1 to 25 of 40 RDF Jobs in the UK

critical code, troubleshooting, and complex customer or stakeholder demonstrations. Mandatory Hands-on Technical Skills • Knowledge graphs: Ontology and semantic model design; property graphs and RDF; graph schema evolution; knowledge representation; provenance; graph quality; entity and relationship resolution. • Graph platforms: Deep implementation experience with Neo4j and Cypher; working knowledge ...

critical code, troubleshooting, and complex customer or stakeholder demonstrations. Mandatory Hands-on Technical Skills Knowledge graphs: Ontology and semantic model design; property graphs and RDF; graph schema evolution; knowledge representation; provenance; graph quality; entity and relationship resolution. Graph platforms: Deep implementation experience with Neo4j and Cypher ; working knowledge ...

teams who deliver them. BASIC (REQUIRED) QUALIFICATION Bachelor's degree or equivalent, plus all of the following: Significant experience with Knowledge Graph technologies (RDF, SPARQL, LPG, SHACL) Advanced practical knowledge of schema design, ontology management, and KG curation Seasoned in designing and developing KG solutions and graph-based ML models ...

teams who deliver them. Basic (required) Qualification Bachelor's degree or equivalent, plus all of the following: Significant experience with Knowledge Graph technologies (RDF, SPARQL, LPG, SHACL). Advanced practical knowledge of schema design, ontology management, and KG curation. Seasoned in designing and developing KG solutions and graph-based ...

responsible approaches to AI, automation, guardrails, auditability and escalation. What We’re Looking For Strong understanding of knowledge representation and semantic technologies, including OWL, RDF, SWRL, SPARQL, JSON-LD, Turtle and N-Quads. Experience with semantic modelling and graph/triple-store technologies such as PoolParty, Stardog, TigerGraph, Ontotext GraphDB ...

graph and ontology specifications. Define entities, relationships, attributes, constraints, classifications, and semantic rules. Develop graph data models using appropriate standards and technologies, such as: RDF and RDF-star OWL and SKOS SHACL SPARQL Property-graph models and Cypher Build data-mapping and ingestion processes from relational databases, APIs, documents … modelling, knowledge representation, or a closely related discipline. Strong understanding of data modelling principles and information architecture. Practical experience with one or more of: RDF, OWL, SKOS, SHACL, SPARQL Neo4j, Amazon Neptune, Stardog, GraphDB, AllegroGraph, or similar graph platforms Cypher or other graph-query languages Experience with data integration ...

pipelines (e.g., Pinecone, Weaviate, Qdrant, pgvector). Practical experience implementing RAG architectures or LLM-integrated data pipelines. Familiarity with semantic web standards—JSON-LD, RDF, OWL, or SKOS. Strong Python skills and experience with data pipeline frameworks. Experience with cloud-native data platforms (AWS, Azure, or GCP). Nice ...

pipelines (e.g., Pinecone, Weaviate, Qdrant, pgvector). Practical experience implementing RAG architectures or LLM-integrated data pipelines. Familiarity with semantic web standards—JSON-LD, RDF, OWL, or SKOS. Strong Python skills and experience with data pipeline frameworks. Experience with cloud-native data platforms (AWS, Azure, or GCP). Nice ...

management (MDM), and reference data management. Proven ability to construct enterprise taxonomies, ontologies, data lineages, and business glossaries. Strong understanding of semantic web technologies (RDF, OWL, Knowledge Graphs). Data Modeler: Expert proficiency in industry‐standard data modeling tools. Mastery of multiple modeling paradigms, e.g. Relational, Dimensional, and NoSQL/ ...

experience. Proven track record delivering semantic layers, ontologies, and knowledge graphs into production environments. Strong understanding of semantic web technologies and knowledge representation (e.g. RDF, OWL, SPARQL, Cypher) and experience with graph databases such as Neo4j or GraphDB. Solid programming skills in Python (or similar) for data integration, pipeline development ...

experience. Proven track record delivering semantic layers, ontologies, and knowledge graphs into production environments. Strong understanding of semantic web technologies and knowledge representation (e.g. RDF, OWL, SPARQL, Cypher) and experience with graph databases such as Neo4j or GraphDB. Solid programming skills in Python (or similar) for data integration, pipeline development ...

responsible approaches to AI, automation, guardrails, auditability and escalation. What We’re Looking For Strong understanding of knowledge representation and semantic technologies, including OWL, RDF, SWRL, SPARQL, JSON-LD, Turtle and N-Quads. Experience with semantic modelling and graph/triple-store technologies such as PoolParty, Stardog, TigerGraph, Ontotext GraphDB ...

responsible approaches to AI, automation, guardrails, auditability and escalation. What We’re Looking For Strong understanding of knowledge representation and semantic technologies, including OWL, RDF, SWRL, SPARQL, JSON-LD, Turtle and N-Quads. Experience with semantic modelling and graph/triple-store technologies such as PoolParty, Stardog, TigerGraph, Ontotext GraphDB ...

Skills & Experience Strong experience in designing and implementing Knowledge Graphs , Ontologies , and Semantic Data Models . Hands-on expertise with semantic technologies such as RDF, OWL, SPARQL, SKOS, SHACL , and graph databases. Experience with graph platforms such as Neo4j, Stardog, GraphDB, Amazon Neptune, Ontotext, or Azure Cosmos DB (Gremlin) . ...

case requires it. You have experience with a graph database or query language, or closely related semantic tooling. Familiarity with Cypher, SPARQL, RDF or property graphs is valuable; no single stack is required. You can work with analysts, product managers, data scientists and engineers to define an ambiguous problem, make ...

Relational and NoSQL (e.g., DynamoDB, PostgreSQL). - Observability/SRE: OpenTelemetry, distributed tracing, metrics/SLOs for data services. Bonus: - Semantic Technologies: Knowledge graphs, RDF/OWL, or property graphs — particularly relevant to the connected/semantic AECDM. - BIM Expertise: Building Information Modeling concepts, Revit families, or similar parametric modeling ...

degree or equivalent BASIC (REQUIRED) QUALIFICATION Bachelor's degree or equivalent, plus at least 4 of the following: Experience with Knowledge Graph technologies (RDF, SPARQL, LPG, SHACL) solid experience in schema design, ontology management, and KG curation Proven experience in designing and developing KG solutions and graph-based ML models ...

multi-agent architectures, with the judgement to know where they fail. Deep, hands‐on expertise in knowledge graphs and semantic modelling: ontology design (RDF/OWL/SKOS or property-graph equivalents), taxonomy and controlled-vocabulary design, entity resolution, schema evolution and versioning, embeddings, vector databases and grounding strategies. Evaluation ...

multi-agent architectures, with the judgement to know where they fail. Deep, hands-on expertise in knowledge graphs and semantic modelling: ontology design (RDF/OWL/SKOS or property-graph equivalents), taxonomy and controlled-vocabulary design, entity resolution, schema evolution and versioning, embeddings, vector databases and grounding strategies. Evaluation ...

schemas, review queries, and discuss data modeling trade-offs with engineers Understanding of graph data modeling concepts including nodes, edges, properties, ontologies and taxonomies, RDF vs property graphs, and entity resolution Proven track record of translating ambiguous business problems into precise functional and non-functional engineering requirements Excellent stakeholder management ...

expertise and the ability to apply it in any enterprise context. Responsibilities Ontology Design & Maintenance: Design, develop, and maintain ontologies (using OWL/RDF or similar) that model key enterprise data domains and relationships, ensuring a consistent and shared data vocabulary across the organization. This includes collaborating with domain experts … similar).Semantic Web Proficiency: Strong knowledge of semantic web technologies and standards - specifically, hands-on proficiency with OWL (Web Ontology Language) and RDF (Resource Description Framework) for ontology modelling, as well as SPARQL for querying graph data.Knowledge Graph Experience: Practical experience building or maintaining knowledge graphs or linked data systems ...

expertise and the ability to apply it in any enterprise context. Responsibilities Ontology Design & Maintenance: Design, develop, and maintain ontologies (using OWL/RDF or similar) that model key enterprise data domains and relationships, ensuring a consistent and shared data vocabulary across the organization. This includes collaborating with domain experts … similar).Semantic Web Proficiency: Strong knowledge of semantic web technologies and standards - specifically, hands-on proficiency with OWL (Web Ontology Language) and RDF (Resource Description Framework) for ontology modelling, as well as SPARQL for querying graph data.Knowledge Graph Experience: Practical experience building or maintaining knowledge graphs or linked data systems ...

willingness to learn the domain Experience with data cataloging, metadata governance, or data discovery platforms Familiarity with semantic web or knowledge graph concepts (e.g., RDF, SKOS, OWL) and experience integrating these into usable data models. Formal ontology design is not required, but ability to collaborate with ontologists is essential Understanding ...

connected R&D data foundations through governance, master data management, interoperability, metadata management, and modern data architectures. Lead the application of semantic technologies, ontologies, RDF-based knowledge graphs, and common data models to create a shared understanding of scientific, research, preclinical, and clinical information across systems.Enable advanced analytics, scientific knowledge ...

connected R&D data foundations through governance, master data management, interoperability, metadata management, and modern data architectures. Lead the application of semantic technologies, ontologies, RDF-based knowledge graphs, and common data models to create a shared understanding of scientific, research, preclinical, and clinical information across systems.* Enable advanced analytics, scientific ...