A graph of registered pesticide products, active substances, crops, target pests, application windows and dosage — built from official register data and product labels, with a working question-answering demo.
Practical Neo4j and graph-AI work — ontology design, multi-source ingestion, and retrieval that holds up in production. By Konrad Kaliciński.
I build and ship knowledge graphs on Neo4j — ontology design, multi-source ingestion, GraphRAG, and the unglamorous plumbing that makes a graph survive contact with real data.
I write about this work every week: what is working, what is not, and what most teams get wrong on their first knowledge-graph project.
— Knowledge graph practitioner and writer: building, teaching, and consulting on graph-AI projects.
A graph of registered pesticide products, active substances, crops, target pests, application windows and dosage — built from official register data and product labels, with a working question-answering demo.
I take on consulting engagements, knowledge-graph reviews, and collaboration on graph projects. Send me a few lines about your graph on LinkedIn — I reply to every serious message.
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