Leveraging LLM for Automated Ontology Extraction and Knowledge Graph Generation

• URL: http://arxiv.org/abs/2412.00608v3
• Date: Tue, 10 Dec 2024 04:28:36 GMT
• Title: Leveraging LLM for Automated Ontology Extraction and Knowledge Graph Generation
• Authors: Mohammad Sadeq Abolhasani, Rong Pan,
• Abstract summary: OntoKGen is a genuine pipeline for ontology extraction and Knowledge Graph generation. OntoKGen enables seamless integration into schemeless, non-relational databases like Neo4j.
• Score: 3.2513035377783717
• License:
• Abstract: Extracting relevant and structured knowledge from large, complex technical documents within the Reliability and Maintainability (RAM) domain is labor-intensive and prone to errors. Our work addresses this challenge by presenting OntoKGen, a genuine pipeline for ontology extraction and Knowledge Graph (KG) generation. OntoKGen leverages Large Language Models (LLMs) through an interactive user interface guided by our adaptive iterative Chain of Thought (CoT) algorithm to ensure that the ontology extraction process and, thus, KG generation align with user-specific requirements. Although KG generation follows a clear, structured path based on the confirmed ontology, there is no universally correct ontology as it is inherently based on the user's preferences. OntoKGen recommends an ontology grounded in best practices, minimizing user effort and providing valuable insights that may have been overlooked, all while giving the user complete control over the final ontology. Having generated the KG based on the confirmed ontology, OntoKGen enables seamless integration into schemeless, non-relational databases like Neo4j. This integration allows for flexible storage and retrieval of knowledge from diverse, unstructured sources, facilitating advanced querying, analysis, and decision-making. Moreover, 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.

Related papers

• Augmented Knowledge Graph Querying leveraging LLMs [2.5311562666866494]
  We introduce SparqLLM, a framework that enhances the querying of Knowledge Graphs (KGs) SparqLLM executes the Extract, Transform, and Load (ETL) pipeline to construct KGs from raw data. It also features a natural language interface powered by Large Language Models (LLMs) to enable automatic SPARQL query generation.
  arXiv  Detail & Related papers  (2025-02-03T12:18:39Z)
• FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs [17.477161619378332]
  We propose a novel flexible modular KG-RAG framework, termed FRAG, which synergizes the advantages of both approaches. By using the query text instead of the Knowledge Graph, FRAG improves retrieval quality while maintaining flexibility.
  arXiv  Detail & Related papers  (2025-01-17T05:19:14Z)
• Ontology-grounded Automatic Knowledge Graph Construction by LLM under Wikidata schema [60.42231674887294]
  We propose an ontology-grounded approach to Knowledge Graph (KG) construction using Large Language Models (LLMs) on a knowledge base. We ground generation of KG with the authored ontology based on extracted relations to ensure consistency and interpretability. Our work presents a promising direction for scalable KG construction pipeline with minimal human intervention, that yields high quality and human-interpretable KGs.
  arXiv  Detail & Related papers  (2024-12-30T13:36:05Z)
• Unveiling User Preferences: A Knowledge Graph and LLM-Driven Approach for Conversational Recommendation [55.5687800992432]
  We propose a plug-and-play framework that synergizes Large Language Models (LLMs) and Knowledge Graphs (KGs) to unveil user preferences. This enables the LLM to transform KG entities into concise natural language descriptions, allowing them to comprehend domain-specific knowledge.
  arXiv  Detail & Related papers  (2024-11-16T11:47:21Z)
• Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains [66.55612528039894]
  Knowledge Graphs (KGs) can serve as reliable knowledge sources for question answering (QA) We present DoG (Decoding on Graphs), a novel framework that facilitates a deep synergy between LLMs and KGs. Experiments across various KGQA tasks with different background KGs demonstrate that DoG achieves superior and robust performance.
  arXiv  Detail & Related papers  (2024-10-24T04:01:40Z)
• Distill-SynthKG: Distilling Knowledge Graph Synthesis Workflow for Improved Coverage and Efficiency [59.6772484292295]
  Knowledge graphs (KGs) generated by large language models (LLMs) are increasingly valuable for Retrieval-Augmented Generation (RAG) applications. Existing KG extraction methods rely on prompt-based approaches, which are inefficient for processing large-scale corpora. We propose SynthKG, a multi-step, document-level synthesis KG workflow based on LLMs. We also design a novel graph-based retrieval framework for RAG.
  arXiv  Detail & Related papers  (2024-10-22T00:47:54Z)
• Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation [69.01029651113386]
  Embodied-RAG is a framework that enhances the model of an embodied agent with a non-parametric memory system. At its core, Embodied-RAG's memory is structured as a semantic forest, storing language descriptions at varying levels of detail. We demonstrate that Embodied-RAG effectively bridges RAG to the robotics domain, successfully handling over 250 explanation and navigation queries.
  arXiv  Detail & Related papers  (2024-09-26T21:44:11Z)
• Leveraging Large Language Models for Semantic Query Processing in a Scholarly Knowledge Graph [1.7418328181959968]
  The proposed research aims to develop an innovative semantic query processing system. It enables users to obtain comprehensive information about research works produced by Computer Science (CS) researchers at the Australian National University.
  arXiv  Detail & Related papers  (2024-05-24T09:19:45Z)
• An Open-Source Knowledge Graph Ecosystem for the Life Sciences [5.665519167428707]
  PheKnowLator is a semantic ecosystem for automating the construction of ontologically grounded knowledge graphs. The ecosystem includes KG construction resources, analysis tools, and benchmarks. PheKnowLator enables fully customizable KGs without compromising performance or usability.
  arXiv  Detail & Related papers  (2023-07-11T18:55:09Z)
• Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding [18.83458273005337]
  Knowledge graphs (KG) are used in a wide range of applications. The automation of KG generation is very desired due to the data volume and variety in industries.
  arXiv  Detail & Related papers  (2022-09-22T14:59:13Z)
• BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models [65.51390418485207]
  We propose a new approach of harvesting massive KGs of arbitrary relations from pretrained LMs. With minimal input of a relation definition, the approach efficiently searches in the vast entity pair space to extract diverse accurate knowledge. We deploy the approach to harvest KGs of over 400 new relations from different LMs.
  arXiv  Detail & Related papers  (2022-06-28T19:46:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.