Automatic Ontology Construction Using LLMs as an External Layer of Memory, Verification, and Planning for Hybrid Intelligent Systems

• URL: http://arxiv.org/abs/2604.20795v1
• Date: Wed, 22 Apr 2026 17:19:43 GMT
• Title: Automatic Ontology Construction Using LLMs as an External Layer of Memory, Verification, and Planning for Hybrid Intelligent Systems
• Abstract summary: This paper presents a hybrid architecture for intelligent systems in which large language models (LLMs) are extended with an external ontological memory layer.<n>The proposed approach constructs and maintains a structured knowledge graph using RDF/OWL representations, enabling persistent, verifiable, and semantically grounded reasoning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a hybrid architecture for intelligent systems in which large language models (LLMs) are extended with an external ontological memory layer. Instead of relying solely on parametric knowledge and vector-based retrieval (RAG), the proposed approach constructs and maintains a structured knowledge graph using RDF/OWL representations, enabling persistent, verifiable, and semantically grounded reasoning. The core contribution is an automated pipeline for ontology construction from heterogeneous data sources, including documents, APIs, and dialogue logs. The system performs entity recognition, relation extraction, normalization, and triple generation, followed by validation using SHACL and OWL constraints, and continuous graph updates. During inference, LLMs operate over a combined context that integrates vector-based retrieval with graph-based reasoning and external tool interaction. Experimental observations on planning tasks, including the Tower of Hanoi benchmark, indicate that ontology augmentation improves performance in multi-step reasoning scenarios compared to baseline LLM systems. In addition, the ontology layer enables formal validation of generated outputs, transforming the system into a generation-verification-correction pipeline. The proposed architecture addresses key limitations of current LLM-based systems, including lack of long-term memory, weak structural understanding, and limited reasoning capabilities. It provides a foundation for building agent-based systems, robotics applications, and enterprise AI solutions that require persistent knowledge, explainability, and reliable decision-making.

Related papers

• When Evidence Shapes Collaboration: Knowledge-Conditioned Topology Generation for Multi-Agent Systems [3.276908575835893]
  We propose K-GAT (Knowledge-Guided Agent Topology Generator), a neuro-symbolic framework that formulates collaboration topology design as a knowledge-conditioned structure learning problem.<n>Experiments on knowledge-intensive benchmarks demonstrate K-GAT's efficiency and effectiveness.
  arXiv  Detail & Related papers  (2026-08-28T06:52:50Z)
• Constructing Dynamic Master Logic Models as Knowledge Graphs for Complex System Diagnostics Using Retrieval-Augmented Large Language Models [0.11666234644810893]
  Dynamic Master Logic (DML) provides a hierarchical framework for representing system behavior by linking functional objectives to underlying structural elements.<n>This study presents a framework for automated construction of DML models from system descriptions as Knowledge Graphs (KG-DML)<n>The results show that KG-DML can transform technical documentation into executable functional models for diagnostic and reliability analysis.
  arXiv  Detail & Related papers  (2026-08-12T17:50:39Z)
• DCD: Domain-Oriented Design for Controlled Retrieval-Augmented Generation [0.0]
  We introduce DCD (Domain-Collection-Document), a domain-oriented design to structure and control query processing in RAG systems without modifying the underlying language model.<n>The proposed approach relies on a hierarchical decomposition of the information space and multi-stage routing based on structured model outputs.<n>We describe the DCD architecture and workflow and discuss results on synthetic evaluation dataset, highlighting their impact on robustness, factual accuracy, and answer relevance in applied RAG scenarios.
  arXiv  Detail & Related papers  (2026-04-08T20:47:51Z)
• Understanding by Reconstruction: Reversing the Software Development Process for LLM Pretraining [66.89012795621349]
  Large Language Models (LLMs) often struggle with the deep, long-horizon reasoning required for complex software engineering.<n>We propose a novel paradigm: understanding via reconstruction.<n>We introduce a framework that synthesizes latent agentic trajectories using a multi-agent simulation.
  arXiv  Detail & Related papers  (2026-03-11T09:23:20Z)
• Architecting AgentOS: From Token-Level Context to Emergent System-Level Intelligence [13.062618208633483]
  This paper proposes AgentOS, a holistic conceptual framework that redefines the Large Language Models as a "Reasoning Kernel" governed by structured operating system logic.<n>By mapping classical OS abstractions such as memory paging interrupt handling and process scheduling onto LLM native constructs, this review provides a rigorous roadmap for architecting resilient scalable and self-evolving cognitive environments.
  arXiv  Detail & Related papers  (2026-02-24T14:12:21Z)
• Closed-Loop LLM Discovery of Non-Standard Channel Priors in Vision Models [48.83701310501069]
  Large Language Models (LLMs) offer a transformative approach to Neural Architecture Search (NAS)<n>We formulate the search as a sequence of conditional code generation tasks, where an LLM refines architectural specifications based on performance telemetry.<n>We generate a vast corpus of valid, shape-consistent architectures via Abstract Syntax Tree (AST) mutations.<n> Experimental results on CIFAR-100 validate the efficacy of this approach, demonstrating that the model yields statistically significant improvements in accuracy.
  arXiv  Detail & Related papers  (2026-01-13T13:00:30Z)
• Event Extraction in Large Language Model [99.94321497574805]
  We argue that EE should be viewed as a system component that provides a cognitive scaffold for LLM centered solutions.<n>This survey covers EE in text and multimodal settings, organizing tasks and taxonomy, tracing method evolution from rule based and neural models to instruction driven and generative frameworks.
  arXiv  Detail & Related papers  (2025-12-22T16:22:14Z)
• Context-Aware Visual Prompting: Automating Geospatial Web Dashboards with Large Language Models and Agent Self-Validation for Decision Support [1.506501956463029]
  Development of web-based dashboards for risk analysis and decision making often challenged by difficulty in big, multidimensional data.<n>We introduce a generative AI framework that automates the creation of interactive geospatial dashboards from user-defined inputs.
  arXiv  Detail & Related papers  (2025-10-10T10:58:15Z)
• GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models [59.72897499248909]
  We propose a novel graph retriever trained end-to-end with Large Language Models (LLMs)<n>Within the extracted subgraph, structural knowledge and semantic features are encoded via soft tokens and the verbalized graph, respectively, which are infused into the LLM together.<n>Our approach consistently achieves state-of-the-art performance, validating the strength of joint graph-LLM optimization for complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-09-20T02:38:00Z)
• Data Dependency-Aware Code Generation from Enhanced UML Sequence Diagrams [54.528185120850274]
  We propose a novel step-by-step code generation framework named API2Dep.<n>First, we introduce an enhanced Unified Modeling Language (UML) API diagram tailored for service-oriented architectures.<n>Second, recognizing the critical role of data flow, we introduce a dedicated data dependency inference task.
  arXiv  Detail & Related papers  (2025-08-05T12:28:23Z)
• Discrete Tokenization for Multimodal LLMs: A Comprehensive Survey [69.45421620616486]
  This work presents the first structured taxonomy and analysis of discrete tokenization methods designed for large language models (LLMs)<n>We categorize 8 representative VQ variants that span classical and modern paradigms and analyze their algorithmic principles, training dynamics, and integration challenges with LLM pipelines.<n>We identify key challenges including codebook collapse, unstable gradient estimation, and modality-specific encoding constraints.
  arXiv  Detail & Related papers  (2025-07-21T10:52:14Z)
• Leveraging Large Language Model for Intelligent Log Processing and Autonomous Debugging in Cloud AI Platforms [1.819979627431298]
  This paper proposes an intelligent log processing and automatic debug framework based on Large Language Model (LLM), named Intelligent Debugger (LLM-ID)<n> Experiments on the cloud platform log dataset show that LLM-ID improves the fault location accuracy by 16.2%, which is significantly better than the current mainstream methods.
  arXiv  Detail & Related papers  (2025-06-22T04:58:37Z)
• PersonalAI: A Systematic Comparison of Knowledge Graph Storage and Retrieval Approaches for Personalized LLM agents [15.524189150821147]
  Large language models (LLMs) combined with Retrieval-Augmented Generation (RAG) fail to scale in complex, long-term interactions.<n>We propose a flexible external memory framework based on knowledge graphs, automatically constructed and updated by the LLM itself.<n>Building upon the AriGraph architecture, we introduce a novel hybrid graph design that supports both standard edges and two types of hyperedges.<n>We evaluate our system on three benchmarks-TriviaQA, HotpotQA, and DiaASQ-demonstrating that different memory and retrieval configurations yield optimal performance depending on the task.
  arXiv  Detail & Related papers  (2025-06-20T13:52:15Z)
• GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation [108.2008975785364]
  Graph Inspired Veracity Extrapolation (GIVE) is a novel reasoning method that merges parametric and non-parametric memories to improve accurate reasoning with minimal external input.<n>GIVE guides the LLM agent to select the most pertinent expert data (observe), engage in query-specific divergent thinking (reflect), and then synthesize this information to produce the final output (speak)
  arXiv  Detail & Related papers  (2024-10-11T03:05:06Z)

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.