Pharma GEO: Building Knowledge Graphs That AI Systems Trust

What Is Generative Engine Optimization (GEO) — and Why Does Pharma Need It Now?

Generative Engine Optimization (GEO) is the practice of structuring digital content so that large language models (LLMs) — ChatGPT, Perplexity AI, Google Gemini, Microsoft Copilot — can find, understand, and cite it when generating answers.¹

GEO differs from SEO in one critical way: AI systems do not rank pages, they synthesise answers. Many use retrieval-augmented generation (RAG) — retrieving facts from an index, then constructing a response — while others rely on knowledge encoded during training, and many production systems combine both approaches. In all cases, the principle holds: content that is machine-readable, entity-linked, and consistently identified is more likely to surface accurately than content that exists only as unstructured text.²

A page that ranks #1 on Google may not appear in an AI answer at all if its content is not machine-readable and its entities are not unambiguously identified.

For pharma, the stakes are clinical as well as commercial. When a patient asks an AI about a drug’s efficacy, the answer depends on what the AI can access, identify, and trust. Science that is invisible to AI — or worse, misattributed — carries real patient-safety risk.³

The enabling infrastructure is the knowledge graph. And most pharma organisations have not yet built it.

What Is a Knowledge Graph?

A knowledge graph represents knowledge as nodes (entities: drugs, diseases, researchers, trials, publications) and edges(relationships: treats, authored, investigated).⁴ Its power lies in connecting heterogeneous data — clinical literature, trial registries, regulatory filings, bibliographic databases — into a coherent, machine-readable whole.⁵

Generative AI systems are shaped by biomedical knowledge graphs in two distinct ways: as training data (Wikidata is a major corpus source for most LLMs), and — in RAG-based pipelines — as indexed retrieval targets. Consumer AI chatbots do not typically query Wikidata or PubMed KG 2.0 dynamically at inference time; what matters is whether your entities are represented in data that these systems were trained on or can retrieve via search:

PheKnowLator — an open-source toolkit for automating FAIR-compliant knowledge graph construction from multi-scale biomedical data. Note: unlike Wikidata, PheKnowLator is a construction framework, not a deployed knowledge base queried by consumer AI systems at inference time; its relevance here is as a methodology reference for organisations building internal graphs.⁸

Wikidata — a continuously updated, openly licensed knowledge graph covering biological pathways, compounds, diseases, protein targets, and scholarly authors linked via ORCID identifiers.⁶

PubMed Knowledge Graph 2.0 — connects biomedical papers, patents, and clinical trials through entity extraction and author disambiguation.⁷

The Author Identity Problem: Why ORCID Is GEO Infrastructure

What is the author disambiguation problem?

The scientific literature contains hundreds of thousands of researchers, many sharing identical names. Without persistent identifiers, a machine cannot determine whether “J. Wang” in a 2019 oncology paper is the same person as “J. Wang” in a 2023 immunology article. Metadata incompleteness and inconsistent identifier use across repositories make machine-readable identity resolution unreliable.⁹

What is ORCID, and why is it the minimum standard?

ORCID (Open Researcher and Contributor ID) is a not-for-profit registry — sustained by fees from member institutions — that assigns persistent, unique identifiers to researchers. Each ORCID iD is a 16-digit number expressed as a hyphenated URI (e.g., https://orcid.org/0000-0002-7421-2028); it is this full URI form that should be used in schema markup and database records.¹⁰

An ORCID record links a researcher’s name, institutional affiliation, publication list (via DOI), funding, peer review activities, and employment history into a single machine-readable profile.

Studies confirm that ORCID-linked data enables high-precision author disambiguation across millions of bibliographic records.⁹ Every researcher associated with your pipeline — KOLs, investigators, medical affairs — should have a complete, public ORCID profile.

Register at: https://orcid.org

How does Wikidata extend author identity for AI systems?

Wikidata is a free, openly licensed knowledge base operated by the Wikimedia Foundation. It supports the ORCID iD property (P496), directly linking a researcher’s Wikidata item to their ORCID profile. Once this connection exists, an AI can move from an ORCID identifier to the researcher’s institutional affiliation, country, research domain, and linked publications — all in one machine-traversable path.⁶

A researcher properly represented in Wikidata can be reliably cited by AI. A researcher who exists only as a name string in a PDF cannot.

Wikidata tools for pharma teams:

What is the full author identifier stack?

Beyond ORCID and Wikidata, a complete identity stack includes: Scopus Author ID, Google Scholar profile, ResearcherID / Web of Science, and ISNI. Cross-referencing these identifiers ensures that any system attempting to identify a researcher arrives at a consistent, authoritative description.

Institutional Identity: Organisations, Drugs, and Trials as Knowledge Graph Nodes

Author identity alone is insufficient. For AI to reason accurately about pharma science, every relevant entity must be machine-identifiable.

Organisations should have Wikidata items including official name, country, website, associated researchers, and links to clinical trials via ClinicalTrials.gov NCT numbers.

Drugs and compounds should be cross-referenced across:

• Wikidata — pharmacological properties and indications
• ChEMBL (European Bioinformatics Institute) — bioactive molecule data
• DrugBank — mechanism, interactions, targets
• RxNorm (US National Library of Medicine) — normalised drug naming
• MeSH — NLM controlled vocabulary for biomedical indexing

Clinical trials should be registered (NCT identifiers), results-reported, and linked from publications. PubMed Knowledge Graph 2.0 already connects papers, patents, and trials [7] — your trial needs to appear in that chain.

Schema.org: Making Web Pages Machine-Readable

What is schema.org markup?

Schema.org is a shared vocabulary for structured data on the web, launched in June 2011 by Google, Bing, and Yahoo — with Yandex joining the initiative in November 2011.¹³ Implemented as JSON-LD code embedded in web pages, it translates human-readable content into machine-readable format — enabling search engines and AI systems to categorise, attribute, and extract information without ambiguity.¹⁴

Does schema.org affect AI visibility?

Yes — with an important nuance. In April 2025, Google stated that structured data supports AI-generated search results. In March 2025, Microsoft indicated that schema markup aids content understanding for Copilot. Neither company has published controlled evidence establishing a direct causal link between schema markup and AI citation frequency; structured data is best understood as reducing ambiguity and improving extractability, rather than as a guaranteed citation driver.¹⁵

Which schema types matter for pharma?

Schema.org’s health and medical types (MedicalEntity and subtypes) are designed to complement existing medical vocabularies and ontologies.¹⁶

The Author–Article–Organisation Triple

The highest-impact schema implementation connects three entities — article, author, institution — using sameAs to link to external identifiers. This is how a disconnected web page becomes a node in a knowledge graph.

Validate all markup at: validator.schema.org  and  search.google.com/test/rich-results

The FAIR Principle: The Framework Tying It All Together

Every recommendation in this article is an application of the FAIR data principles — Findable, Accessible, Interoperable, and Reusable — first articulated in Scientific Data in 2016.¹⁷

The Practical Roadmap: Seven Steps to a Pharma Knowledge Graph

Step 1 — Audit. Inventory all authors, drugs, trials, and institutions relevant to your pipeline. For each entity, identify which persistent identifiers exist and which are missing.

Step 2 — Complete ORCID profiles. Facilitate ORCID registration for every researcher in your network. Priority fields: name variants, affiliations, publication list via DOI, funding records. Set to public visibility.

Step 3 — Create and enrich Wikidata items. For researchers meeting notability criteria, create or enrich Wikidata items with ORCID iD (P496), employer (P108), field of work (P101), and linked publications. For drugs, populate ChEMBL, DrugBank, and MeSH identifiers.

Step 4 — Deploy schema.org markup. Implement JSON-LD on all owned web properties. Connect ScholarlyArticle → Person (ORCID sameAs) → Organization (Wikidata sameAs) → Drug (DrugBank/Wikidata sameAs).

Step 5 — Structure abstracts for AI extractability. Every publication announcement should include a plain-language PICO summary (Population, Intervention, Comparator, Outcome). Short, structured summaries that AI can extract verbatim improve citation likelihood.¹⁸

Step 6 — Register findings in the ORKG. For pivotal studies, submit structured contributions to the Open Research Knowledge Graph (orkg.org). This makes findings machine-queryable beyond the PDF layer.¹⁹

Step 7 — Monitor AI visibility. Regularly query major AI systems with questions HCPs or patients would realistically ask. Identify attribution gaps and trace them to missing or inconsistent data. GEO is a continuous programme.

Generative AI systems that lack reliable structured data do not simply fail to cite — they fill the gap with whatever unstructured text they can parse, which may be outdated, misattributed, or inaccurate. A complete knowledge graph — correct identifiers, accurate schema, cross-referenced entities — actively reduces the risk of AI-generated misinformation about your products.

Why This Is a Compliance Asset, Not Just a Marketing Play

This same infrastructure supports pharmacovigilance data quality, FAIR compliance required by research funders, and interoperability with EMA and FDA electronic submissions. The semantic foundation for GEO is also the foundation for responsible data stewardship.

Regulatory Considerations: What GEO Does Not Override

Knowledge graph infrastructure makes pharma science more machine-readable. That increased legibility cuts both ways.

Promotion rules still apply. EMA and FDA regulations governing the digital promotion of medicinal products apply regardless of the technical format in which claims are made. Schema markup on drug pages that foregrounds efficacy data without proportionate safety information may constitute non-compliant promotion, even if the intent is purely informational.

Fair balance in structured data. The Drug schema type and associated MedicalCondition properties make indication claims directly machine-extractable. Medical affairs and regulatory teams should review any schema deployment on branded or indication-specific pages before publication.

Off-label risk. Structured data that links a drug entity to unapproved indications — even via Wikidata items or third-party graph entries your organisation did not author — can amplify off-label associations in AI-generated answers. Monitoring AI outputs for your molecules is not just a GEO visibility exercise; it is a pharmacovigilance and compliance obligation.

Wikidata is a public commons, not a controlled channel. Wikidata entries can be edited by any registered user. Your organisation can contribute accurate data, but it cannot control subsequent edits. Treat Wikidata as a partially-controlled input to AI systems, not a managed asset.

GEO should be a cross-functional programme: medical affairs, regulatory, legal, and digital working from a shared content governance framework before any structured data is deployed at scale.

Key Takeaways

• GEO requires entities, not just content. AI systems reason over connected data. Your science must be represented as identifiable, cross-referenced entities — not just keyword-optimised text.
• ORCID is the entry condition. Without persistent author identifiers, AI cannot attribute findings reliably. Every researcher in your network needs a complete, public ORCID profile.
• Wikidata is the authority layer. Linking ORCID to Wikidata creates a machine-traversable identity graph that AI systems actively consult.
• Schema.org connects your web properties to the graph. The sameAs property is the bridge between a web page and a knowledge graph node.
• FAIR data is AI-legible data. Findable, Accessible, Interoperable, Reusable — these principles are directly operational in the GEO context.
• Accuracy is non-negotiable. All identifiers, Wikidata items, and schema markup must reflect verifiable, factual information. Manipulative or misleading structured data violates platform policies and undermines trust.

References

[1]  Aggarwal, P. et al. (2024). GEO: Generative Engine Optimization. KDD 2024, ACM. DOI: 10.1145/3637528.3671900  https://collaborate.princeton.edu/en/publications/geo-generative-engine-optimization

[2]  Viseven. (2026). GEO for Pharma Industry: How to Stay Compliant and Seen.  https://viseven.com/generative-engine-optimization-for-pharma/

[3]  Indegene. (2025). GEO vs AEO vs LLMO: The New Search Optimization Trinity for Pharma.  https://www.indegene.com/what-we-think/reports/geo-vs-aeo-vs-llmo

[4]  Perdomo-Quinteiro, P. & Belmonte-Hernández, A. (2024). Knowledge graphs for drug repurposing. Briefings in Bioinformatics, 25(6). DOI: 10.1093/bib/bbae461  https://academic.oup.com/bib/article/25/6/bbae461/7774899

[5]  Serra, A. et al. (2025). An update on knowledge graphs in drug discovery. Expert Opinion on Drug Discovery, 20(5), 599-619. DOI: 10.1080/17460441.2025.2490253  https://doi.org/10.1080/17460441.2025.2490253

[6]  Waagmeester, A. et al. (2020). Wikidata as a knowledge graph for the life sciences. eLife, 9, e52614. DOI: 10.7554/eLife.52614  https://elifesciences.org/articles/52614

[7]  Xu, J. et al. (2025). PubMed Knowledge Graph 2.0. Scientific Data, 12, 1018. DOI: 10.1038/s41597-025-05343-8  https://doi.org/10.1038/s41597-025-05343-8

[8]  Callahan, T.J. et al. (2024). An open source knowledge graph ecosystem for the life sciences. Scientific Data, 11, 363. DOI: 10.1038/s41597-024-03171-w  https://doi.org/10.1038/s41597-024-03171-w

[9]  Kim, J. & Owen-Smith, J. (2021). ORCID-linked labeled data for author name disambiguation. Scientometrics. DOI: 10.1007/s11192-020-03826-6  https://arxiv.org/abs/2102.03237

[10]  Sprague, E.R. (2017). ORCID. Journal of the Medical Library Association, 105(2). DOI: 10.5195/jmla.2017.89  https://jmla.pitt.edu/ojs/jmla/article/view/89

[11]  Wikidata. ORCIDator — community tool documentation.  https://www.wikidata.org/wiki/Wikidata:ORCIDator

[12]  Wikidata. Author Disambiguator tool.  https://www.wikidata.org/wiki/Wikidata:Tools/Author_Disambiguator

[13]  Guha, R.V., Brickley, D. & Macbeth, S. (2016). Schema.org: Evolution of structured data on the web. Commun. ACM, 59(2). DOI: 10.1145/2844544  https://dl.acm.org/doi/10.1145/2844544

[14]  Walker Sands. (2025). How Schema Markup Can Enhance LLM Visibility.  https://www.walkersands.com/about/blog/how-can-schema-markup-support-llm-visibility/

[15]  Jurenka, A. (2026). How schema markup fits into AI search — without the hype. Search Engine Land. https://searchengineland.com/schema-markup-ai-search-no-hype-472339

[16]  Schema.org. Health and medical types: MedicalEntity and subtypes.  https://schema.org/docs/meddocs.html

[17]  Wilkinson, M.D. et al. (2016). The FAIR guiding principles for scientific data management. Scientific Data, 3, 160018. DOI: 10.1038/sdata.2016.18  https://doi.org/10.1038/sdata.2016.18

[18]  Evertune AI. (2025). GEO in Pharma: Six Steps to Owning AI-Driven Health Queries.  https://www.evertune.ai/resources/insights-on-ai/generative-engine-optimization-geo-in-pharma-six-steps-to-owning-ai-driven-health-queries

[19]  Open Research Knowledge Graph (ORKG).  https://orkg.org

Olivier Gryson, PharmD, MSc
25 years of experience in digital marketing in the pharmaceutical industry
Special focus on AI Search in Pharma Marketing