Knowledge Graph Experts in Munich
in minutes from over 15,000 CVs with the power of AI.Hire experts who design linked data models, build graph schemas, and connect sources into usable knowledge layers. They work with RDF, SPARQL, OWL, and property graphs, then deliver search, recommendation, and entity resolution projects with fast, precise matching from vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used Knowledge Graph
Fred Hauschel
Last position:
Software Architect and Developer at Personal project
A recurring problem in my own AI-supported projects: requirements analysis, use cases, and architecture decisions can be created quickly with AI support, but they remain hard to trace and scattered across Markdown files – knowledge is lost as soon as it is no longer in the context window. arknet turns requirements engineering and architecture knowledge into structured, verifiable data instead of plain text: requirements, use cases, and architecture decisions as a continuously linked knowledge graph, traceable from the requirement to the architecture decision – queryable for both people and AI agents alike. Technically based on RDF/OWL and its own MCP server.
Result: MCP daemon running, Docker image automatically published on GHCR, nine hexagonal modules, eleven ADRs (including an open-core licensing model). Requirements engineering and ubiquitous language hexagon active. Publicly available since 07/2026 as a Community Edition under Apache-2.0 (github.com/kogn-io/arknet), together with the Claude Code plugin and the GHCR image; open-core model.
Label: Java, Maven, RDF, RDF4J, OWL, SPARQL, Model Context Protocol, Spring AI, Docker, GitHub, Git, Claude Code, Obsidian, DDD, Hexagonal Architecture, ArchUnit, JUnit, AssertJ
Mirza Klimenta
Last position:
Agentic AI for a DeepResearch project at Freelance
- Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
- Used multiple experts (OpenAI models) collaborating during document drafting
- Extracted useful information from the knowledge graph
- Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
- Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
- Deployed initial application as a Streamlit app
Omar Ashour
Last position:
Senior Fullstack AI Engineer (Team Lead – B2C Platform) at mama health
- Partner directly with C-level leadership (CEO, CAIO, CTO) on architecture, OKR strategy, and cross-team roadmap prioritization, translating strategic goals into structured engineering requirements.
- Surfaced and mapped technical debt across the entire organization with C-level leadership and co-defined a prioritized remediation strategy, balancing debt paydown against feature delivery.
- Led code reviews and technical standards across the team, fostering a mentor-first environment with two-way feedback dialogue — pairing on complex pipeline work and unblocking junior engineers on async architecture patterns.
- Re-architected the AI companion's core processing pipeline from synchronous to asynchronous with a queue-based worker architecture, enabling horizontal scalability and cutting upload processing time ~4x (from ~22s to 5–10s) while improving response accuracy.
- Designed an AI-driven document intelligence workflow with automatic multi-document classification, per-document summarization, and relevance guardrails for the patient care journey.
- Built a unified patient memory system (short- and long-term context) bridging the document vault and chatbot into a single bidirectional, context-aware platform.
Andreas Anding
Last position:
AI Consultant & Digital Architect at TeamIntel
- Governed multi-agent orchestration for regulated, EU-based companies – self-hostable, compliant with the EU AI Act and GDPR („by design“), BYOM (own models/GPU).
- Two-gate governance: agent deliberation + mandatory human approval, full signed audit trail; graduated autonomy model („internal → autonomous per skill“).
- Verified knowledge graph („Company Brain“) with source evidence for every answer; own orchestration framework (Virtual Team Framework).
- Industry solutions for financial services: compliance monitoring, invoice and contract review; hands-on development with LLMs (including Anthropic/Claude), agentic workflows, RAG.
- Building the governance-focused multi-agent platform TeamIntel (see AI reference projects).
Nima Nooshi
Last position:
Co founding LLM Engineer at LLM Ventures
- Co-founded an AI venture focused on building production-grade LLM applications and agentic systems
- Designed and implemented multi-agent AI workflows for financial and trading applications
- Developed LLM-powered copilot architectures for portfolio analysis, trade management, and personalized user coaching
- Built on-device and edge-deployed inference applications, optimizing models for low latency, privacy, and resource-constrained environments
- Led system architecture decisions across model selection, orchestration, state management, and deployment
Stephan Baier
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
Antonio Enea Marraffa
Last position:
Senior PO/PM/Agile Master for AI/NLP/ML Products at Freelancer
- PO/PM for digital products such as Search, Recommendations and AI (IR/ML) related projects, Knowledge and Document Management Systems, search with LLMs, RAG and Knowledge Graph
- Agile evangelist helping people, teams and organizations work in agile ways
Thomas Rost
Last position:
Senior Manager AI and Data Science at SK Advisory
- Consulting AI and Machine Learning
- Strategy
- Project Management
- Validation
- Proof of concepts
Discover over 15,000 top freelancers
Statistics of experts using Knowledge Graph
Aggregated from the professional profiles of matched freelancers.
Experience
18 years
Position duration
2 years
Positions per freelancer
14
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Professional Services, Banking and Finance
Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
83%
Master's degree or higher
83%
Doctorate
67%
Certifications per freelancer
2
Most common languages
English, German, Arabic
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Munich 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it is
A knowledge graph connects people, products, places, documents, and events into a shared model of meaning. It helps teams keep business data consistent and searchable across systems. In Munich, companies use it for product knowledge, research, media, mobility, and enterprise search.
Typical work
- Model entities, relationships, and identifiers
- Link data from APIs, catalogs, and content systems
- Build semantic search and recommendation logic
- Support entity resolution and master data use cases
- Expose graph data for analytics and knowledge assistants
Core stack
Strong specialists know RDF, SPARQL, OWL, and property graph models. They often work with Neo4j, GraphDB, Stardog, Apache Jena, and schema design tools. They also understand data quality, ontology work, and how to keep graph structures readable for teams that must maintain them.
When to bring help
Companies bring in freelance expertise when data is scattered, relationships are hard to query, or a new graph initiative needs a clear model. That is common in regulated industries, publishing, e-commerce, and industrial software. A good expert can turn vague requirements into a graph that teams can actually use.
What strong specialists do
Strong professionals do more than load triples or create nodes. They define the right entities, choose the right model, and test how queries behave in real use. They also align domain experts, search teams, and data teams so the graph fits the business meaning, not just the source tables.
Munich collaboration
In Munich, knowledge graph work often sits close to enterprise IT, research groups, and product teams. Some projects need on-site workshops for ontology and data modeling; others run well remotely if the source systems and stakeholders are well organized. Clear English is common, and German can help in domain-heavy workshops.
Frequently asked questions
Key details about Knowledge Graph, drawn from the questions we get asked most.
A knowledge graph is used to connect business facts that live in different systems and make them queryable as one model. It is a strong fit for search, recommendations, entity resolution, data discovery, and semantic APIs. Teams also use it to give structure to content, products, people, and documents.
A knowledge graph focuses on relationships and meaning, while a relational database focuses on tables and joins. Graph models make it easier to follow connected data across many hops and to change the model as the domain evolves. Relational systems still matter for transactions and structured records, so the two often work together.
A knowledge graph can be built with either RDF or a property graph model, depending on the use case. RDF and SPARQL are often chosen for semantic interoperability, standards, and ontology-heavy work. Property graphs are often preferred for operational traversal, product features, and simpler team adoption.
A strong knowledge graph specialist usually brings data modeling, ontology design, ETL or ELT, and solid SQL or API skills. Search, Python, and data quality work are also useful. For enterprise work, experience with access control and source system integration helps a lot.
A knowledge graph project can start with a focused specialist if the scope is clear and the data sources are well understood. Larger initiatives need someone who can shape the domain model, guide stakeholder alignment, and avoid overcomplicated schemas. The key is not title or seniority alone, but proven delivery on similar graph work.
Yes, Knowledge Graph work is often well suited to remote collaboration because modeling, query design, and integration can be reviewed online. In Munich, on-site time can still help during discovery, ontology workshops, or stakeholder sessions with domain teams. Many projects use a hybrid setup.
Look for a knowledge graph expert who can explain the domain model in plain language and show how it supports real queries. Good signs include clear entity choices, consistent naming, workable ontology rules, and examples of how they handled messy source data. Ask for past graphs, query patterns, and the trade-offs they made.
A knowledge graph professional should be comfortable with RDF, SPARQL, OWL, and graph databases such as Neo4j, GraphDB, or Stardog when relevant. They should also understand linked data, schema design, and how to map source records into stable entities. If the project is semantic, ontology work matters a lot.
The average hourly rate of freelancers in Munich, Germany who have used Knowledge Graph in their recent projects is 108 €, which corresponds to a daily rate of about 860 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Knowledge Graph in their recent projects, 83% hold at least a Bachelor's degree, 83% hold at least a Master's degree, and 67% hold a doctorate.
On average, freelancers in Munich, Germany who have used Knowledge Graph in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers in Munich, Germany who have used Knowledge Graph in their recent projects are English (100%), German (88%), and Arabic (13%).
The most common industries among freelancers in Munich, Germany who have used Knowledge Graph in their recent projects are Information Technology (100%), Professional Services (88%), and Banking and Finance (63%).
The most common business areas among freelancers in Munich, Germany who have used Knowledge Graph in their recent projects are Information Technology (100%), Product Development (100%), and Business Intelligence (75%).
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.
Countries:
Berlin Hamburg 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
