In this episode, we discuss the profound shift in the search paradigm brought about by the rise of Generative Artificial Intelligence (GAI) and Large Language Models (LLMs).

It was based on the article I published yesterday, which was initiated after I saw the video presented by Professor Jenna Hartel, offering a detailed analysis of Olaf Sundin's conference paper. The paper theorizes how GAI is forcing a re-evaluation of the concepts of search, sources, and information evaluation.

To write the blog post, I used Sundin's work as a starting point to argue that GAI is causing the "death of sources," since systems now provide direct answers instead of directing users to source documents.

We then agreed that this shift undermines the traditional evaluation of information and concluded that the SEO solution (Search Engine Optimization) professionals should focus on semantic SEO, making content structured and semantically rich so that it becomes a reliable source of facts that feeds AI algorithms.

This episode is based on the Semantic Blog post and offers a comprehensive overview of the fundamentals, development, and advanced applications of ontologies in the technology landscape between 2022 and 2025.

We define ontologies as the backbone of semantic understanding in Artificial Intelligence (AI) systems, detailing their essential components (classes, properties, and axioms) and distinguishing them from Knowledge Graphs (KGs).

You will hear about Ontology Engineering, including development methodologies and the growing synergy with Large Language Models (LLMs), which are used both to accelerate ontology creation and to improve their accuracy.

We conclude the episode by illustrating the versatility of ontologies across use cases, including Explainable AI (XAI), semantic information retrieval, and the integration of heterogeneous data in sectors such as healthcare and construction.

Hello! I recently had one of those conversations that makes us pause and reflect on the speed at which the world of search is evolving.

In this episode, we dive into the heart of this change, exploring the synergy between three forces that are transforming the way we access and create information on the web: large-scale language models (LLMs), such as GPT or Gemini, knowledge graphs (KGs), and the search engines we use every day.

On the one hand, we have the fluidity of LLMs' language and, on the other, the precision and factuality of KGs, which map entities and their relationships. What happens when they come together? Search is no longer just a list of links; it becomes an intelligent system, capable of understanding the semantic intent of your query, resolving ambiguities, and delivering direct and accurate answers, rather than just blue links.

We discussed the techniques that make this possible, such as Retrieval-Augmented Generation (RAG), which combats the obsolescence of LLMs and reduces the dreaded "hallucinations" when querying direct facts from KGs.

And, of course, as an International SEO Specialist and consultant focused on Semantic SEO, the big question that drives me is: how does this redefine website optimization? The focus now shifts from the keyword to the meaning, structure, and semantic quality of content. It's a call for valuable content that demonstrates E-E-A-T (Experience, Expertise, Authority, and Trust) and for the correct implementation of structured data (Schema.org), ensuring that your brand is a well-defined entity in the search engine's knowledge map.

Finally, we leave you with a thought: will the convenience of having ready-made answers from AI make us lose the serendipitous discovery and critical thinking that comes from exploring the web?

Join us to understand this complex yet fascinating interconnection and discover the new priorities for your SEO.

You can use my Notebook to ask questions and learn more about this topic: https://notebooklm.google.com/notebook/a98aa6db-9931-473f-836c-6a0b0f4bb3a6