Triple Structuring
Proper structuring of Triples is essential for maximizing the functionality and usability of the Intuition system. By adhering to standardized formats and selecting appropriate Predicates, users ensure that data remains easily indexable, queryable, and discoverable. These practices enhance the system’s overall utility and accessibility.
| Subject | Predicate | Object |
|---|---|---|
| The subject in a simple English sentence such as John runs, John is a teacher, or John drives a car, is the person or thing about whom the statement is made, in this case John. | A predicate is the modifier of the subject - a subsequent description of the subject headed with a verb. For the simple sentence "John linguistics, an object is any of several types of arguments. An argument is an expression that helps complete the meaning of a predicate. |
A triple’s state is composed of:
- The reference to a [Subject] and its associated metadata
- The reference to a [Predicate] and its associated metadata
- The reference to an [Object] and its associated metadata
- Users positions on the affirmative/negative vaults of the Triple
Standardized Triple Construction
When structuring Triples, it is important to establish clear and consistent relationships. Consider the scenario of adding a Book to a Book List. There are multiple valid ways to express this relationship, such as:
| Subject | Predicate | Object |
|---|---|---|
| Book | inList | Book List |
| Book List | hasEntry | Book |
Over time, the community may converge on one preferred expression. While initial experimentation may lead to variation, the system is designed to foster consensus, ensuring that widely accepted Triple structures become the standard.
Incentivizing Consensus
Economic incentives play a key role in promoting alignment around effective Triple structures. Users are rewarded for adopting and promoting structures that they anticipate will gain widespread acceptance. This incentivization mechanism ensures that the most coherent and efficient structures rise to prominence, creating a more organized and interoperable data ecosystem.
Token Curated Registry of Triples
The Intuition system implements a global, permissionless, decentralized Token Curated Registry (TCR) for structured data. This approach:
- Reduces the "human friction" associated with traditional data structuring methods.
- Provides guardrails for organic convergence on standard data structures.
- Creates a self-sustaining system where users continuously refine and optimize semantic data.
Through this mechanism, the Intuition system becomes a dynamic, decentralized platform for structuring and refining knowledge.
Best Practices for Atom and Triple Structuring
The integrity of the Intuition system relies on consistent adherence to best practices in structuring Atoms and Triples. While incentives encourage good behavior, a clear understanding of these practices is crucial during the platform's early stages. Without structured guidance, the risk of suboptimal data creation increases, potentially undermining the system’s effectiveness.
To mitigate this risk, Intuition emphasizes education and community-driven refinement of data structuring standards. This ensures the creation of high-quality, semantically robust data.
Promoting Flatness in Data Structuring
To enhance scalability and maintain semantic coherence, the Intuition system prioritizes flat data structures. This means composing higher-order data relationships using Triples rather than deeply nested Atoms. Flat structures reduce data fragmentation and simplify navigation, making the system more efficient.
The Power Set Problem
A flat data structure directly addresses the exponential complexity of the Power Set Problem. The power set of a set ‘S’ contains all possible subsets of ‘S’, leading to 2^n potential combinations for a set with n elements. For example:
If
$$ S = {1, 2, 3} $$
the subsets are:
$$ {}, {1}, {2}, {3}, {1, 2}, {1, 3}, {2, 3}, {1, 2, 3} $$
The exponential growth of potential combinations highlights the difficulty of reconciling complex data schemas, a challenge the Intuition system overcomes through flat structuring.
Simplified Example: Verifiable Credentials
Consider a schema for a "Profile" containing 25 fields, such as "Name" and "Age." This results in
$$ 2^{25}=33,554,432 $$
potential combinations. Adding permutations for field variations (e.g., "First Name" vs. "Full Name") compounds this complexity exponentially. Such challenges are addressed by minimizing nested structures and promoting flat, reusable data models.
Implementing Flat Structures
The Intuition system incentivizes users to adopt minimal and efficient schemas, optimizing reusability and semantic composability. For example:
- Create discrete fields for an object:
- Book [title][Book Title]
- Book [publishedDate][Publication Date]
- Associate the object with a subject:
- Author [hasWritten][Book]
By breaking down data into Semantic Triples, the Power Set Problem is effectively reduced to a linear model. This ensures scalability and manageability while maintaining the system’s economic and semantic integrity.
Flat, modular data structures enable a more navigable and efficient Intuition system, empowering users to focus on meaningful knowledge creation and curation.