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Extracting a knowledge graph from Wikipedia articles on Geometry using LLMs.

Have a look at the notebooks:

  • manually_annotate_polygon_wikipedia_article.ipynb
  • extract_kg.ipynb

TODO:

  1. calculate metrics better, accounting for equivalences in the graph, e.g. square -> 4-gon -> polygon is almost as good as square <- polygon -> 4-gon
  2. add annotations for same-as relationship, e.g. 3-gon is same as triangle
  3. use pydantic and tools API to format ChatGPT output in a JSON
  4. take a long-context model like Claude instead of atomic chunks, compare the results
  5. Try extracting all terms as NER first, then do $N^2$ calls to check if connections exist. Compare the connection calls with and without context chunk.