Knowledge Graphs & Semantic Web

Some problems are fundamentally about relationships. Knowledge graphs model complex domains in ways that traditional databases cannot, enabling queries that would be impossible with tables and joins.

What We Offer

Ontology Design — Formal modelling of your domain using OWL and RDFS. We help you capture the concepts, relationships, and rules that define your business in a machine-readable form.

RDF & SPARQL Development — Graph database implementation using RDF triple stores. Complex queries across linked data using SPARQL, from simple lookups to sophisticated inference.

NLP-Powered Analytics — Natural language processing pipelines that extract entities and relationships from unstructured text, automatically populating knowledge graphs from documents.

Visualisation & Exploration — Interactive graph visualisation using d3.js and specialised graph tools. Make complex relationships comprehensible to non-technical stakeholders.

Technologies

Standards: RDF, RDFS, OWL, SKOS, SPARQL 1.1

Triple Stores: Apache Jena, Stardog, GraphDB, Amazon Neptune

Property Graphs: Neo4j, Amazon Neptune

NLP: spaCy, Stanford NLP, custom entity extraction

Visualisation: d3.js, Cytoscape.js, custom graph rendering

Our Approach

Knowledge graphs are powerful but can become academic exercises without clear business value. We focus on practical applications—improving search, enabling discovery, automating classification—that deliver measurable ROI.