-generated.
We now have a huge set of tools for that under the broader AI/ML umbrella - ML is obviously imperfect, but its cautious utilization across various industries is, to me, a step in the right direction. There's simply no need to pigeonhole ourselves into a "semantic web" data model that might not fit a particular topic or application.
I personally think that embeddings (https://milvus.io/docs/v1.1.0/vector.md) will eventually take over semantic search applications. NLP, for better or worse, have seen great success naively throwing text into massive pre-trained models (I say naively because a lot of these models still think Obama or Trump is president). We've also made great progress unifying the architecture of NLP and CV models via transformer architectures, and we're now seeing lots of CV applications follow suit.
We now have a huge set of tools for that under the broader AI/ML umbrella - ML is obviously imperfect, but its cautious utilization across various industries is, to me, a step in the right direction. There's simply no need to pigeonhole ourselves into a "semantic web" data model that might not fit a particular topic or application.
I personally think that embeddings (https://milvus.io/docs/v1.1.0/vector.md) will eventually take over semantic search applications. NLP, for better or worse, have seen great success naively throwing text into massive pre-trained models (I say naively because a lot of these models still think Obama or Trump is president). We've also made great progress unifying the architecture of NLP and CV models via transformer architectures, and we're now seeing lots of CV applications follow suit.