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

This episode offers a detailed definition of Semantic SEO, contrasting it with traditional SEO practices based on search volume and keywords. Semantic SEO is presented as a method that focuses on constructing meaning and aligning concepts to help search engines understand content, increasing its perceived quality and relevance.

We emphasize that Semantic SEO does not use keywords and differs from terms like Entity SEO and Topic Cluster, although it does use entities to resolve ambiguities. The essence of this approach lies in analyzing domain knowledge and structuring the project from the inside out, rather than focusing externally on competitors or Search trends.

Address the issue. Today, we have a different episode. We analyze ODKE+, a pipeline that promises to solve a significant problem with graph usage in Semantic SEO projects: their freshness and reliability.

The article comprehensively introduces ODKE+, a production-grade system developed by Apple for the automatic extraction of open-domain knowledge from web sources, using Large Language Models (LLMs) and ontologies. This system is designed to maintain up-to-date and complete knowledge graphs (KGs), addressing the challenges of volume, variety, veracity, and velocity associated with online information. ODKE+ employs a modular pipeline that includes components such as an Extraction Launcher to detect obsolete facts, a hybrid Knowledge Extractor (based on patterns and ontology-driven LLMs), and a Corroborator with a lightweight LLM for grounding verification, ensuring high factual accuracy of 98.8% across millions of ingested facts. The system supports both batch and streaming modes, significantly improving data coverage and freshness compared to traditional methods.

This episode is based on an article written by Alexander Rodrigues Silva of the Semantic SEO blog, which presents an in-depth analysis of the inner workings of Large-Scale Language Models (LLMs), particularly the Transformer engine and its central Attention component. The author shares six surprising discoveries that emerged from a series of interactions with his AI agent, offered as a service called Agent+Semantic. The explanations focus on how words acquire contextual meaning through initial vectors and internal Query, Key, and Value dialogues, showing that meaning is encoded as geometric directions in a multidimensional space. Finally, the text demystifies the concept of machine "learning," comparing it to a mathematical optimization process, like a ball rolling downhill in the cost function.

Today's episode, excerpted from "Blog Seântico" by Alexander Rodrigues Silva, offers a detailed definition of Semantic SEO, contrasting it with traditional keyword-based SEO practices. The author explains that Semantic SEO focuses on constructing meaning and conceptual alignment to improve search engines' understanding of content, qualifying it as high-quality. This strategy stands out for not using keyword research or search volume, focusing instead on providing metadata that semantically connects data, increasing content relevance. Furthermore, the text clarifies that, although it uses entities to resolve ambiguities, Semantic SEO is not synonymous with Entity SEO or Topic Cluster. The main difference lies in the approach, where Semantic SEO looks "from the inside out" of the organization, deeply analyzing the knowledge domain to structure the optimization strategy.

In this episode, we offer a comprehensive overview of Domain Analysis (DA), presenting it as a central socio-cognitive and methodological approach to Knowledge Organization (KO) in Information Science (IS). The texts we use as a basis examine the historical and conceptual development of DA, crediting its introduction into IS to authors such as Hjørland and Albrechtsen, and discussing how it contrasts with more formalistic approaches by emphasizing the study of domains as discursive communities embedded in the social division of labor. There is a detailed discussion of Hjørland's eleven initial approaches to DA, including bibliometric studies, user analysis, and document analysis, with an emphasis on their combined application. Furthermore, academic articles exemplify the application of DA in specific contexts, such as the analysis of scientific production in Brazilian journals and the relationship between KO and archival science, while the YouTube transcript complements it with reflections on the role of DA as a methodology that underpins information systems.