AutoKG: Efficient Automated Knowledge Graph Generation for Language
  Models
Bohan Chen Andrea L. Bertozzi
Traditional methods of linking large language models (LLMs) to knowledge
bases via the semantic similarity search often fall short of capturing complex
relational dynamics. To address these limitations, we introduce AutoKG, a
lightweight and efficient approach for automated knowledge graph (KG)
construction. For a given knowledge base consisting of text blocks, AutoKG
first extracts keywords using a LLM and then evaluates the relationship weight
between each pair of keywords using graph Laplace learning. We employ a hybrid
search scheme combining vector similarity and graph-based associations to
enrich LLM responses. Preliminary experiments demonstrate that AutoKG offers a
more comprehensive and interconnected knowledge retrieval mechanism compared to
the semantic similarity search, thereby enhancing the capabilities of LLMs in
generating more insightful and relevant outputs.