The Semantic Web Renaissance

Why the Semantic Web Failed

The Beginning

In 2004, the w3c came together to outline a way by which the web and semantics could be brought together. They called it the Semantic Web. Unfortunately, what the w3c came to learn was the foundation by which one works with data semantics and the web are unfortunately orthogonal. Most people want the web to be simple, somewhere they can hang out, a town square, something akin to Socrate's Agora. But, the semantic web was anything but simple. Learning to navigate the hybrid mesh of RFCs, contradictory specifications, and complicated file formats was more than enough to drive an HTML author to Microsoft Word. However, a platform as rich as the web does not have to be be the same thing to two people, and this ultimately is where I believe the semantic web failed.

Ultimately, the authors of Semantic Web specifications refused to hide the complexity of the underlying data structure, and left authors to make the massive leap from communicating to authoring a graph (and not just any graph, a graph with a massive amount of Rules). The inference engine and the calculus behind it were not merited. What was merited was a product that introduced individuals to the power of meaning to generate insights. In other words, data semantics were never for the everyman, they were never for the Marketer or Blog author or for the social media user. The Semantic Web was always for the data nerd, and for a very specific kind of data nerd who was willing and capable of reading the massive amount of specs (see here, here, and here for a few), and use the power of decentralization to make sense of it all.

In other words, the concept of the semantic web was good, leverage the power of the web as a decentralized data structure to drive crowd-sourced meaning at scale. Take the massive amount of information that was getting pumped into the web in form of content and organize it intelligently, and build underlying models that deeply understood the semantics of the information getting published.

The Good Parts

There was and is lots of good in treating the web as a semantic data source. First, it gets the run-of-the-mill web developer thinking in terms of graphs in addition to applications. It allows for the richness of the web to be transferred to apis, and ultimately Graphql was highly influenced by the Semantic Web. Applications like CWM suffered from this as well. They were so busy statically parsing that they lost the meaning of how mass audiences use the internet. This is why the Swap utilities remain popular despite some audiences having moved on to MCP and A2A as application platforms of choice

Why LLMs will usher in the Semantic Web Renaissance

What the Semantic Web was missing was a unifying “killer app” that could turn the graph into something more approachable. The LLMs is exactly that. In a sense, what ChatGPT and its Transformer brethren have accomplished is to turn semantics into an obviously useful system. It makes semantics something easy to interact with. It is what these data-starved LLMs require in order to be truly useful. Imagine for a second a world where explainable LLMs were a given, and we could give the models the context they needed without any effort. To me, this is the promise of the Semantic web. As a data engineer, I believe LLMs will show us why we (as data professionals) need the semantic web. We need it to inject existing semantics into our systems to give LLMs context and drive intelligent traversal of facts, figures, relationships, even information about the neural network itself.

Created: [[2025_11_11|November 11, 2025]] Last Updated: [[2025_11_11]]