Enhance output nodes with OpenAlex-inspired topic attributes

[FrancisTembo]

Problem Statement
OpenAlex provides a robust system of topics and labels that accurately describe research outputs. By adopting a similar framework—as detailed in this system overview—we can enhance our research index. This approach will enable us to categorise and discover outputs more effectively based on key attributes.

Proposed Solution
Investigate the use of large language models (LLMs) to process database nodes and associated metadata, thereby generating structured metadata fields. These enriched metadata fields can be integrated through a dedicated service (e.g., an Azure Function) that periodically runs to update and enhance the nodes in our database.

References

1. LangChain Structured Outputs
2. Azure Functions: Create Your First Function Using the Azure Developer CLI
3. OpenAlex Topic Classification Whitepaper
4. OpenAlex_topic_mapping_table