Earlier today, Kurt Cagle published an insightful article titled 'RAG, Context and Knowledge Graphs,' which included prompts for generating queries using ChatGPT. Reading the post prompted me to test his initial prompt across various chatbot interfaces.
Why is this important?
SPARQL is crucial aspect of a Semantic Web vision, where hyperlinks represent entities and their relationships. As a query language, it harnesses these hyperlinks for web-scale queries, simplifying data lookup and reducing LLM load by allowing humans or user agents to perform query execution via hyperlink lookup.
Prompt Processing Challenge Comparisons
Prompt:
Using the DBpedia endpoint, list all movies produced by Ridley Scott and encode the sparql query in a clickable link.
Tabulated Results
Results Page Links
ChatGPT via OpenLink Personal Assistant (OPAL) -- with SPASQL execution module enabled
Conclusion
Query generation, a significant feature of many Large Language Models (LLMs), represents a facet of code generation. Evaluating these models through direct testing reveals nuanced differences. Nevertheless, their capacity for generating code substantially enhances productivity for both developers and non-developers alike.