Knowledge Graph Design & Implementation.
Connect your organization's data, content, and expertise into a queryable, AI-ready knowledge graph — and activate the use cases that disconnected systems and flat databases have never been able to deliver.
Information Management Services
The Situation — When Clients Come to Us
Organizations come to us for knowledge graph work when:
- Data and content exist in disconnected repositories with no formal model of how entities, concepts, and relationships connect — making cross-system insight, AI reasoning, and regulatory traceability unreliable or impossible
- GenAI, RAG, or intelligent search applications are underperforming because the semantic layer those systems depend on — a populated, queryable graph of entities and relationships — was never built
- Organizations in FSI, life sciences, healthcare, publishing, government, or defense where entity resolution, knowledge reuse, regulatory compliance, or data lineage depends on representing relationships that relational databases and taxonomies cannot adequately model
- Organizations that have designed a taxonomy and ontology and are now ready to populate an instance graph — connecting real-world data to the conceptual schema and activating specific use cases
- Enterprises spending 40–60% of IT budgets moving and mapping data between siloed applications — and recognizing that a knowledge graph is the architectural foundation that eliminates that cost permanently
What We Do — Our Approach
// Phase 1
Use Case Scoping & Architecture Design
We work with business and technical stakeholders to define the knowledge graph scope — identifying the entities, relationships, and use cases (semantic search, Client 360, entity resolution, regulatory compliance, RAG, recommendation) that will be activated in the first phase. We design the knowledge graph architecture — ontology foundation, data source integration approach, triplestore selection, and the 'think big, start small' phasing plan that delivers use-case value at each increment.
// Phase 2
Ontology Foundation & Data Modeling
We design or refine the ontology schema that will structure the knowledge graph — defining class hierarchies, property definitions, relationship model, and the data transformation logic required to map source system data to the graph model.
// Phase 3
Graph Population & Integration
We transform and load data from source systems into the knowledge graph — writing and executing ETL/ELT transformation scripts, validating graph data for logic and reasoning integrity, and integrating with the chosen triplestore platform (GraphDB, MarkLogic, Amazon Neptune, or equivalent).
// Phase 4
Use Case Activation
We activate the priority use cases against the populated graph — developing SPARQL queries, semantic search integrations, API layers, or AI/RAG connections that deliver demonstrable business value. Each phase delivers a working use case, not just a structural milestone.
// Phase 5
Governance, Training & Expansion Roadmap
We design the knowledge graph governance model — data stewardship for graph content, ontology change management, and the versioning and release processes that keep the graph current. We deliver training for internal teams and a phased expansion roadmap for adding additional domains and use cases.
What You Get — Deliverables
- Knowledge graph architecture design — ontology foundation, integration approach, triplestore selection, and phasing plan
- Ontology schema (RDF/OWL) structuring the knowledge graph
- Populated knowledge graph with integrated data from source systems
- Data transformation scripts and integration documentation
- Graph validation and reasoning test results
- SPARQL query library for priority use cases
- Use case–specific integrations (semantic search, RAG, API, entity resolution, compliance reporting) — as scoped
- Knowledge graph governance model — stewardship, versioning, and change management
- Internal team training and knowledge transfer materials
- Phased expansion roadmap for additional domains and use cases
Connect your data, content, and expertise into a knowledge graph that makes AI reliable and integration debt a thing of the past.
Iknow's knowledge graph practice combines ontological design expertise with deep implementation capability. Let's discuss your use case.
