How to maximise AI Search visibility through query fan-out insight (and KGraph)

In the era of 0-Click and AI Search, you must follow the framework I present in this deck:

1) Create a Brand Ontology, aka your Knowledge Graphs.
2) Merge your Knowledge Graphs
3) Investigate the Search Ecosystems around the Entities you target, and do a gap analysis of your Knowledge Graph model with search insights.
4) Improve taxonomy and navigation
5) Create your architecture around Content Hubs that not only target "your topics" in the informational facet, but also:
5.1) In all the micro-moments of the search and customer journey.
5.2) In the multimodal and omnichannel surfaces.
5.3) In the sentiments implied by the searches done by your targeted buyer and audience personas.

Transcript

1. Gianluca Fiorelli Strategic and International SEO Consultant at ILoveSEO.net How to maximise AI Search visibility through query fan-out insight (and KGraph) AI Innovators
2. The Context In a 0-click Search era, visibility is everything
3. https://ahrefs.com/blog/ai-overviews-reduce-clicks/ https://www.linkedin.com/posts/joshua-blyskal_chatgpt-referral- traffic-is-down-52-since-activity-7364003556087005185-BaJ0 https://datos.live/report/state-of-search-q2-2025/ https://www.tryprofound.com/blog/chatgpt-intent- landmark-study 49,6% of conversations are generative or with no search intent.
4. Visibility ≠ Clicks Visibility = Memory
5. https://www.advancedwebranking.com/bl og/attention-mechanisms-ai-search Awareness Attention Memorability Hyper personalization
7. Each element of our website must align to a phase of the Messy Middle: Nudge Explore Evaluate, Decide/Purchase.
8. Before the Messy Middle: the moment the Journey starts
9. The true Messy Middle. Here is where the Search / Customer Journeys happen... everywhere.
10. Here is where we target the intents
11. The end of Search / Customer Journeys on our website (or not...).
12. The Framework Designing for entity alignment, AI (and classic Search) retrievability, and human engagement.
13. The AI Search Visibility Framework BRAND ONTOLOGY (1) (Defining our brand domain and entities) BRAND ONTOLOGY (2) (Defining the relationships & merging KGraphs) TAXONOMY & NAVIGATION (Setting taxonomy as the AI Search internal map and maximize topical authority) CONTENT HUBS BASED ARCHITECTURE (Mapping entities for topical authority) ADVANCED CLUSTERING (Clustering for intent, format, sentiment/buyer persona) CONTENT CREATION (Structure content for AI retrievability and readers’ engagement) MEASURE & REFRESH (Track AI Search/SERP topic visibility & share of voice, and update for recency) QUERY EXPANSION & FULL INTENT COVERAGE (Beyond KWs: map entities’ semantic intent)
14. What about multilingual & multi-country websites?
15. AI Search is... complicated
17. Gemini, ChatGPT, Perplexity all weight: User Language, GEO Memory, Source Density by Locale.
18. LLMS substantially ignore the hreflang
19. The AI Search Visibility Framework BRAND ONTOLOGY (1) (Defining our brand domain and entities) BRAND ONTOLOGY (2) (Defining the relationships & merging KGraphs) TAXONOMY & NAVIGATION (Setting taxonomy as the AI Search internal map and maximize topical authority) CONTENT HUBS BASED ARCHITECTURE (Mapping entities for topical authority) ADVANCED CLUSTERING (Clustering for intent, format, sentiment/buyer persona) CONTENT CREATION (Structure content for AI retrievability and readers’ engagement) MEASURE & REFRESH (Track AI Search/SERP topic visibility & share of voice, and update for recency) QUERY EXPANSION & FULL INTENT COVERAGE (Beyond KWs: map entities’ semantic intent) L O C A L I S A Z I O N L A Y E R
20. Brand Ontology (1) Defining our brand domain concepts and entities.
22. Brand Ontology and Knowledge Graph The Brand The Game Star Wars Mini Painting
23. The Brand Company Identity Organization (the company itself: name, legal entity, brand) Founders / Executives / Key People Headquarters & Offices (addresses, geo-coordinates) Brand / Subsidiaries / Sister Companies Markets & Customers Target Audience / Buyer Personas Customer Segments (B2B, B2C, enterprise, SMB, etc.) Industry / Market (vertical, NAICS/SIC classification) Geographies / Regions Served Operations Business Units / Departments Processes (sales cycle, support, logistics) Partners / Vendors / Suppliers Technology Stack / Tools Reputation & Presence Reviews / Testimonials Awards / Certifications News / Press Mentions Social Profiles Financials & Legal Investors / Shareholders Funding Rounds Annual Revenue / KPIs Legal Status / Registrations
24. Brand Ontology and Knowledge Graph The Brand The Game Star Wars Mini Painting
25. Star Wars: Legion Factions Bounty Hunters Shadow Collective Galactic Republic Separatist Alliance ... Eras Fall of Jedi Reign of the Empire Age of Rebellion The New Republic ... Units' types Unit expansion Commander Operative Personnel ... Units, Characters Ahsoka Tano Stormtroopers Rebel Pathfinders Cassian Andor and K-2SO... Sets Star Wars: Legion Galactic Empire Blizzard Force Legion 501st Star Wars: Separatist Alliance ... Accessories & Upgrades Upgrade Card pack Imperial Bunker Rebel Troopers Upgrade ...
26. Localisation Game KGraph @prefix ex: <https://example.com/kg/> . @prefix schema: <https://schema.org/> . @prefix skos: <http://www.w3.org/2004/02/skos/core#> . ex:rebel-commandos-legion a ex:LegionUnit ; rdfs:label "Rebel Commandos"@en ; rdfs:label "Rebel Commandos"@es-ES ; rdfs:label "Comandos Rebeldes"@es-MX ; schema:areaServed "Global" ; ex:faction ex:rebel-alliance ; ex:representsCharacter ex:cassian- andor-lore ; skos:closeMatch ex:cassian-andor-lore ; schema:isPartOf ex:star-wars-legion- game . Core Idea • One IRI per unit / faction / era, not for language • Languages & Markets handled with labels + named graphs • Cross-graph links via bridging predicates, not owl:sameAs Takeaway Each game unit has one canonical IRI. Localised names are labels, not new entities.
27. Brand Ontology and Knowledge Graph The Brand The Game Star Wars Mini Painting
28. Star Wars Characters Skywalker lineage Rebellion icons Jedi/Sith Sequel Trilogy ... Planets & Locations Core Worlds (Coruscant, Corellia...) Mid Rim (Naboo, Kashyyyk...) Outer Rim (Tatooine, Hoth...) ... Organizations & Factions Jedi Order Sith Galactic Republic Separatist Alliance Galactic Empire Rebel Alliance Empire Remnants ... Species / Creatures Humans Wookies Zabrak Togruta Ewoks ... Droids Astromech droids Protocol droids Assassin droids Battle droids ... Battles / Events Clone Wars Battle of Yavin Battle of Hoth Battle of Kashyyyk Battle of Scarif ...
29. Localisation Lore KGraph @prefix ex: <https://example.com/kg/> . @prefix schema: <https://schema.org/> . @prefix owl: <http://www.w3.org/2002/07/owl#> . ex:cassian-andor-lore a ex:StarWarsCharacter ; rdfs:label "Cassian Andor"@en , "Cassian Andor"@es , "Cassian Andor"@it ; schema:appearsIn ex:rogue-one-film ; schema:memberOf ex:rebel-alliance ; owl:sameAs <https://www.wikidata.org/entity/Q27855776> . ex:rebel-alliance a ex:Faction ; rdfs:label "Rebel Alliance"@en , "Alianza Rebelde"@es , "Alleanza Ribelle"@it . Core Idea • Lore characters & events are distinct real-world entities. • owl:sameAs → external identity (Wikipedia...) • Game <-> Lore bridging uses ex:representsCharacter Takeaway Use owl:sameAs only for external truths, never to bridge domains within your own ontology.
30. Brand Ontology and Knowledge Graph The Brand The Game Star Wars Mini Painting
31. Mini Painting Materials & Media Paint types (acrylics, oils, inks...) Mediums & Additives (thinners, retarders) Others (pigments, pencils...) ... Tools & Equipment Brushes (synthetics, sables...) Airbrush (compressors, nozzle sizes...) Surface preparation tools Lighting and Workspace ... Miniature Subjects Tabletop Minis (Warhammer 40K, SW: Legion...) Scale models (tanks, airplanes...) Bust and Display sculptures ... Painting Techniques Fundamentals (basecoating, washing, layering...) Intermediate (feathering, web blending, stippling...) Advanced (NMM, TMM, OSL...) Expert (volumetric lightning...) Style variant (grimdark, eavy metal, comic...) Processes & Workflows Preparation (assembling, sanding, priming...) Painting workflow (sketch, basecoat, shade...) Post-processing (varnishing, sealing...) Experimentation / Mixed Media (paint types combos... ... Effects & Finishes Lighting Effects (OSL, backlighting... Surface finishes (matte, gloss...) Weathering (rust, mud...) Special FX (blood, energy glow... ...
32. Localisation Mini Painting KGraph @prefix ex: <https://example.com/kg/> . @prefix schema: <https://schema.org/> . @prefix skos: <http://www.w3.org/2004/02/skos/core#> . # Global facts { ex:osl-lighting a ex:PaintingTechnique ; rdfs:label "Object Source Lighting"@en ; rdfs:label "Efecto de luz OSL"@es ; rdfs:label "Illuminazione OSL"@it . ex:rebel-commandos-painting-guide a schema:HowTo ; schema:about ex:rebel-commandos-legion , ex:osl- lighting ; schema:inLanguage "en" . } # Spain ex:es-ES { ex:osl-lighting ex:preferredBrand "Vallejo" ; schema:areaServed "ES" . } # Mexico ex:es-MX { ex:osl-lighting ex:preferredBrand "Citadel" ; schema:areaServed "MX" . } Core Idea • Techniques & materials = shared entities with localised labels. • Market-specific facts → name graphs (TRiG format). • Connect painting guides to game units via schema:about Takeaway Same technique, localised practice: model global entity + per-markets facts in named graphs.
33. Localisation Relation Use Example owl:sameAs Exact identity (external KGraph) Cassian Andor Wikidata Q27855776 ex:representsXCharacter Game → Lore bridge Rebel Commandos unit → Cassian Andor schema:about Content → Entity Painting Guide → Rebel Commandos + OSL skos:closeMatch Conceptual proximity Legion unit Lore entity Named Graphs Regional facts Spain = Vallejo, USA = Citadel
34. Brand Ontology (2) Defining the relationships (edges) between entities (nodes) and merging the Knowledge Graphs.
35. Now that we have the Knowledge Graphs, we must merge them in only one. Why? Because only so we can ask complex questions that spans all them at once, and so better inform taxonomies, architecture, content opportunities and internal linking. Star Wars: Legion KG Star Wars KG Mini Painting KG
36. Brand KG • Link Graph Star Wars: Legion KG • Shopping Graph • Link Graph Star Wars KG Mini Painting KG
37. Step 1 - Entity Matching & Alignment Find anchor entities (i.e., Luke Skywalker) Use matching techniques Exact matching (i.e., Luke Skywalker) Fuzzy matching (i.e., Darth Vader vs Vader) Property-based matching (i.e., Anakin Skywalker & Legion 501st > Galactic Republic Establish skos:closeMatch, skos:exactMatch et al types of links. Legion:Luke_Skywalker_Unit skos:closeMatch StarWars:Luke_Skywalker_character
38. Tech: entity alignment @prefix ex: <https://example.com/kg/> . @prefix owl: <http://www.w3.org/2002/07/owl#> . @prefix skos: <http://www.w3.org/2004/02/skos/core#> . # Game entity ex:anakin-legion-unit a ex:LegionUnit ; ex:representsCharacter ex:anakin-skywalker-lore . # Lore entity ex:anakin-skywalker-lore a ex:StarWarsCharacter ; owl:sameAs <https://www.wikidata.org/entity/Q19076> ; skos:closeMatch ex:anakin-legion-unit . Core Idea • Match entities that describes the same real-world thing across graphs. • Use equivalence only for true identity (owl:sameAs). • Use contextual links (e.g., skos:closeMatch) for related but non-identical nodes Takeaway Use owl:sameAs only for true identity. Bridge game lore with contextual predicates.
39. Step 2 – Relationship Discovery & Inference From Star Wars: Legion KG Anakin Skywalker isA Commander_Unit and belongsToFaction Galactic Republic From Star Wars KG Anakin Skywalker isA Jedi and participatedIn Clone Wars From Mini Painting KG Anakin Skywalker canBePaintedWith acrylic paintings and canBeBasedwith Desert texture Example of inference The Legion:Commander_Unit named “Anakin Skywalker” represents a Jedi from the lore. This unit (Legion:Anakin_Skywalker_Unit) has a corresponding miniature (MiniPainting:Anakin_Skywalker_Model) that is often painted using Acrylic Paintings for its Jedi uniform. This unit is thematically appropriate for game scenarios set during the Clone Wars
40. Tech: Relationship discovery # From Star Wars KG ex:anakin-skywalker-lore ex:participatedIn ex:clone-wars . # From Legion KG ex:anakin-legion-unit ex:belongsToFaction ex:galactic-republic . # From Mini-Painting KG ex:anakin-model ex:canBePaintedWith ex:acrylic- paints . # Inferred relationship ex:anakin-legion-unit ex:representsCharacter ex:anakin-skywalker-lore ; ex:contextualEra ex:clone-wars ; ex:preferredMedium ex:acrylic-paints . Core Idea • Infer new knowledge by combining statements from connected graphs. • Used shared entities to create cross-domain insights. • Record inferred edges explicitly in the merged graph. Takeaway Use owl:sameAs only for true identity. Bridge game lore with contextual predicates.
41. Step 3 – Schema Mapping and Merging Identify equivalent properties Create a unified graph Copy all nodes and relationships for the Star Wars: Legion KG Integrate the matched entities and their unique properties from the other two KGraphs Create the new, inferred relationships discovered in Step 2
42. Tech: Schema mapping @prefix ex: <https://example.com/kg/> . # Unified Graph { ex:anakin-skywalker a ex:CompositeEntity ; ex:hasRole ex:commander-unit ; ex:hasAffiliation ex:galactic-republic ; ex:appearsIn ex:clone-wars ; ex:paintingMethod ex:acrylic-paints ; owl:sameAs <https://www.wikidata.org/entity/Q19076> . } # Provenance Graphs ex:legionKG { ex:anakin-skywalker ex:source "Legion KG" . } ex:loreKG { ex:anakin-skywalker ex:source "Star Wars Lore KG" . } ex:paintingKG { ex:anakin-skywalker ex:source "Mini Painting KG" . } Core Idea • Merge matched entities and inferred relations into one consistent schema. • Copy unique properties, preserve provenance with named graphs. • Result = a single model panning all domains and locales that can be queried. Takeaway The merged graph unifies all perspectives. Now we have one semantic backbone to power taxonomy, content hubs & AI retrieval
43. Tech: Schema mapping across locale @prefix ex: <https://example.com/kg/> . @prefix schema: <https://schema.org/> . # Global { ex:rebel-commandos-legion a ex:LegionUnit ; rdfs:label "Rebel Commandos"@en ; schema:areaServed "Global" . } # Spain ex:es-ES { ex:rebel-commandos-legion rdfs:label "Rebel Pathfinders"@es-ES ; schema:offers ex:scarif-basing-kit-es ; schema:areaServed "ES" . } # Mexico ex:es-MX { ex:rebel-commandos-legion rdfs:label "Comandos Rebeldes"@es-MX ; schema:offers ex:scarif-basing-kit-mx ; schema:areaServed "MX" . } Core Idea • Keep one global IRI per entity → local graphs hold market-specific facts. • Language variants → rdfs:label@es-ES, rdfs:label@es-MX, rdfs:label@it-I. • Named graphs (TRiG) store localized assertions (schema:areaServed, regional offers). Takeaway One IRI across markets; localized graphs carry regional meaning without fragmenting identity.
44. Thanks to the new merged graph, now we can ask complex questions that span all the ontology domains at once. Example: Show me all the “Rebel Alliance” units from the “Age of New Republic” era and the recommended ”contrast paints” for their miniatures. Step 3 – Schema Mapping and Merging
45. Remember Ground the ontology on real-world knowledge aka involve domain experts like product managers and competitive painters. Plan for recurring maintenance because new techniques emerges, new products are released, and Lucasfilm produces new movies, series, videogames et all.
46. Query Expansion & Full Intent Coverage Go beyond keywords: map entities’ semantic intent.
47. Let’s be honest keyword research sucks
48. PAA are expanded queries. Practically a prototype of query fan-out Use this instead: PAA
49. Legión 501 Star Wars: Las Guerras Clon ¿Cuántos clones tiene la Legión 501? ¿Quién fue el clon de mayor rango en el 501? ¿Cuál es el ejército más grande de Star Wars? ¿Quiénes eran los 15 clones que desobedecieron la Orden 66? ¿Quién fue el último clon de Star Wars? ¿Qué clon dejó embarazada a una Jedi? ¿Qué pasó con la Legión 501? ¿Qué rango tenía Ahsoka en la Legión 501? ¿En qué se especializaba la Legión 501? ¿Qué pasó con la Legión 501 después de Endor? ¿Cuántos clones había en cada legión? ¿Qué es la 501? ¿Qué legión comandaba Mace Windu? ¿La Legión 501 siempre fue clon? ¿Cuántos clones tenía la Legión 501? ¿Quién fue el clon original de Star Wars? 501st legion star wars the clone wars Who was in the 501st clone squad? Are Ahsoka's clones 501st? Is Cody higher rank than Rex? How many 501st clones are there? What is the 501st Legion age limit? What is rule 37 in Star Wars? Who is clone trooper 0000? Did the 501st become stormtroopers? How many clones are in a legion? Who led the 501st before Rex? What legion did Mace Windu lead? Who is the 501st clone in Kenobi? What happened to Commander Cody? Does the 501st Legion appear in shows? Who led the 501st Legion of clones? Was the 501st the best clone legion?
50. Use this instead: Topic filters
51. Use this instead: PASF
52. Use this instead: Images Search Tags
53. Use this instead: AI Mode/Overview query fan-out
54. Use this instead: Other LLMS Query Fan-Outs and follow-ups
55. But also use this: answers by ChatGPT, Gemini, AIO/Mode...
56. Why do I also use the synthetic answers of LLMS? Identify the "Core Intent" of the Topic The synthetic answer is the AI's best attempt at fulfilling the user's true intent, distilled from all the top-ranking sources sourced through the query fan-out. Find Semantic and Content Gaps The AI answer synthesizes information from multiple sources. It's a cheat sheet for what a "complete" answer should look like. Benchmark for AI Search SEO
57. But also use this: SERPs and Web Guides SERP Video block vblock result 1 vblock result 2 ... AIO source 1 source 2 ... Discussions & Forums thread 1 thread 2 ... Organic search snippet 1 Organic search snippet 2 ... Web Guide Organic search snippet 1 Organic search snippet 2 ... Web Guide intro Web Guide section 1 Organic snippet 1 Organic snippet 2 ... Web Guide section 2 Organic snippet 1 Organic snippet 2 ... Web Guide section 3 Organic snippet 1 Organic snippet 2 ...
58. The Importance of Google Web Guide Web Guide EN US Introduction Comprehensive Guides for Battle-Ready Star Wars: Legion Figures. Speed Painting Tutorials. Painting Specific Star Wars: Legion Figures Battle-Ready. Community Tips for Efficient Battle-Ready Painting. Foundational Miniature Painting Skills. General Video Playlist for Star Wars: Legion Painting. Community Advice for Beginner Painters. Understanding “Battle-Ready” Miniature Standards Web Guide ES Introducción Guías oficiales y recursos esenciales. Tutoriales de pintura rápida (battle-ready). Pintura de unidades especificas para Star Wars: Legion. Pintura rápida de droides de combate. Pintura de armadura blanca y Clone Troopers. Consejos y materiales para principiantes. Guías de pintura para Rebeldes. Guías y listas de reproducción en YouTube. Web Guide IT Introduzione Guide ufficiali e panoramiche di pittura. Video tutorial generali per Star Wars: Legion. Pittura battle-ready per Truppe d’assalto e Cloni. Pittura veloce e facile per Droidi di battaglia. Tutorial per miniature specifiche dell’Impero. Tutorial per miniature specifiche dei Ribelli. Tutorial di pittura per veicoli. Guida per iniziare a dipingere.
59. Embeddings & Cosine Similarity for Gap Analysis What to use SERPs, Google Web Guide, and Synthetic Answers. What to do Embedding & Cosine Similarity analysis. Rank topics and subtopics in terms of relevance (on a local level too). Individuate weaknesses in you KGraphs & existing content/architecture. Integrate KGraphs and prioritise improvements for you content/architecture.
60. Taxonomy & Navigation Setting taxonomy as the AI Search internal map and maximize topical authority.
61. Stop basing the taxonomy only over the standard manufacturing catalogue!
62. Combine the created KGraph with insights from keyword research, SERPs and audience analyses, and create alternative meaningful ways to navigate through the product catalogue Kashyyyk Scarif Hoth Endor Jakku Phase 2 Clone Troopers Jin Herso Blizzard Force Han Solo Iden Versio Super Tactical Droid Cassian Handor General Veers Leia Organa Imperial Stormtroopers Wookie Warriors Rebel Commandos Snowtroopers Ewok Warriors Moff Gideon Yoda Director Orson Krennic AT-ST Walkers Rebel Veterans Leia Organa NR-N99 Persuader Class Tank Droid Imperial Shoretroopers Echo Base Defenders Darth Vader Rebel Trooper B1 Battle Droids … Han Solo Imperial Stormtroopers Imperial Specialists … T-47 Airspeeder AT-ST Walkers Rebel Specialists … … … Battle Navigation
63. • Hierarchies in EN / ES / IT must mirror ontology relations. • Add facet aliases → skos:altLabel. • Example: Scarif Battle Collection = Colección Batalla de Scarif@es-MX. Taxonomy and Multilingual / Multi-country Map
64. Taxonomy and Multilingual / Multi-country Map Products Choose your faction Battle or Skirmish? Units’ type How to play How to paint Prodotti Scegli la fazione Ricrea le battaglie Unità Come giocare Come dipingere Navigation should align with local interests
65. • Align taxonomy with ontology. Ontology defines conceptual relationship. Taxonomy structures them for navigation. Remember
66. • Align taxonomy with ontology. Ontology defines conceptual relationship. Taxonomy structures them for navigation. • Move beyond pure hierarchy: don’t be scared by tags and facets Remember
67. • Align taxonomy with ontology. Ontology defines conceptual relationship. Taxonomy structures them for navigation. • Move beyond pure hierarchy: don’t be scared by tags and facets • Optimize taxonomy for classic and AI Search Remember
68. • Align taxonomy with ontology. Ontology defines conceptual relationship. Taxonomy structures them for navigation. • Move beyond pure hierarchy: don’t be scared by tags and facets • Optimize taxonomy for classic and AI Search • Design for localised UX: look for an intuitive and multi-path navigation based on locale behaviour. Remember
69. Taxonomy is a map aka a navigational schema that reinforces topical depth, clarifies domain expertise, create pages that act as semantic getaways, and anticipate the query fan-out. Outcomes
70. Content Hubs Architecture Mapping entities for topical authority.
71. Star Wars: Legion KG Star Wars KG Mini Painting KG Our Knowledge Graph It is our master schema or “semantic backbone”. It is where all the relationships between the entities we target exist.
72. It provides us the user language, popular questions, and current points of interest. In other words, they mirrors how our entities are used in the real world. Our query expansion research
73. Star Wars: Legion KG Star Wars KG Mini Painting KG Our goal is to map the unstructured data of the query expansion research with the structured and contextual framework of the KGraph.
74. NER for entity extraction: Sabine Wren, Cassian Andor, Proton charge, Slapchop... Normalization of intents/questions: “how to (assemble, paint...)” ”best paint scheme for...” “rules clarification for...” ”comparison with...” Step 1: Search insights ingesting and normalization
75. Star Wars: Legion KG Star Wars KG Mini Painting KG Step 2.1: Entity Mapping • We see “Cassian Andor” in a PAA. We find the node in the KGraph. • The KGraph also has the relationship Cassian Andor > work with > Rebel Commandos. • Insight: search data indicates that this fact is a priority in terms of interests.
76. Step 2.2: Intent Mapping • Fan-out > How to paint Star Wars: Legion Rebel Commandos. • In the KGraph the RebelCommandos node is connected to the MiniPainting subgraph. We can traverse this path: Rebel Commandos is a Miniature that requires the painting technique Layering • Search data mentions “Citadel Contrast” and “Slapchop”. These too are nodes in our KGraph under PaintingMethod • Insight: now we know that we should create a content for specifically address, compare, or provide tutorials for these popular methods, which are linked entities to the central product.
77. Step 3: Use KGraph Traversal to define core topic clusters Gameplay & Strategies Example: Rebel Commandos has_keyword=:Scout,:Sharpsho oter,:Infiltrate → Create content about the game’s core mechanics. Example: Rebel Commandos Is_Related_To=:CassianAndor → Create content on hero pairing and list building. Painting & Hobby Example: Rebel Commandos is_painted_with=:CitadelPaint,: Vallejo,:ArmyPainter → Create content comparing brands for painting this unit. Example: Rebel Commandos Requires_Techique=:EdgeHigh Lighting,:Weathering,:Camoufla gePattern → Create guides on thematic bases. Lore & Background Example: Rebel Commandos is_part_of=:RebelAlliance → Create content about the Rebel Alliance’s special forces doctrine. Example: Rebel Commandos Appears_In=:TheReturnOfJedi → Create an informational content about the role of the Battle of Endor, where the commandos intervened.
78. Step 4: Prioritizing and Refining Cluster with Search Data Example: In our KGraph, “Camouflage Pattern” is an important node related to the node “Rebel Commandos”. Search data also recurringly presents it, strongly hinting us that thematic camouflage patterns are one of the high priority topics for a “Rebel Commandos” cluster.
79. Step 5: Designing the Content Hub Architecture Pillar Page Star Wars: Legion Rebel Commandos Ultimate Guide Gameplay cluster Rebel Commandos Unit Guide: Rules & Keywords Explained Best Rebel Commando Loadouts: Snipers vs. Saboteurs How to Build a Competitive list with Cassian Andor & Commandos Lore cluster The Heroes of Scarif: The Lore Behind the Rebel Commandos Rebel Alliance Special Forces in Star Wars Canon and Legends Painting cluster How to Paint Rebel Commandos: A Step-by-Step Guide 3 Easy Camouflage Schemes for Rebel Commandos (Endor, Jakku, Scarif) Basing Your Commandos: Creating a Realistic Forest Floor
80. Why “This Is the Way”? Because: • Contextual interlinking comes natively. • Content prioritization is justified by search data and confirmed by KGraph. • Content completeness is a fact. • Content strategy is sustainable and scalable.
81. But it is not enough anymore
82. Advanced Clustering Clustering for intent, format and sentiment/buyer persona.
83. Clustering for intent
84. Clustering for intent – Improving the Knowledge Graph
85. Clustering for intent – Improving the Knowledge Graph SKUs (Shopping Graph) SWL98 (Din Djarin & Grogu) SWL99 (IG-Series Assassin Droids) SWQ09 (Customizable Imperial Officer & Agent) SWQ103 (Commander Darth Vader and General Veers) SWQ138 (Outer Rim Outlaws) SWQ16 (Rebel Commandos) URLs (Internal link graph) Din Djarin & Grogu IG-Series Assassin Droids Customizable Imperial Officer & Agent Commander Darth Vader and General Veers Outer Rim Outlaws Rebel Commandos
86. Clustering for intent I Want to Know (theoretical) Lore, Rules, Unit Identity Who are the Rebel Commandos in the Star Wars Canon? Rebel Commandos vs. Rebel Pathfinders: what are the differences? Unit analysis: Rebel Commandos in Star Wars: Legion (abilities, strengths, weaknesses). I want to Know (practical) Hobby, Modeling, Strategies Which other Rebel units synergize best with Commandos? How many Rebel Commandos can you field? Squad sizes and options explained. Which other Rebel units synergize best with Commandos? I Want to Go Navigational, PLPs, Collection Hubs Rebel Alliance unit expansions PLP (with Rebel Commandos listed). Scenario Hub: Scarif Battle Collection (with Rebel Commandos listed). Star Wars Era Hub: Reign of Empire (with Rebel Commandos listed).
87. Clustering for intent I Want to Do Hobbyist action, Painting, Gameplay Tactics How to paint Rebel Commandos Battle Ready? How to paint Rebel Commandos for Scarif scenario? How to paint a diorama with Rebel Commandos? I Want to Buy (but I need help) Decision Support, FAQs Are Rebel Commandos worth in Legion 2025 meta (Faq) Rebel Commandos vs. Rebel Pathfinders: which should I buy first? What expansions include the Commandos unit cards and upgrades? I Want to Buy (and I know what) Transaction, PDPs Rebel Commandos PDP with FAQ section. Bundle PDP collection: Rebel Commandos + Cassian Andor & K-2SO + Jyn Erso Pro-painted Rebel Commandos squad for collectors (exclusive PDP)
88. Why this content hub works? Full search/customer journey covered. Lore curiosity → Tactical evaluation → Navigational hubs → Hobby “doing” → Buyer support → Final purchase. The Messy Middle is covered. Triggers → Exploration & Evaluation loop → Experience on site AI Search readiness. Content is structured around entity clarity, practical vs. theoretical queries, “chunk” elements (i.e., FAQs in PDPs).
89. Clustering for format To every micro-moment corresponds a set of SERP Features
90. Clustering for format Example: I want to do → How to Paint Rebel Commandos? Videos block Images block Search snippet with video rich result Discussions & Forum block
91. Clustering for format
92. Clustering for buyer persona (and sentiment) https://blog.google/products/search/ai-mode-agentic-personalized/
93. Clustering for buyer persona (and sentiment) Effective content begins with deep persona understanding. New Players (painting is less important) Seek: Quick wins, Affordability, Basic Theory. Prefer: Tables, FAQs, tutorials. Lean to: Clarity, Reassurance, Confidence. Painters-first (playing can even be optional) Seek: Technique depth, Product Reviews, Insights. Prefer: Video Tutorials, Charts, Long-form Content. Lean to: Immersion, Creativity, Inspiration.
94. Clustering for buyer persona (and sentiment) Remember: search queries always imply an emotional context, especially in conversational search. Content should respond accordingly. Example: Query: “How to avoid paint clumping?” Real question: “Why are my paints clumping?” Implied sentiment: Frustration. Content tone of voice: Reassuring and clear in troubleshooting.
95. Clustering for buyer persona (and sentiment)
96. Content Creation A few actionable concepts and recommendations.
97. Format for AI, Write for Humans Apply the inverted Pyramid at Both Macro and Micro Levels Aka not only in the overall page but also at a section level: 1. Core Answer/Takeaway. 2. Supporting detail, nuance and optional depth.
98. Format for AI, Write for Humans Enhance Chunking with Clear Headings and Structured Formats Aka structure your content into modular, self- contained sections, each with descriptive subheadings 1. H2/H3 for chunks delineation. 2. Bullet point and numbered lists for clarity. 3. Tables for side-by-side comparisons It is not “SEO on-page 101” and ranking factors: it’s Semantics <h1>“How to Achieve Smooth Blends with Oil Paints</h1> Intro with context and quick answer. <h2>Step-by-Step</h2> Short intro Image with alt tag plus caption <h3>Step 1: xxx</h3> Short description plus one pro tip Image with alt tag plus caption <h3>Step X: xxx</h3> Short description plus one pro tip <h2>An actionable table for your painting work</h2> Short intro Table + Link to its PDF version to print it. <h2>Common questions and our expert answers to them</h2> Short intro <h3>FAQ 1</h3> Answer to FAQ 1
99. Use the right Schema Type to Define Content Meaning Schema may or may not be used... ... but I do not care. Schema facilitates parsing, and the better the parsing the more efficient is the indexing. The more efficient is the indexing, the wider is the query set our web page can be visible for. The largest is the query-set, the higher is the probability it will be ranking in the query fan- outs documents retrieval (if everything else is in place). First: Schema is not synonymous of Rich Results. Second: Always use the @id fields Third: Learn that something like WebPage exists. Fourth: Use @graph to combine different Schema. Fifth: Use the correct types. Schema helps us build a consistent semantic web across our site, and can help resolving entities, connect context across pages, strengthen the perceived topical authority.
100. E-E-A-T (yep) Treat E-E-A-T as a Semiotic Framework E-E-A-T is a conceptual model for signalling credibility, experience, expertise and contextual authority. We must present these semiotics signs. For instance: • Referencing named entities. • Attributing techniques to recognized practitioners. • Showing specificity and precision in how entities are used.
101. Maintain Human Focus: Plain Language and Accessibility Write for humans first Even with AI optimization in mind, write for humans first. Key practices: • Use plain, approachable language. • Favor an active voice and short sentences. • Avoid unnecessary jargon. • Ensure visual clarity (e.g., contrast, alt text, font hierarchy). This increases content accessibility and improves dwell time and engagement signals Google uses for evaluation. Please, avoid this: The foundational paradigm of Non-Metallic Metal (NNM) is the phenomenological deconstruction of light's interaction with a non-dielectric surface. Its execution requires a rigorous application of chiaroscuro, creating a stark value dichotomy between the specular highlight — the point of maximum luminous flux — and the form's core shadow. The entire volumetric illusion is then articulated through a meticulous modulation of chromatic temperature, integrating the reflected hues of the celestial and terrestrial environment to achieve ...
102. Share What Only Humans Can: Originality, Context, Insight AI can summarize. AI cannot: • Tell personal stories. • Offer contextual judgment. • Share firsthand experimentation. • Articulate failure, frustration, or inspiration My hypothetical website should highlight its unique artistic methods, team experiences, and behind-the-scenes insights, aka the things AI cannot synthesize from public data. This originality makes content “worth quoting,” not just summarizing (and make people remembering you and returning directly to your website).
103. THINK LIKE A SEMIOTICIAN
104. MODEL LIKE A LINGUIST
105. DESIGN LIKE A STRATEGIST
106. PUBLISH LIKE A BRAND
107. BE VISIBLE EVERYWHERE
108. Gianluca Fiorelli Strategic and International SEO Consultant at ILoveSEO.net How to maximise AI Search visibility through query fan-out insight (and KGraph) AI Innovators