VISEON: Glossary

VISEON Semantic Intelligence Glossary

This glossary defines the key concepts, technologies, and methodologies that power AI discoverability and semantic search optimisation. Each term represents a critical component in building knowledge graphs that make brands discoverable to generative AI systems.

Search Engine Optimisation (SEO)

The practice of optimising websites to improve visibility in search engine results through technical, content, and structural improvements.

Also known as: SEO, Search Optimisation

Semantic SEO

SEO strategy focusing on meaning and context through structured data to help search engines understand content relationships and intent.

Also known as: Semantic Search Optimisation

Knowledge Graph

A structured representation of entities, attributes, and relationships that enables machine understanding and reasoning. Knowledge graphs form the foundation of how AI systems understand and connect information across the web.

AI Discoverability

The capability of content to be found, understood, and recommended by AI-powered search engines and generative AI systems. Brands without AI discoverability face digital obscurity in the age of generative search.

Also known as: Generative AI Visibility, AI Search Optimisation

Schema.org

Collaborative vocabulary for structured data markup on web pages, enabling search engines and AI systems to understand content semantics. Schema.org provides the standard vocabulary used across the web for describing entities and their relationships.

JSON-LD

A method of encoding linked data using JSON, widely used for implementing Schema.org structured data. JSON-LD is the preferred format for semantic markup because it separates structured data from HTML content.

Also known as: JSON for Linking Data

Structured Data

Standardised format for providing information about a page and classifying the page content to help search engines understand it. Structured data transforms unstructured web content into machine-readable information.

Also known as: Semantic Markup

Generative AI

AI systems capable of generating new content including text, images, and responses based on training data and user prompts. Generative AI systems like ChatGPT, Claude, and Gemini are fundamentally changing how people discover information.

Also known as: Generative Artificial Intelligence, Gen AI

Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) AI technique that combines information retrieval with text generation to provide more accurate and contextual responses. RAG enables generative AI systems to ground their outputs in retrieved information from external data sources such as document collections, vector databases, or enterprise knowledge bases, reducing the likelihood of hallucinations.

Also known as: RAG, RAG AI, VectorRAG

GraphRAG

GraphRAG AI technique that combines information retrieval with text generation using structured knowledge graphs. GraphRAG enables generative AI systems to ground their responses in verified, relationship-rich information by traversing graph-based data structures, allowing for more sophisticated reasoning across connected entities and their relationships compared to traditional vector-based retrieval methods.

Also known as: GraphRAG, Graph-based RAG

Digital Obscurity

The state of being invisible or undiscoverable to AI-powered search engines and generative AI systems due to lack of structured data. Digital obscurity is the new invisibility – brands without semantic markup are effectively invisible to AI-powered discovery.

Category Theory

A mathematical framework for describing abstract structures and relationships, applied to knowledge graph architectures for compositional coherence. VISEON applies category theory principles to ensure knowledge graphs maintain mathematical consistency and composability.

Model Context Protocol (MCP)

An open protocol that enables AI assistants to securely access data and tools through standardised server connections. MCP servers and tools allows AI systems to dynamically retrieve information from knowledge graphs and external data sources.

Also known as: MCP

Python Package Index (PyPI)

The official repository for Python software packages, enabling distribution and installation of Python libraries. VISEON’s tools and integrations are available through PyPI for easy implementation.

Also known as: PyPI, PyPI.org

Vector Embeddings

Numerical representations of data that capture semantic meaning, enabling AI systems to understand relationships between concepts. Vector embeddings power semantic search by representing entities as points in high-dimensional space where similar concepts cluster together.

Also known as: Embeddings, Semantic Vectors

Semantic Search

Search technique that understands user intent and contextual meaning rather than just matching keywords. Semantic search powers modern AI-driven discovery by understanding the relationships between concepts.

NLWeb

Microsoft Research protocol for web-scale natural language understanding optimised for LLM ingestion through structured JSON-LD. NLWeb demonstrates how major AI research teams are prioritising structured data for training language models.

Also known as: Natural Language Web

Ontology

Formal specification of concepts, relationships, and constraints within a domain, enabling shared understanding between systems. Ontologies provide the semantic framework that allows different AI systems to interpret knowledge graphs consistently.

Also known as: Web Ontology

Resource Description Framework (RDF)

W3C standard for describing resources on the web through subject-predicate-object triples. RDF forms the foundation of the semantic web and knowledge graph technologies.

Also known as: RDF

SPARQL

Query language for databases that use RDF format, enabling semantic queries across knowledge graphs. SPARQL allows complex queries across distributed knowledge graphs using semantic relationships.

Also known as: SPARQL Protocol and RDF Query Language

Large Language Model (LLM)

AI model trained on vast text datasets to understand and generate human language. LLMs power generative search engines and increasingly rely on structured data and knowledge graphs for accurate information retrieval.

Also known as: LLM, Language Model

autoMagically

Automation that happens seamlessly and effortlessly, combining automated processes with intelligent orchestration. Essential for hyperautomation and agentic RPA implementations.

Also known as: Automagic, Automatic and Magical

Agentic RPA

Advanced robotic process automation where AI agents make autonomous decisions within automated workflows, enabling self-directed process execution.

Also known as: Agentic Robotic Process Automation, Intelligent RPA

Hyperautomation

Business-driven approach to rapidly identify, vet, and automate business and IT processes through orchestrated use of multiple technologies including AI and RPA.

AI and Data Engineering

The application of artificial intelligence and data engineering principles to create innovative, scalable solutions that drive business growth and operational efficiency.

Also known as: AI Engineering, Data Engineering, AI/ML Engineering

Semantic Web

An extension of the World Wide Web that enables data to be shared and reused across applications, enterprises, and communities through standardised formats. Built on RDF and ontology frameworks.

Also known as: Web 3.0, Machine-Readable Web

Agentic Web

An evolution of the World Wide Web where AI agents autonomously discover, interpret, and interact with web content and services on behalf of users or organizations. Built on protocols like MCP (Model Context Protocol) and NLWeb, enabling agent-to-agent and agent-to-service communication through structured data formats like Schema.org and JSON-LD.

Also known as: Open Agentic Web (Microsoft terminology)

Status: Emerging concept (2025), not yet standardised

Key difference from Semantic Web: Semantic Web makes data machine-readable; Agentic Web makes services agent-actionable

Agentic Search

Next-generation search systems where AI agents autonomously navigate, retrieve, and synthesise information across multiple sources to answer complex queries. Uses Model Context Protocol and RAG techniques.

Also known as: AI Agent Search, Autonomous Search

Augmented Search

Search systems enhanced with AI capabilities including natural language understanding, context awareness, and intelligent result synthesis through semantic search and generative AI.

Also known as: AI-Augmented Search, Enhanced Search

Voice Search

Search queries performed through voice commands, requiring natural language processing and conversational AI capabilities for optimal results.

Also known as: Voice Query, Spoken Search

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Explore how these concepts work together to power AI discoverability:

This glossary is maintained by VISEON.IO as part of our commitment to advancing semantic intelligence and AI discoverability standards.