This episode was based on the article "Domain Analysis in SEO" from the Semantic Blog, which explores domain analysis as a fundamental tool in Information Science and its application in semantic SEO. I sought to detail how domain analysis helps understand, organize, and retrieve information in specific areas of knowledge, going beyond simple keyword research.

The discussion covers Birger Hjørland's eleven approaches to domain analysis, illustrating their relevance for creating robust taxonomies, ontologies, and workflows in SEO. The article also draws a parallel between domain analysis and domain modeling, highlighting their differences and the value of the former for developing effective digital solutions and promoting interdisciplinarity.

In this episode, we propose a new methodology for web content management, focusing on users' information needs diverging from Google's current search intent-based approach. You'll hear about criticisms of the search intent model for its simplicity and for ignoring users' subjective aspects, analyzing its historical Evolution, as well as Google's four intent classifications. In contrast, we present Carol Kuhlthau's Information Search Process (ISP), which considers the emotional, cognitive, and physical aspects of search and adapts three of its stages—Initiation, Exploration, and Collection—to formulate a new framework for creating content briefs. We advocate integrating concepts from library science and information science to improve SEO and content management, aiming to meet users' complex needs.

In today's episode, "Query Unfolding: A Data-Driven Approach to AI Search Visibility," we explore how Google's AI search breaks down an initial query into multiple contextual subqueries, a process called "query fan-out." We discuss how traditional SEO tools are inadequate for this new reality, proposing a new framework and a simulator in Google Colab to predict the likely follow-up questions AI might generate. This episode is based on my article of the same name, where I comment on Andrea Volpini's original text, adding insights into Ranganathan's concept of facets and delving deeper into the implications of "fan-out" for information retrieval with LLMs and knowledge graphs. In essence, the core text offers a 10-principle strategic guide to optimizing content for AI search, emphasizing semantic coverage, conversational adaptability, and understanding how AI fragments and interprets content to generate comprehensive answers.