After all these years I am still amazed at what we can do with our #UniProtKB #SPARQL endpoint. A database query with a result that is almost a terrabyte in size ;) over the open internet.

I've published a detailed article about #ChatGPT hallucination minimization using predefined #SPARQL-based query templates scoped to Linked Open Data (LOD) Cloud Knowledge Graphs such as #UniProtKB, #RHEA, #chEBI, and #OMIM.

Read: https://www.linkedin.com/pulse/reducing-hallucinations-chatgpt-using-predefined-query-idehen-4qxje/ .

#HowTo #UseCase #VirtuosoRDBMS #SemanticWeb #LinkedData #KnowledgeGraph #AI #GenAI

Reducing Hallucinations in ChatGPT using Predefined Query Templates scoped to Knowledge Graphs

In the fast-paced world of artificial intelligence, chatbots using language model strategies are becoming more common. However, these chatbots sometimes generate responses based on incorrect or fabricated information, a problem often referred to as “hallucinations.

I work on #UniProtKB, and I and my colleagues would like to know how useful that is. If you have ever used UniProt could you please fill out https://ec.europa.eu/eusurvey/runner/use_value_assessment_of_UniProt and boosts will be appreciated.