Knowledge Graph Experts in Berlin
to connect data and insight, matched in minutes with vetted freelancersHire experts who model business concepts, connect structured and unstructured data, and deliver semantic search or recommendation systems. FRATCH matches you quickly and precisely with vetted, available freelancers suited to your project.
Meet FRATCH Experts in Berlin, who have recently used Knowledge Graph
Abhishek N.
Last position:
Fullstack Developer at DAMALO GmbH
- Own full-stack development of an AI-native enterprise platform built on TypeScript, React, Vite, tRPC, Hono, and PostgreSQL, delivering AI-powered consulting workflows to B2B clients.
- Designed and shipped a multi-agent AI system using ReAct framework and Claude skills-style workflow patterns, including an intelligent PM assistant with rich system prompts, slash commands, tool integrations, and streaming chat UI.
- Architected an LLM evaluation framework: rubric-based LLM-as-judge, golden datasets, regression testing, and automated quality gating — ensuring consistent AI output quality at scale.
- Integrated LangFuse for end-to-end LLM tracing, conversation replays, and evaluation pipelines, enabling data-driven prompt optimisation that reduced token costs and response variance.
- Built with Drizzle ORM, pgvector, and knowledge graphs for structured data access, semantic search, and relationship-aware AI reasoning across the platform.
- Led TanStack React Query migration across the application — replacing manual state management with centralised caching and automatic refetching, reducing data-fetching boilerplate significantly.
- Practiced AI-native development throughout: Claude Code, Codex, Perplexity SDK, and LLM-assisted testing across the full development lifecycle. Deployed on Vercel + Azure ACA with Biome for linting/formatting.
Louis G.
Last position:
Freelance Solutions Architect and Machine Learning Engineer at Self-employed
- Develop and demonstrate solutions using GenAI software like langchain, vercel ai sdk, copilotkit
- Work with customers to understand their challenges and provide the best solutions based on open-source data products
- Build RAG and GraphRAG solutions using Neo4j, lancedb, and Postgres
- Deploy a LLMOps platform using kubernetes, terraform, helmfile, Arize phoenix, mlflow
- Architect and build data pipelines using dbt, Trino, Spark, Iceberg, Airflow, ArgoCD, terraform, kubernetes
- Delivered user-centred technical strategy for Agriculture 4.0 and precision livestock farming, helping my client secure funding from Bpifrance
- Delivered a prospecting tool for a leading French solar carport installer, using geospatial computing (GIS), speeding up the sales process
- Built digital twin architecture for solar carports and EV chargers, making real-time monitoring and smart charging possible
Ludvig G.
Last position:
Founder at Insightl.ai Lernplattform
- Attempted founding of a platform for career development and personal coaching
- Top 3 placement in the Berlin-Brandenburg business plan competition
- Conducted independent market analysis and user research
- Built a comprehensive knowledge graph for roles, skills, and experiences
- Data transformation and setting up data pipelines on Azure
Mojtaba P.
Last position:
Data and AI consultant at Self employed
Helping companies adapt AI into their BI stack, agentic BI, knowledge graphs, internal company RAG system, semantic layers, BI harness, KPI alerts, data warehousing, etl, elt, data modelling, reporting layer with AI touch
Claudia H.
Last position:
Founder & AI Product Lead at Unforgotten
- Conceived, built and iterated an applied-AI MVP that turns in-depth audio interviews into structured, long-form narrative outputs across multiple genres (e.g. memoir, institutional knowledge, thematic essays) using agentic orchestration and multi-step reasoning.
- Designed and implemented core workflows in a Next.js-based stack, working with structured representations (JSON and other formats), retrieval-augmented generation and emerging knowledge graph structures to maintain context and consistency over long documents.
- Defined and tested agent behaviors across realistic storytelling scenarios, including ideal user journeys, edge cases and failure modes, with explicit criteria for coherence, factual alignment and user intent satisfaction.
- Currently running targeted user tests with selected partners to validate use cases and inform the next product iterations.
Kaan D.
Last position:
IT Consultant at Tensora GmbH
Oguzhan Y.
Last position:
Applied AI Consultant at Bertelsmann SE & Co. (Smart Agency)
- Built and deployed agentic digitization tools (LLM website generator and social content) for Mittelstand brands.
- Re-architected AWS infrastructure, added observability and guardrails; owned end-to-end delivery and handover.
- Set up editor accept-rate, p95 latency, and guardrail catch-rate checks.
Discover over 15,000 top freelancers
Statistics of experts using Knowledge Graph
Aggregated from the professional profiles of matched freelancers.
Experience
14 years (Germany: 16 years)
Position duration
2.1 years (Germany: 1.9 years)
Positions per freelancer
7 (Germany: 11)
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Advertising, Energy
Certification focus areas
Business Intelligence, Information Technology, Product Development
Bachelor's degree or higher
100% (Germany: 93%)
Master's degree or higher
86% (Germany: 81%)
Certifications per freelancer
2
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Berlin are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Berlin using Knowledge Graph
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Knowledge Graph experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (100%)
- Advertising (43%)
- Energy (43%)
- Banking and Finance (43%)
- Media and Entertainment (43%)
- Professional Services (43%)
- Automotive (29%)
- Cosmetics (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What knowledge graphs do
A knowledge graph represents entities, attributes and relationships as connected, queryable data. It gives software context that tables and isolated documents often lack, helping teams discover how products, customers, places, concepts and events relate. Common outputs include semantic search, recommendation systems, data catalogs and intelligent question answering.
Core standards and models
Projects commonly use RDF triples, ontologies and taxonomies to define meaning consistently. SPARQL supports graph queries, while OWL and SHACL help describe relationships and validate data. Some teams choose property graph models with labeled nodes and edges, using tools such as Neo4j and Cypher alongside or instead of RDF technologies.
Ecosystem and delivery
Knowledge graph work spans data engineering, information architecture and applied AI. Specialists may connect APIs, databases, documents and master data, then create a graph layer that other applications can use.
- Design entity and relationship models
- Build ingestion and entity-resolution pipelines
- Create ontology, taxonomy and vocabulary structures
- Implement graph search, exploration and validation
- Integrate graph data with machine learning or language systems
When companies need specialists
Companies often bring in freelance expertise when data exists across disconnected systems or when search results lack business context. A specialist can assess the source data, select an appropriate graph model and establish governance before implementation expands. This is useful in retail, manufacturing, finance, healthcare, publishing and public-sector information systems.
What strong professionals deliver
Strong professionals translate business language into precise concepts and relationships. They understand identity resolution, provenance, access control, versioning and data quality, not only graph syntax. They can explain trade-offs between RDF and property graphs, design maintainable ingestion workflows and test whether query results reflect the intended meaning.
- Clear ontology and modeling decisions
- Traceable sources and reliable data lineage
- Queries designed for real user needs
- Documentation that supports future teams
- Practical integration with existing systems
Working with Berlin teams
Knowledge graph projects in Berlin may support technology, commerce, mobility, research or media organizations. Remote collaboration works well when modeling decisions, sample data and acceptance criteria are documented; on-site workshops can help align domain experts around shared definitions. German and English communication may both matter when concepts, policies and source systems span local and international teams.
Frequently asked questions
Need clarity? These are the questions we hear most often about Knowledge Graph.
A knowledge graph connects entities and relationships so software can interpret context across many data sources. Companies use it for semantic search, recommendations, fraud analysis, data integration, question answering and connected data catalogs.
A knowledge graph focuses on relationships and meaning, while a relational database organizes records into predefined tables and joins. Graph models are often useful when connections change frequently or when users need to explore indirect relationships across domains.
A knowledge graph can use either approach, depending on the project. RDF with SPARQL suits standards-based data exchange, shared vocabularies and formal semantics, while property graphs with systems such as Neo4j often suit application-focused traversal and graph analytics.
A knowledge graph specialist should usually understand data modeling, ontology design, SQL, APIs and pipeline development. Experience with entity resolution, metadata management, search, machine learning and language models can also be valuable.
A knowledge graph project needs practical experience with the relevant data sources, domain vocabulary and query patterns, not just familiarity with graph terminology. The specialist should be able to show sound modeling decisions, reliable ingestion and validation against real business questions.
A knowledge graph project can be delivered remotely when stakeholders share source definitions, modeling decisions and test cases in writing. Berlin teams may still benefit from on-site workshops when domain experts need to agree on German and English terminology or resolve conflicting business meanings.
A knowledge graph should be judged by the accuracy, traceability and usefulness of its relationships, not by its size. Ask how sources are linked, how conflicts are handled, how changes are governed and whether representative queries produce explainable results.
A knowledge graph engagement should define the target use cases, source systems, domain scope, modeling method and expected deliverables. Clarify whether the work covers discovery, ontology design, pipeline implementation, graph deployment, documentation or handover.
The average hourly rate of freelancers in Berlin, Germany who have used Knowledge Graph in their recent projects is 93 €, which corresponds to a daily rate of about 745 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Knowledge Graph in their recent projects, 100% hold at least a Bachelor's degree and 86% hold at least a Master's degree.
On average, freelancers in Berlin, Germany who have used Knowledge Graph in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Berlin, Germany who have used Knowledge Graph in their recent projects are German (100%), English (100%), and French (29%).
The most common industries among freelancers in Berlin, Germany who have used Knowledge Graph in their recent projects are Information Technology (100%), Advertising (43%), and Energy (43%).
The most common business areas among freelancers in Berlin, Germany who have used Knowledge Graph in their recent projects are Information Technology (100%), Product Development (100%), and Research and Development (86%).
Main locations of FRATCH Experts, who have recently used Knowledge Graph
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin 
Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
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