OntoRAG: Enhancing Question-Answering through Automated Ontology Derivation from Unstructured Knowledge Bases

• URL: http://arxiv.org/abs/2506.00664v1
• Date: Sat, 31 May 2025 18:33:39 GMT
• Title: OntoRAG: Enhancing Question-Answering through Automated Ontology Derivation from Unstructured Knowledge Bases
• Authors: Yash Tiwari, Owais Ahmad Lone, Mayukha Pal,
• Abstract summary: This paper introduces OntoRAG, an automated pipeline designed to derive from unstructured knowledge bases.<n>OntoRAG integrates advanced techniques, including web scraping, PDF parsing, hybrid chunking, information extraction, knowledge graph construction, and ontology creation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Ontologies are pivotal for structuring knowledge bases to enhance question answering (QA) systems powered by Large Language Models (LLMs). However, traditional ontology creation relies on manual efforts by domain experts, a process that is time intensive, error prone, and impractical for large, dynamic knowledge domains. This paper introduces OntoRAG, an automated pipeline designed to derive ontologies from unstructured knowledge bases, with a focus on electrical relay documents. OntoRAG integrates advanced techniques, including web scraping, PDF parsing, hybrid chunking, information extraction, knowledge graph construction, and ontology creation, to transform unstructured data into a queryable ontology. By leveraging LLMs and graph based methods, OntoRAG enhances global sensemaking capabilities, outperforming conventional Retrieval Augmented Generation (RAG) and GraphRAG approaches in comprehensiveness and diversity. Experimental results demonstrate OntoRAGs effectiveness, achieving a comprehensiveness win rate of 85% against vector RAG and 75% against GraphRAGs best configuration. This work addresses the critical challenge of automating ontology creation, advancing the vision of the semantic web.

Related papers

• DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation [4.113142669523488]
  Domain-specific QA systems require generative fluency but high factual accuracy grounded in structured expert knowledge.<n>We propose DO-RAG, a scalable and customizable hybrid QA framework that integrates multi-level knowledge graph construction with semantic vector retrieval.
  arXiv  Detail & Related papers  (2025-05-17T06:40:17Z)
• RAKG:Document-level Retrieval Augmented Knowledge Graph Construction [10.013667560362565]
  This paper focuses on the task of automatic document-level knowledge graph construction.<n>It proposes the Document-level Retrieval Augmented Knowledge Graph Construction (RAKG) framework.
  arXiv  Detail & Related papers  (2025-04-14T02:47:23Z)
• Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation [52.8352968531863]
  Large language models (LLMs) have made significant progress in general-purpose natural language processing tasks.<n>This paper presents a novel framework that combines knowledge graph (KG) and retrieval-augmented generation (RAG) techniques to enhance LLM performance in the telecom domain.
  arXiv  Detail & Related papers  (2025-03-31T15:58:08Z)
• Retrieval-Augmented Generation with Hierarchical Knowledge [38.500133410610495]
  Graph-based Retrieval-Augmented Generation (RAG) methods have significantly enhanced the performance of large language models (LLMs) in domain-specific tasks.<n>Existing RAG methods do not adequately utilize the naturally inherent hierarchical knowledge in human cognition.<n>We introduce a new RAG approach, called HiRAG, which utilizes hierarchical knowledge to enhance the semantic understanding and structure capturing capabilities of RAG systems.
  arXiv  Detail & Related papers  (2025-03-13T08:22:31Z)
• ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation [16.204046295248546]
  Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs)<n>We introduce a novel graph-based RAG approach, called Attributed Community-based Hierarchical RAG (ArchRAG)<n>We build a novel hierarchical index structure for the attributed communities and develop an effective online retrieval method.<n>ArchRAG has been successfully applied to domain knowledge QA in Huawei Cloud Computing.
  arXiv  Detail & Related papers  (2025-02-14T03:28:36Z)
• Graph Foundation Models for Recommendation: A Comprehensive Survey [55.70529188101446]
  Large language models (LLMs) are designed to process and comprehend natural language, making both approaches highly effective and widely adopted.<n>Recent research has focused on graph foundation models (GFMs)<n>GFMs integrate the strengths of GNNs and LLMs to model complex RS problems more efficiently by leveraging the graph-based structure of user-item relationships alongside textual understanding.
  arXiv  Detail & Related papers  (2025-02-12T12:13:51Z)
• GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation [84.41557981816077]
  We introduce GFM-RAG, a novel graph foundation model (GFM) for retrieval augmented generation.<n>GFM-RAG is powered by an innovative graph neural network that reasons over graph structure to capture complex query-knowledge relationships.<n>It achieves state-of-the-art performance while maintaining efficiency and alignment with neural scaling laws.
  arXiv  Detail & Related papers  (2025-02-03T07:04:29Z)
• FastRAG: Retrieval Augmented Generation for Semi-structured Data [1.5566524830295307]
  This paper introduces FastRAG, a novel RAG approach designed for semi-structured data. FastRAG employs schema learning and script learning to extract and structure data without needing to submit entire data sources to an LLM. It integrates text search with knowledge graph querying to improve accuracy in retrieving context-rich information.
  arXiv  Detail & Related papers  (2024-11-21T01:00:25Z)
• StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization [94.31508613367296]
  Retrieval-augmented generation (RAG) is a key means to effectively enhance large language models (LLMs) We propose StructRAG, which can identify the optimal structure type for the task at hand, reconstruct original documents into this structured format, and infer answers based on the resulting structure. Experiments show that StructRAG achieves state-of-the-art performance, particularly excelling in challenging scenarios.
  arXiv  Detail & Related papers  (2024-10-11T13:52:44Z)
• 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.<n>At its core, Embodied-RAG's memory is structured as a semantic forest, storing language descriptions at varying levels of detail.<n>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)
• Graph Retrieval-Augmented Generation: A Survey [28.979898837538958]
  Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. This paper provides the first comprehensive overview of GraphRAG methodologies. We formalize the GraphRAG workflow, encompassing Graph-Based Indexing, Graph-Guided Retrieval, and Graph-Enhanced Generation.
  arXiv  Detail & Related papers  (2024-08-15T12:20:24Z)
• LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model [96.889634747943]
  Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential. We propose a novel structure-aware GLM, fully unleashing the power of syntactic knowledge for UIE. Over 12 IE benchmarks across 7 tasks our system shows significant improvements over the baseline UIE system.
  arXiv  Detail & Related papers  (2023-04-13T04:01:14Z)

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.