Structured Data for AI Visibility Terminology: A Practical Implementation

In the rapidly evolving world of Artificial Intelligence (AI), defining new industry terms and ensuring they are understood by search engines and AI systems is a challenge. When 365i, an AI site identity service, set out to publish the canonical definition of AI Visibility, they faced this exact question: how to make these definitions machine-readable? This article shares their practical implementation, insights, and recommendations.

Defining New Industry Terms: The Challenge

365i created precise definitions for six interrelated concepts: AI Visibility, AI Visibility Checking, AI Discovery Files, AI Visibility Tracking, AI Visibility Monitoring, and AI Retrieval Testing. These terms describe a new domain and didn't exist in established dictionaries. The goal was to help search engines and AI systems understand that these were formal definitions, part of a controlled vocabulary, uniquely citable, and with a canonical source.

The Solution: Schema.org's DefinedTermSet and DefinedTerm

Schema.org's DefinedTermSet and DefinedTerm were found to be the solution for this use case. The DefinedTermSet is designed for sets of defined terms, such as classifications, glossaries, dictionaries, or enumerations. The DefinedTerm lets you mark up individual terms with properties like name, description, termCode, inDefinedTermSet, and url.

Implementation Details

• Using @graph for Multiple Entities: 365i used @graph to define multiple connected entities in a single JSON-LD block.
• Fragment Identifiers for Deep Linking: Each term got its own @id with a fragment identifier, allowing other pages and sites to link directly to specific definitions.
• Term Codes for Citation: The termCode property gave each term a short, citable identifier, particularly useful for documentation, academic citation, and machine processing.
• Wikidata sameAs Links: Corresponding Wikidata items were created for each term and linked via sameAs, creating a bidirectional connection between the definitions and the open knowledge graph.
• Combining with Other Schema Types: The same page also included WebPage, TechArticle, Organization, BreadcrumbList, SpeakableSpecification to provide additional context.

Implications for North East India and Beyond

The practical implementation of structured data for AI Visibility terminology by 365i is not only relevant to the AI industry but also has implications for businesses and organizations in North East India and the broader Indian context. As AI systems become more prevalent, understanding and implementing structured data can help improve search engine rankings, enhance AI system interactions, and foster a more accurate representation of digital entities.

Lessons Learned and Recommendations

365i learned several lessons during the implementation process, including the distinction between DefinedTerm and CategoryCode, the proper use of inDefinedTermSet, and the alignment of HTML with JSON-LD using itemscope, itemtype, and itemprop attributes. They also recommend licensing permissively, providing machine-readable formats, and creating Wikidata items to connect to the broader knowledge graph.

Conclusion

The practical implementation of structured data for AI Visibility terminology by 365i demonstrates the importance of making definitions machine-readable. As the AI landscape continues to evolve, understanding and implementing structured data will become increasingly crucial for businesses and organizations to communicate accurately with AI systems.