@Misc{NLQ-GPT-SOML-GraphQL-2023,
  author       = {Vladimir Alexiev},
  title        = {{Natural Language Querying with GPT, SOML and GraphQL}},
  howpublished = {Ontotext Last Friday Webinar},
  month        = may,
  year         = 2023,
  url_video    = {https://drive.google.com/file/d/1TOHrtlleOAkv4oZYhlAWa22mUqtvsV7o/view},
  abstract     = {Clients want to talk to their KG, i.e. ask questions about the schema and data in natural language. LLMs like GPT and LLAMA have opened a revolution in this regard. Currently Ontotext is exploring 8 themes with LLMs.
NLQ can be accomplished either by:
- Providing data from GraphDB to the LLM, or
- Presenting a schema to the LLM and asking it to generate queries.
In this talk we explore query generation.
- SPARQL queries are complex, so even for known schemas (eg Wikidata, DBpedia), GPT has trouble generating good queries, see \Shared drives\KGS\AI-GPT\GPT-SPARQL. Furthermore, RDF schemas (OWL and SHACL) are complex. But I'm sure there will be fast progress in SPARQL generation, see LlamaIndex advances in GDB-8329
- GraphQL queries are regular and much simpler, and SOML is a simpler schema language (from which the Ontotext Platform generates GraphQL schema, queries and SHACL shapes). In this talk I'll show how GPT4 can answer questions about a schema, and generate GraphQL to answer questions about data.}
}

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