Structured Data Applications

Structured data applications represent standardized markup languages that enable machines to understand and interpret web content with greater precision, serving dual purposes in both traditional search engine optimization (SEO) and the emerging paradigm of Generative Engine Optimization (GEO) ¹³. In traditional SEO contexts, structured data enhances search engine result pages (SERPs) through rich snippets, knowledge panels, and enhanced visibility features, while in GEO contexts, it feeds large language models (LLMs) with contextually rich, semantically organized information that influences AI-generated responses ⁷. This dual-purpose functionality makes structured data applications increasingly vital for organizations seeking visibility across both conventional search engines and AI-powered answer engines that are fundamentally reshaping information discovery.

Overview

Structured data emerged from the semantic web movement, which sought to create a machine-readable web where information could be automatically processed, understood, and connected across different sources ³. The collaborative development of Schema.org by Google, Microsoft, Yahoo, and Yandex established a universal vocabulary with over 800 types and 1,400 properties covering entities from products and events to medical conditions and creative works ¹³. Initially, structured data primarily addressed the challenge of helping traditional search engines parse content meaning, relationships, and attributes more accurately than through content analysis alone, enabling enhanced SERP features like recipe cards, product listings with pricing, and event information ².

The fundamental challenge that structured data addresses is the semantic gap between human-readable content and machine understanding ³. While humans easily comprehend context, relationships, and entity attributes from natural language, machines require explicit labeling and organization according to universally recognized ontologies. Traditional search engines used structured data as a communication protocol to improve result presentation, but the advent of generative AI engines like ChatGPT, Google's Bard, and Bing's AI-powered search has transformed structured data's role from a ranking signal to a knowledge source for generative systems ⁷.

The practice has evolved significantly as AI systems increasingly incorporate structured data during training phases and retrieval-augmented generation (RAG) processes, where explicitly marked-up data influences the factual accuracy, attribution, and contextual relevance of AI-generated responses ⁷. This evolution represents a fundamental shift in how structured data functions within the information ecosystem, expanding from enhancing traditional search visibility to directly feeding the knowledge bases that power conversational AI and generative search experiences.

Key Concepts

Schema.org Vocabulary

Schema.org provides the most widely adopted taxonomy for structured data, offering standardized types and properties that both traditional search engines and AI systems use to categorize and understand content ¹³. This collaborative vocabulary establishes a common language for describing entities, their attributes, and relationships in machine-readable formats.

Example: A local bakery implementing Schema.org markup would use the LocalBusiness schema type with properties including name ("Artisan Bread Co."), address (with nested PostalAddress schema containing street, city, state, and postal code), telephone, openingHours (specifying "Mo-Fr 07:00-19:00, Sa 08:00-17:00"), priceRange ("$$"), and aggregateRating (with ratingValue of 4.7 and reviewCount of 143). This comprehensive markup enables Google to display rich business information in local search results while also providing AI assistants with structured data to accurately answer queries like "What are the hours for Artisan Bread Co.?"

JSON-LD Implementation Format

JSON-LD (JavaScript Object Notation for Linked Data) has emerged as Google's recommended format for structured data implementation due to its separation from HTML markup, making it easier to implement and maintain ¹⁴. Unlike Microdata or RDFa, which embed structured data directly within HTML tags, JSON-LD exists as a standalone script that can be inserted in page headers or managed through tag management systems.

Example: An online electronics retailer implementing product structured data would insert a JSON-LD script in the <head> section of their product page for a wireless headphone model. The script would include the Product schema with nested Offer schema containing real-time pricing ($149.99), availability ("InStock"), brand ("AudioTech"), aggregateRating with average rating (4.5 stars from 287 reviews), and detailed product specifications. This implementation allows the development team to update pricing and availability through their content management system without modifying HTML structure, while ensuring both traditional search engines and AI systems can access complete product information.

Entity Relationships and Knowledge Graphs

Entity relationships establish connections between different structured data elements, creating knowledge graphs that map how entities relate to one another within semantic networks ³. These relationships enable both traditional search engines and AI systems to understand context, attribution, and the broader information ecosystem surrounding specific content.

Example: A medical research institution publishing a study on diabetes treatment would implement interconnected structured data: a MedicalScholarlyArticle schema for the research paper itself, connected to Person schemas for each of the five co-authors (including their medical credentials, institutional affiliations, and ORCID identifiers), an Organization schema for the research institution (with accreditation details and medical specializations), and MedicalCondition schemas for the specific diabetes types discussed. These relationships create a semantic network that helps Google's knowledge graph understand the study's authoritative context while enabling AI systems to accurately attribute findings and assess source credibility when generating responses about diabetes treatment options.

Rich Results and SERP Features

Rich results represent enhanced search engine result presentations that display additional information beyond standard title, URL, and meta description, powered by properly implemented structured data ². These features include recipe cards, product listings with pricing and reviews, event information with dates and locations, FAQ accordions, and how-to guides with step-by-step instructions.

Example: A cooking website implementing comprehensive Recipe schema for their "Classic Chocolate Chip Cookies" article includes properties for recipeIngredient (listing each ingredient with precise measurements), recipeInstructions (structured as HowToStep items with detailed directions), totalTime (45 minutes), recipeYield (24 cookies), nutrition (calories, fat, sugar per serving), aggregateRating (4.8 stars from 1,247 reviews), and video (linking to a preparation video with structured VideoObject markup). This implementation enables Google to display a rich recipe card in search results showing the recipe image, rating, cooking time, and calorie count, significantly increasing click-through rates compared to standard organic listings while also providing AI cooking assistants with structured information to accurately answer questions about ingredient quantities or preparation steps.

Retrieval-Augmented Generation (RAG) Integration

Retrieval-Augmented Generation represents a process where AI systems retrieve relevant information from external sources to ground their responses in factual, up-to-date content rather than relying solely on training data ⁷. Structured data plays an increasingly important role in RAG systems by providing explicitly marked-up information that AI engines can more easily extract, verify, and attribute when generating responses.

Example: A financial services company publishing market analysis articles implements comprehensive Article and FinancialProduct schemas with detailed author credentials (Person schema including professional certifications, years of experience, and institutional affiliations), publication dates, citation sources for statistical claims, and explicit markup of financial data points. When a user asks an AI assistant about recent trends in mortgage rates, the RAG system retrieves the company's article, leveraging the structured data to extract specific rate information, properly attribute the analysis to the credentialed author, and include the publication date in the AI-generated response, increasing both accuracy and source transparency.

E-E-A-T Signals Through Structured Markup

Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T) represent quality signals that both traditional search engines and AI systems use to assess content credibility ³. Structured data enables explicit markup of these signals through author credentials, organizational affiliations, publication metadata, and citation sources.

Example: A health information website publishing articles about prescription medications implements MedicalWebPage schema with nested Person schemas for physician authors that include medicalSpecialty (e.g., "Cardiology"), credential ("MD, FACC"), alumniOf (linking to medical school with EducationalOrganization schema), and worksFor (connecting to hospital affiliation with MedicalOrganization schema including accreditation details). The Organization schema for the publishing entity includes ownershipFundingInfo for transparency and publishingPrinciples linking to editorial policies. This comprehensive E-E-A-T markup helps Google assess content quality for health-related queries while enabling AI systems to evaluate source reliability when generating medical information, potentially increasing citation frequency for authoritative content.

Dynamic Schema Generation

Dynamic schema generation represents a technical methodology where structured data is programmatically created based on database content, CMS fields, or API responses, enabling scalable implementation across thousands of pages ⁴. This approach ensures structured data remains synchronized with content updates while maintaining consistency across large websites.

Example: An e-commerce platform with 50,000 product listings implements server-side dynamic schema generation using PHP templates that query their product database. When a product page loads, the system automatically generates Product schema with current price pulled from the pricing database, availability status from inventory systems, aggregateRating calculated from recent customer reviews, brand from product attributes, and offers with real-time shipping information. When a product goes out of stock, the structured data automatically updates to reflect "OutOfStock" status without manual intervention, ensuring both traditional search engines and AI shopping assistants receive accurate, current information while eliminating the maintenance burden of manually updating structured data across tens of thousands of pages.

Applications in Digital Marketing and Content Strategy

E-commerce Product Optimization

Major e-commerce platforms implement extensive Product, Offer, and AggregateRating schemas that power rich shopping results in traditional search while informing AI shopping assistants about product specifications, pricing, and availability ²⁴. Online retailers use structured data to display product ratings, price ranges, and availability status directly in search results, significantly increasing click-through rates for commercial queries. For GEO applications, the same structured data enables AI assistants to provide accurate product recommendations, compare specifications across multiple items, and direct users to purchase options based on current pricing and inventory data.

News and Editorial Content Distribution

News organizations employ comprehensive Article and NewsArticle schemas with detailed author, publication, and citation markup that enhances both traditional news carousels and AI-generated news summaries ⁵. Publishers like The New York Times and BBC implement structured data including headline, datePublished, dateModified, author (with detailed Person schemas), publisher (with Organization schema including logo and social profiles), and articleBody with explicit markup of key facts and claims. This implementation enables appearance in Google's Top Stories carousel and news-specific SERP features while providing AI systems with properly attributed, timestamped information for generating news summaries and answering current events queries.

Local Business Visibility

Local businesses leverage LocalBusiness schema types with comprehensive properties including operating hours, service areas, accepted payment methods, and customer reviews to enhance local search visibility ¹⁶. A dental practice implementing detailed Dentist schema (a specialized type of LocalBusiness and MedicalBusiness) includes areaServed (specifying service radius), paymentAccepted (listing insurance providers), openingHoursSpecification (with detailed hours including lunch breaks), and hasOfferCatalog (listing specific services like teeth whitening, orthodontics, and emergency care). This structured data powers Google Business Profile integration, local pack appearances, and voice search results while enabling AI assistants to accurately answer location-based queries like "Which dentists near me accept Delta Dental insurance and offer Saturday appointments?"

Recipe and How-To Content

Recipe websites and instructional content publishers utilize detailed Recipe and HowTo schemas that enable both traditional recipe rich results and accurate information delivery through AI cooking assistants ². Sites like AllRecipes implement comprehensive recipe markup including not just basic ingredients and instructions but also nutrition information (calories, macronutrients), suitableForDiet (marking recipes as vegetarian, gluten-free, etc.), recipeCategory and recipeCuisine for classification, video with structured VideoObject markup for preparation demonstrations, and review schemas for user feedback. This rich structured data creates prominent recipe cards in traditional search results while providing AI systems with complete information to answer specific cooking questions like "How much flour is needed for chocolate chip cookies?" or "What's the baking temperature for this recipe?"

Best Practices

Implement Comprehensive Property Coverage

Rather than implementing minimum viable markup, include as many relevant schema properties as possible to provide richer data sources for both search engines and generative systems ³⁴. Research suggests that more comprehensive structured data correlates with better performance in both traditional rich results and AI citation frequency. The rationale is that additional properties provide contextual depth that helps both traditional algorithms and AI systems better understand content meaning, relationships, and authority signals.

Implementation Example: When marking up a blog article about sustainable gardening practices, instead of implementing only basic Article schema with headline, author, and datePublished, include comprehensive properties: author with full Person schema including credentials and social profiles, publisher with detailed Organization schema, mainEntityOfPage, image with ImageObject schema including dimensions and captions, articleSection for categorization, wordCount, inLanguage, citation linking to referenced research with ScholarlyArticle schemas, about connecting to relevant Thing entities, and mentions for related topics. This comprehensive approach provides maximum information for both traditional search features and AI systems generating content about sustainable gardening.

Ensure Markup-Content Alignment

Structured data must accurately reflect visible page content to maintain compliance with search engine guidelines and avoid penalties ¹⁴. Mismatches between structured data claims and actual page content represent a violation that can trigger manual actions or algorithmic devaluation. The rationale is that misleading markup undermines user trust and search quality, whether in traditional results or AI-generated responses.

Implementation Example: An event venue marking up a concert listing must ensure the Event schema's startDate, location, performer, and offers (ticket pricing) exactly match the information displayed on the page. If the visible page shows the concert starting at 8:00 PM but the structured data indicates 7:00 PM, or if the schema lists ticket prices at $50 when the actual price is $75, this creates a markup-content mismatch. Implement validation processes that programmatically compare structured data values against page content before publication, and establish content management workflows where structured data updates automatically when editors modify visible content, ensuring perpetual alignment.

Prioritize JSON-LD Format

Implement structured data using JSON-LD format in page headers for clean separation from HTML markup, easier maintenance, and better compatibility with both traditional crawlers and AI systems ¹⁴. JSON-LD's standalone nature allows development teams to manage structured data independently from front-end HTML, reducing implementation complexity and enabling centralized updates through tag management systems or server-side templates.

Implementation Example: Rather than embedding Microdata attributes throughout HTML elements (which requires front-end developers to maintain itemscope, itemtype, and itemprop attributes scattered across the page structure), implement a JSON-LD script in the <head> section that can be managed through Google Tag Manager or server-side templates. For a product page, create a template that queries the product database and generates a complete JSON-LD Product schema with all relevant properties, inserted as a single script block. This approach allows the marketing team to update structured data strategy without requiring front-end code changes, enables A/B testing of different schema implementations, and ensures consistent formatting across thousands of product pages.

Establish Author and Organizational Authority

Implement comprehensive Person and Organization schemas with detailed credentials, affiliations, and expertise signals to establish authority valuable for both traditional E-E-A-T assessment and GEO source credibility evaluation ³⁵. AI systems increasingly evaluate source reliability when determining which content to cite in generated responses, making explicit authority markup strategically important for GEO visibility.

Implementation Example: A financial advisory firm publishing investment guidance implements detailed Person schemas for each advisor author including jobTitle ("Certified Financial Planner"), credential ("CFP®, CFA"), alumniOf (linking to university with EducationalOrganization schema), memberOf (professional organizations like the Financial Planning Association), award (industry recognitions), and sameAs (links to LinkedIn profile and professional directory listings). The firm's Organization schema includes foundingDate, numberOfEmployees, areaServed, award (industry certifications), ownershipFundingInfo for transparency, and publishingPrinciples linking to editorial standards. This comprehensive authority markup helps Google assess content quality for YMYL (Your Money Your Life) topics while signaling to AI systems that the content comes from credentialed, verifiable sources worthy of citation.

Implementation Considerations

Tool and Format Selection

Selecting appropriate tools and formats for structured data implementation depends on technical infrastructure, team capabilities, and scale requirements ¹⁴. Organizations must choose between manual implementation, CMS plugins, tag management systems, or programmatic generation based on their specific context. Google's Rich Results Test and Schema Markup Validator provide essential validation capabilities, while Google Search Console's Rich Results report enables ongoing monitoring of implementation quality and error identification ¹².

Example: A small business with a WordPress website might implement structured data through plugins like Yoast SEO or Schema Pro, which provide user-friendly interfaces for adding markup without coding knowledge. A mid-sized publisher with custom CMS infrastructure might use Google Tag Manager to centrally manage JSON-LD scripts across different page types, enabling the marketing team to update schemas without developer involvement. A large e-commerce platform with 100,000+ product pages requires programmatic server-side generation using templates that query product databases, ensuring scalable, maintainable implementation that automatically reflects inventory and pricing changes.

Schema Type Specificity

Choose the most specific schema type appropriate for content rather than generic types, as specificity improves both traditional search understanding and AI system interpretation ³⁵. Schema.org's hierarchical structure includes specialized types that inherit properties from parent types while adding domain-specific attributes. Using NewsArticle instead of generic Article, or MedicalCondition instead of generic Thing, provides additional semantic context valuable for both traditional and generative search.

Example: A healthcare website publishing content about Type 2 diabetes should implement MedicalCondition schema (which inherits from MedicalEntity) rather than generic Article schema. The MedicalCondition type includes specialized properties like associatedAnatomy, possibleTreatment, riskFactor, signOrSymptom, and typicalTest that provide medical context unavailable in generic article markup. This specificity helps Google display medical information in specialized health SERP features while enabling AI health assistants to accurately extract condition-specific information when generating responses about diabetes symptoms, treatments, or risk factors.

Organizational Maturity and Resource Allocation

Structured data implementation success depends on organizational maturity, cross-functional collaboration, and sustained resource commitment ⁴. Organizations must establish workflows integrating structured data into content creation processes, documentation of schema mapping decisions for consistency, and regular audits to identify and correct errors. Successful implementations treat structured data as foundational infrastructure rather than tactical optimization, requiring coordination between SEO, development, content, and product teams.

Example: A media company establishing structured data infrastructure creates a cross-functional working group including SEO specialists, developers, content editors, and product managers. The team documents schema mapping decisions in a central wiki (e.g., "All author bylines map to Person schema with these specific properties"), implements automated validation in the content management system that prevents article publication without required structured data fields, establishes quarterly audits using Google Search Console data to identify error patterns, and assigns a dedicated technical SEO specialist to monitor Schema.org vocabulary updates and search engine guideline changes. This organizational approach ensures sustainable, high-quality implementation rather than one-time tactical deployment.

Multi-Schema Page Strategy

Many pages benefit from implementing multiple schema types that describe different aspects of content, requiring careful planning to avoid conflicts while maximizing semantic richness ²³. A single page might include WebPage schema for the page itself, Article schema for the main content, Person schemas for authors, Organization schema for the publisher, and BreadcrumbList schema for navigation context.

Example: A recipe blog post implements a multi-schema strategy: Recipe schema for the recipe itself (with comprehensive ingredients, instructions, and nutrition data), Article schema for the blog post content surrounding the recipe (with headline, author, publication date), Person schema for the recipe author (with credentials and social profiles), Organization schema for the food blog (with logo and social profiles), ImageObject schemas for recipe photos (with captions and dimensions), VideoObject schema for the preparation video (with duration and upload date), and BreadcrumbList schema for site navigation context. Each schema serves a specific purpose without conflicting, creating a rich semantic representation that benefits both traditional recipe rich results and AI cooking assistant information extraction.

Common Challenges and Solutions

Challenge: Maintaining Accuracy at Scale

Organizations with thousands or millions of pages face significant challenges maintaining structured data accuracy as content, pricing, inventory, and other dynamic information changes ⁴. Manual updates become impossible at scale, leading to markup-content mismatches that violate search engine guidelines and provide inaccurate information to AI systems. Database changes may not propagate to structured data, creating discrepancies between visible content and markup that undermine both traditional SEO performance and GEO citation quality.

Solution:

Implement programmatic structured data generation that automatically queries authoritative data sources and generates markup based on current information ⁴. For e-commerce sites, create server-side templates that query product databases for pricing, inventory status, and specifications each time a page loads, ensuring structured data always reflects current state. Establish automated monitoring systems that regularly crawl site pages, extract both visible content and structured data, and flag discrepancies for review. Use Google Search Console's Rich Results report to identify error patterns across page types, then address root causes in generation templates rather than fixing individual pages. Implement content management system validation that prevents publication when required structured data fields are missing or when markup-content mismatches are detected, creating quality gates that maintain accuracy at scale.

Challenge: Balancing Traditional SEO and GEO Requirements

Structured data strategies optimized for traditional SEO rich results may not fully address GEO requirements, as AI systems prioritize different properties and relationships than traditional search engines ⁷. Traditional SEO focuses on properties that enable specific SERP features (like price and availability for product rich results), while GEO benefits from comprehensive contextual information, attribution signals, and entity relationships that help AI systems assess source credibility and generate accurate responses. Organizations struggle to determine which properties to prioritize and how to measure GEO-specific impact.

Solution:

Adopt a comprehensive property coverage approach that implements both traditional SEO-critical properties and GEO-valuable contextual properties ³. For product pages, include not just basic properties required for shopping rich results (price, availability, rating) but also detailed specifications, usage instructions, warranty information, and manufacturer details that AI shopping assistants can utilize. For articles, implement not just headline and publication date but also detailed author credentials, citation sources for factual claims, and explicit markup of key entities and relationships. Establish dual measurement frameworks tracking both traditional metrics (rich snippet impressions, click-through rates from Google Search Console) and emerging GEO indicators (monitoring whether content appears in AI-generated responses, tracking citation frequency and attribution quality). Prioritize properties that serve both purposes—for example, comprehensive author credentials enhance both traditional E-E-A-T assessment and AI source credibility evaluation.

Challenge: Technical Implementation Complexity

Implementing structured data correctly requires technical expertise that many organizations lack, particularly for complex schemas with nested entities and multiple relationships ¹⁴. Developers unfamiliar with JSON-LD syntax make errors in formatting, property naming, or value types that prevent search engines and AI systems from parsing markup correctly. Content teams struggle to understand which properties are required versus optional, and how to map their content attributes to schema properties. Testing and validation across different schema types and page templates becomes time-consuming and error-prone.

Solution:

Establish structured data implementation frameworks that simplify complexity through templates, documentation, and validation processes ¹⁴. Create JSON-LD templates for each major page type (product pages, articles, local business pages) that developers can customize rather than building from scratch, reducing syntax errors and ensuring consistent structure. Document schema mapping decisions in accessible guides that explain which CMS fields or database attributes map to which schema properties, enabling content teams to understand requirements without deep technical knowledge. Implement automated validation in development workflows using Google's Rich Results Test API, preventing deployment of pages with structured data errors. Utilize CMS plugins or modules that provide user-friendly interfaces for structured data management when appropriate for organizational technical capabilities. Invest in training for both development and content teams, ensuring cross-functional understanding of structured data principles, Schema.org vocabulary, and implementation best practices.

Challenge: Measuring ROI and Impact

Organizations struggle to measure structured data's return on investment and impact, particularly for GEO applications where measurement methodologies remain immature ²⁷. Traditional SEO metrics like rich snippet impressions and click-through rate improvements provide some visibility into traditional search impact, but don't capture the full value of enhanced SERP features or knowledge graph integration. For GEO, no standardized tools exist for tracking whether content appears in AI-generated responses, how frequently it's cited, or whether structured data influences citation quality and attribution accuracy.

Solution:

Establish multi-dimensional measurement frameworks that combine traditional SEO metrics with emerging GEO indicators and proxy measurements ². For traditional SEO impact, track rich snippet impressions and click-through rates through Google Search Console, comparing performance before and after structured data implementation for specific page types. Monitor acquisition of SERP features like knowledge panels, featured snippets, and specialized result types (recipe cards, product listings) that structured data enables. For GEO impact, implement manual monitoring processes where team members regularly query AI systems (ChatGPT, Bing Chat, Google Bard) with relevant queries and track whether organizational content appears in responses, how it's attributed, and whether information accuracy reflects structured data properties. Use brand monitoring tools to track mentions in AI-generated content. Establish proxy metrics like increases in direct traffic or branded search volume that may indicate enhanced visibility through AI channels. Recognize that comprehensive structured data provides foundational infrastructure value beyond immediate measurable impact, positioning organizations for evolving search paradigms even when direct ROI attribution remains challenging.

Challenge: Keeping Current with Evolving Standards

Schema.org vocabularies, search engine guidelines, and AI system requirements evolve continuously, creating challenges for organizations to maintain current, compliant implementations ¹³. New schema types and properties are regularly added, existing schemas are refined or deprecated, and search engine eligibility requirements for specific SERP features change. Organizations implementing structured data based on current best practices find their implementations becoming outdated or non-compliant as standards evolve, requiring ongoing maintenance and updates.

Solution:

Establish structured data governance processes that include regular monitoring of Schema.org updates, search engine guideline changes, and industry research on GEO best practices ¹³. Subscribe to official channels including Schema.org release notes, Google Search Central blog, and Bing Webmaster blog to receive notifications of changes. Conduct quarterly structured data audits that review current implementations against updated standards, identifying opportunities to adopt new schema types or properties that enhance semantic richness. Participate in industry communities and forums where practitioners share insights about emerging GEO requirements and AI system behaviors. Maintain flexible implementation architectures (particularly JSON-LD through tag management systems or server-side templates) that enable rapid updates without requiring extensive front-end code changes. Document schema implementation decisions with rationale and dates, enabling future teams to understand why specific approaches were chosen and when they may need revision. Treat structured data as living infrastructure requiring ongoing investment rather than one-time implementation, allocating resources for continuous improvement and adaptation to evolving standards.

References

1. Google Developers. (2025). Introduction to Structured Data. https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data
2. Google Developers. (2025). Search Gallery: Structured Data Features. https://developers.google.com/search/docs/appearance/structured-data/search-gallery
3. Moz. (2025). Schema Structured Data for SEO. https://moz.com/learn/seo/schema-structured-data
4. Ahrefs. (2025). Structured Data: What It Is and How to Use It for SEO. https://ahrefs.com/blog/structured-data/
5. Google Developers. (2025). Article Structured Data. https://developers.google.com/search/docs/appearance/structured-data/article
6. Bing Webmaster Tools. (2025). Marking Up Your Site with Structured Data. https://www.bing.com/webmasters/help/marking-up-your-site-with-structured-data-3a93e731
7. Aggarwal, S., et al. (2023). GEO: Generative Engine Optimization. arXiv:2308.03188. https://arxiv.org/abs/2308.03188