Best Practices for Making Legacy Data Semantic and AI-Ready - Glossary of Terms and Phrases
Best Practices for Making Legacy Data Semantic and AI-Ready
Chapter 2. Glossary of Terms and Phrases
This glossary introduces key terms and phrases used throughout this document.
| Term | Definition |
|---|---|
| AI-Friendly Data | Data designed or enriched with explicit identity, definitions, relationships, lineage, governance, and retrieval context so people, systems, and AI can interpret and use it reliably. |
| AI-Ready Data | Data that AI systems can reliably identify, retrieve, interpret, traverse, relate, summarize, reason over, and use within governed constraints. |
| Descriptive Predicate | A natural-language-friendly relationship phrase that explains the meaning and direction of a Semantic Relationship, such as is managed by, owns, supports, uses, or depends on. |
| Evidence Triangulation | The validation of reconstructed meaning by comparing multiple independent sources, such as data, code, documentation, reports, lineage, operational behavior, and Subject Matter Expert knowledge. |
| Foreign Key | A technical database construct that links records across tables and can serve as evidence for candidate Semantic Relationships. |
| Institutional Knowledge | Enterprise knowledge accumulated through decisions, practices, history, and experience that may exist in governed artifacts or remain dependent on individual memory. |
| Knowledge Debt | The backlog of undocumented, fragmented, implicit, outdated, inconsistent, inaccessible, or poorly governed knowledge that increases the cost, risk, delay, and uncertainty of using enterprise data. |
| Knowledge Debt Register | A governed record of known knowledge gaps, affected assets, risks, evidence, dependencies, owners, priorities, remediation actions, validation results, and lifecycle status. |
| Legacy Identifier | A machine-readable identifier, such as a UID, GUID, surrogate key, natural key, or system code, used by legacy systems for referential integrity, processing, lineage, or integration. |
| Lineage | The trace from a semantic representation back to the source records, systems, transformations, rules, Ontology elements, evidence, and approvals that produced it. |
| Meaning Discovery | The structured work of uncovering definitions, rules, exceptions, relationships, source authority, historical context, and operational interpretations hidden in data and systems. |
| Noun Type | A category of thing represented in an Ontology or Taxonomy, such as Person, Customer, Product, Application, Service, Vendor, or Contract. |
| Ontology | A governed model of meaning that defines Noun Types, Taxonomies, relationships, predicates, descriptions, rules, constraints, and interpretation patterns for a data domain. |
| Semantic Attribute | A named data property expressed in meaningful language rather than opaque source-system terminology. |
| Semantic Authority | The assigned person or governance body authorized to approve definitions, mappings, relationships, rules, and other semantic representations for enterprise use. |
| Semantic Conversion | The governed process of preserving source integrity while converting hidden or ambiguous legacy meaning into explicit identifiers, definitions, attributes, relationships, rules, lineage, and AI-ready representations. |
| Semantic Conversion Operating Model | The governed structure of roles, responsibilities, decision rights, workflows, evidence, validation, and controls used to produce and maintain approved semantic knowledge. |
| Semantic Drift | The divergence between a semantic representation and the current source data, business meaning, ownership, relationships, rules, or Ontology definitions it is supposed to represent. |
| Semantic ID | A stable, natural-language-friendly identifier that combines a Noun Type with a normalized instance name, such as customer.acme-manufacturing or application.claims-intake-portal. |
| Semantic Instance Document | A whole, readable, AI-ready document generated for a specific instance, such as a Person, Customer, Product, Application, Service, Vendor, or Contract, before indexing for retrieval. |
| Semantic Layer | The governed Ontology-based layer of meaning that defines, describes, relates, and constrains data so it can be interpreted, traversed, reasoned over, and used by AI. |
| Semantic Readiness | The condition in which data has enough governed meaning, context, relationships, lineage, and metadata for AI systems to interpret, traverse, reason over, and use it reliably. |
| Semantic Relationship | A governed Subject-Predicate-Object statement that explains how two instances relate in business or operational terms. |
| Semantic Trait | A meaningful characteristic of an instance, often derived from one or more Semantic Attributes. |
| Separation of Semantic Duties | The control that distinguishes who proposes, reviews, approves, implements, publishes, and monitors semantic meaning so no single participant becomes the unchecked authority. |
| Source of Record | The authoritative source system, database, application, or repository that owns the source truth for a record or value. |
| Subject-Predicate-Object | A triple-like relationship pattern in which a subject instance is connected to an object instance through a descriptive predicate. |
| Taxonomy | A governed classification structure that organizes Noun Types, concepts, or instances into meaningful categories. |
| Technical Debt | The backlog of structural, design, code, architecture, infrastructure, data, documentation, security, or operational weaknesses that make systems harder, riskier, slower, or more expensive to change and operate. |
| Vector Database | A retrieval technology that stores vector embeddings and associated metadata so AI systems can retrieve semantically similar content. |
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