It's time to start talking about web 3.0 again

It’s been over a decade since the concept of “Web 3.0” was first introduced, and since then, the term has largely faded into obscurity. While many may have forgotten about the concept of a semantic web, it’s time to start talking about it again. With advancements in artificial intelligence and machine learning, the need for a more intelligent, semantic web is more pressing than ever.

Before diving into why it’s time to revisit the concept of Web 3.0, it’s important to understand what it is. Web 3.0, also known as the “semantic web,” is the next evolution of the internet. It’s a more intelligent web that’s able to understand the meaning behind data, making it easier to organize, interpret, and connect information. Essentially, the semantic web aims to make the internet more intuitive and user-friendly by making it easier for computers to understand and process data.

So why did the concept of Web 3.0 fade into obscurity? There are a few reasons for this. First, the term “Web 3.0” was initially introduced during a time when Web 2.0 was still in its infancy. While Web 2.0 brought us social media, online collaboration, and user-generated content, it didn’t fully realize the promise of a more intelligent web.

Second, the idea of a semantic web was also met with skepticism from some who believed it was too ambitious and unrealistic. Some argued that the technology required to create a semantic web was still in its early stages and that it would take years, if not decades, to fully develop.

It did not help that the term was hijacked på the Blockchain crowd. The term Web3, the kind that is often used by tech bros and bitcoin traders, has nothing to do with Web 3.0. Completely different and the similarity in name has hurt the development and legitimacy of what Tim Berners-Lee initially described as the web of data, the data web, Web 3.0.

Despite these challenges, the need for a more intelligent web is more pressing than ever. With the rise of artificial intelligence and machine learning, the ability to understand the meaning behind data is becoming increasingly important. In order to fully realize the potential of AI and machine learning, we need a more intelligent web.

One of the key benefits of a semantic web is that it can help make data more discoverable. By using metadata to describe the meaning behind data, it becomes easier to search and find information.

This is particularly important in fields such as healthcare, where accurate and timely information can be critical to patient care.

Another benefit of a semantic web is that it can help break down the barriers between different data sources. In today’s world, data is often siloed within different organizations and systems. A semantic web can help bring all of this data together in a more meaningful way, allowing for greater insights and connections to be made.

By breaking those silo walls data can become free to intermingle with other similar data. Data that may be structured in a different way but on a fundamental level is still the same. Just with people’s walls to divide us tends to be a bad idea.

So, how can we start working towards a semantic web? One approach is to start focusing on data standards and ontologies. A data standard is a set of rules that define how data should be structured and organized.

Ontologies, on the other hand, are a way of defining the relationships between different types of data.

By focusing on the structure and classification of the data, we can start to build a more intelligent and open web. This will require collaboration between different organizations and industries as well as a concerted effort to adopt and implement these standards and ontologies. This will cause obvious friction yet is paramount to the success in this endeavor. We must break through these walls if we are ever going to see the full benefits of the next generation of the web.

Another approach is to focus on developing more intelligent algorithms and systems that can process and understand data in a more meaningful way. This will require continued investment in artificial intelligence and machine learning, as well as a focus on developing more sophisticated natural language processing capabilities.

This is the current solution and over time an herculean effort has been made to invent, build and train models that can find structure and meaning out of largely unstructured data.

But think about what feats could be achieved if we combine these techniques. AI could and would be able to process data faster than ever and the structured data could be consumed and utilized in far ‘lesser’ applications than the once running a full fledged AI in the background. Data itself could become something larger than itself as we structure and organize it. The future is bright already but could be even brighter.

In conclusion, it’s time to start talking about Web 3.0 again. With advancements in AI and machine learning, the need for a more intelligent, semantic web is more pressing than ever. While the concept of Web 3.0 may have faded into obscurity, the need for it has not.