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Semiautomated feature extraction

As explained in Knowledge Graphs and LLMs in Action

Semiautomated feature engineering combines automated feature generation with human control or domain-informed refinement.

Semiautomated feature extraction builds numerical representations from graph structure while retaining human oversight. It balances manual feature engineering’s interpretability with automated generation’s efficiency, accepting less interpretability as automation increases. section 10.0

A practical semiautomated workflow includes:

  • Generate interpretable graph features automatically, as ReFeX does. section 10.4
  • Use domain expertise to guide metric selection and validate results. section 10.4
  • Prune correlated or irrelevant features for the specific machine-learning task. section 10.4

How Knowledge Graphs and LLMs in Action covers it

How deep it goes
Defines ReFeX and validation considerations but does not descend into its implementation details.
Examples
Uses node degree, clustering coefficients, centrality measures, and ReFeX as graph-feature examples.
What it assumes
Assumes graph structure, graph properties, machine-learning tasks, and domain expertise for feature selection.
What you can do after
Apply automated feature generation while validating, pruning, and retaining domain-informed control.
How it connects
Connects manual feature engineering, representation learning, graph machine learning, domain expertise, and feature selection.
Cover of Knowledge Graphs and LLMs in Action
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