RAG & semantic search
Metadata and relationships help select more relevant sources.
KNOWLEDGE ARCHITECTURE
Knowledge Architecture for AI
AI is only as good as the knowledge it works with.
Documents, data and concepts must be findable, understandable and connected before AI can produce reliable results. Knowtive works with the information architecture beneath modern AI and knowledge systems.
01Documents→
02Metadata→
03Concepts→
04Entities→
05Relationships→
06Search / AI
METADATA
Metadata gives information context
Document type, author, project, product, date, subject and access classification describe what information is, where it belongs and how it may be used.
This supports filtering, retrieval, governance, permissions and context.
TAXONOMIES
A shared language for classification
A taxonomy is a controlled vocabulary that organises concepts into a practical hierarchy. It helps people and systems apply the same terms and makes filtering more consistent.
01Technology
02Artificial Intelligence
03Machine Learning
04Large Language Models
Researcher
WORKS ON →
Project
USES ↓
Technology
← DESCRIBES
Publication
ONTOLOGIES
Ontologies — when relationships matter
A taxonomy organises concepts into categories. An ontology also describes meaning and relationships between concepts and entities.
A Researcher works on a Project. A Project uses a Technology. A Publication describes a Technology. These relationships can provide better context for semantic search, RAG, knowledge graphs, entity resolution, research intelligence and explainability.
A simple information problem can often be solved with good metadata and strong search. Ontologies become particularly relevant when the domain is complex and relationships carry meaning.
ENTITIES & ENTITY RESOLUTION
The same reality. Different names.
One organisation may appear under several names. Entity resolution helps identify candidates, compare evidence, resolve identity and preserve provenance without losing the original wording.
Novo Nordisk A/SNovo NordiskNovo
01Identify candidate
02Compare evidence
03Resolve identity
04Preserve provenance
RESOLVED ENTITYNovo Nordisk3 documented aliases
KNOWLEDGE GRAPHS
Make real relationships visible
A knowledge graph can represent connections between documents, people, projects, products, organisations, concepts and technologies. Every connection describes a semantic relationship rather than decorative proximity.
Project Atlas
Maja JensenPERSON
Status Q2DOCUMENT
KnowtiveORGANISATION
Semantic searchTECHNOLOGY
Retrieval qualityCONCEPT
WHY THIS MATTERS FOR AI
Better structure gives AI better context
Knowledge architecture can strengthen retrieval for RAG, semantic search, AI assistants and research intelligence while supporting provenance, explainability and access control.
AI Knowledge Assistants
Structure gives an assistant better context.
Research intelligence
Entities improve literature and patent surveillance.
Governance
Provenance and access control make use more manageable.
WHEN IS THIS NECESSARY?
Use only the structure the problem requires
Not every solution needs an ontology or knowledge graph. A simple information problem may need only clean metadata and strong search.
Complex domains with many concepts, name variants and relationships may benefit from richer semantic models. Knowtive starts with value and complexity, not a preferred technology.
SIMPLEMetadata + search
→Clear document types, fields and filters.
COMPLEXSemantic model
Entities, ontologies and knowledge graphs.
Knowtive makes knowledge useful.
Talk to Knowtive about your knowledge architecture
Start with the problem. Together, we can identify the combination of structure, search and AI that makes sense.
Let’s talk