Summary of Leveraging Llm For Automated Ontology Extraction and Knowledge Graph Generation, by Mohammad Sadeq Abolhasani et al.
Leveraging LLM for Automated Ontology Extraction and Knowledge Graph Generation
by Mohammad Sadeq Abolhasani, Rong Pan
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel pipeline for ontology extraction and Knowledge Graph (KG) generation is introduced in this research paper, dubbed OntoKGen. The approach leverages Large Language Models (LLMs) through an interactive user interface guided by the adaptive iterative Chain of Thought (CoT) algorithm to ensure that the extracted ontology and subsequent KG generation align with user-specific requirements. The pipeline recommends an ontology grounded in best practices, minimizing user effort while providing valuable insights that may have been overlooked. The generated KG serves as a robust foundation for future integration into Retrieval Augmented Generation (RAG) systems, offering enhanced capabilities for developing domain-specific intelligent applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OntoKGen is a new tool that helps people extract important information and create knowledge graphs from large technical documents. It uses big language models and an interactive interface to make sure the extracted information matches what users want. The tool suggests an ontology based on best practices, making it easier for users to find valuable insights they might have missed. This technology can be used in various applications that require intelligent decision-making. |
Keywords
» Artificial intelligence » Knowledge graph » Rag » Retrieval augmented generation
