LLM-Supported Formal Knowledge Representation for Enhancing Control Engineering Content with an Interactive Semantic Layer

• URL: http://arxiv.org/abs/2511.02759v1
• Date: Tue, 04 Nov 2025 17:36:57 GMT
• Title: LLM-Supported Formal Knowledge Representation for Enhancing Control Engineering Content with an Interactive Semantic Layer
• Authors: Julius Fiedler, Carsten Knoll, Klaus Röbenack,
• Abstract summary: This paper briefly describes an LLM-supported method for semi-automated generation of formal knowledge representations.<n>We demonstrate how language models can assist in transforming natural-language descriptions and mathematical definitions into a formalized knowledge graph.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The rapid growth of research output in control engineering calls for new approaches to structure and formalize domain knowledge. This paper briefly describes an LLM-supported method for semi-automated generation of formal knowledge representations that combine human readability with machine interpretability and increased expressiveness. Based on the Imperative Representation of Knowledge (PyIRK) framework, we demonstrate how language models can assist in transforming natural-language descriptions and mathematical definitions (available as LaTeX source code) into a formalized knowledge graph. As a first application we present the generation of an ``interactive semantic layer'' to enhance the source documents in order to facilitate knowledge transfer. From our perspective this contributes to the vision of easily accessible, collaborative, and verifiable knowledge bases for the control engineering domain.

Related papers

• The Empowerment of Science of Science by Large Language Models: New Tools and Methods [7.392576669260369]
  Large language models (LLMs) have exhibited exceptional capabilities in natural language understanding and generation, image recognition, and multimodal tasks.<n>This manuscript conducts a comprehensive review of the core technologies that support LLMs from a user standpoint, including prompt engineering, knowledge-enhanced retrieval augmented generation, fine tuning, pretraining, and tool learning.
  arXiv  Detail & Related papers  (2025-11-19T11:57:22Z)
• Prompting is not Enough: Exploring Knowledge Integration and Controllable Generation [89.65955788873532]
  Open-domain question answering (OpenQA) represents a cornerstone in natural language processing (NLP)<n>We propose a novel framework named GenKI, which aims to improve the OpenQA performance by exploring Knowledge Integration and controllable Generation.
  arXiv  Detail & Related papers  (2025-05-26T08:18:33Z)
• Agentic Publications: An LLM-Driven Framework for Interactive Scientific Publishing, Supplementing Traditional Papers with AI-Powered Knowledge Systems [0.0]
  "Agentic Publications" is a novel framework for transforming papers into interactive knowledge systems.<n>Our architecture integrates structured data with unstructured content through retrieval-augmented generation and multi-agent verification.<n>Key features include continuous knowledge updates, automatic integration of new findings, and customizable detail levels.
  arXiv  Detail & Related papers  (2025-05-19T15:28:10Z)
• Semantic Mastery: Enhancing LLMs with Advanced Natural Language Understanding [0.0]
  The paper discusses state-of-the-art methodologies that advance large language models (LLMs) with more advanced NLU techniques.<n>We analyze the use of structured knowledge graphs, retrieval-augmented generation (RAG), and fine-tuning strategies that match models with human-level understanding.
  arXiv  Detail & Related papers  (2025-04-01T04:12:04Z)
• Leveraging Distillation Techniques for Document Understanding: A Case Study with FLAN-T5 [0.0]
  We present a novel approach wherein we distill document understanding knowledge from the proprietary LLM ChatGPT into FLAN-T5. Our findings underscore the potential of distillation techniques in facilitating the deployment of sophisticated language models in real-world scenarios.
  arXiv  Detail & Related papers  (2024-09-17T15:37:56Z)
• Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge [76.45868419402265]
  multimodal large language models (MLLMs) have made significant strides by training on vast high-quality image-text datasets. However, the inherent difficulty in explicitly conveying fine-grained or spatially dense information in text, such as masks, poses a challenge for MLLMs. This paper proposes a new visual prompt approach to integrate fine-grained external knowledge, gleaned from specialized vision models, into MLLMs.
  arXiv  Detail & Related papers  (2024-07-05T17:43:30Z)
• FecTek: Enhancing Term Weight in Lexicon-Based Retrieval with Feature Context and Term-level Knowledge [54.61068946420894]
  We introduce an innovative method by introducing FEature Context and TErm-level Knowledge modules. To effectively enrich the feature context representations of term weight, the Feature Context Module (FCM) is introduced. We also develop a term-level knowledge guidance module (TKGM) for effectively utilizing term-level knowledge to intelligently guide the modeling process of term weight.
  arXiv  Detail & Related papers  (2024-04-18T12:58:36Z)
• Large Language Models for Scientific Information Extraction: An Empirical Study for Virology [0.0]
  We champion the use of structured and semantic content representation of discourse-based scholarly communication. Inspired by tools like Wikipedia infoboxes or structured Amazon product descriptions, we develop an automated approach to produce structured scholarly contribution summaries. Our results show that finetuned FLAN-T5 with 1000x fewer parameters than the state-of-the-art GPT-davinci is competitive for the task.
  arXiv  Detail & Related papers  (2024-01-18T15:04:55Z)
• Automating Knowledge Acquisition for Content-Centric Cognitive Agents Using LLMs [0.0]
  The paper describes a system that uses large language model (LLM) technology to support the automatic learning of new entries in an intelligent agent's semantic lexicon. The process is bootstrapped by an existing non-toy lexicon and a natural language generator that converts formal, ontologically-grounded representations of meaning into natural language sentences.
  arXiv  Detail & Related papers  (2023-12-27T02:31:51Z)
• Empowering Language Models with Knowledge Graph Reasoning for Question Answering [117.79170629640525]
  We propose knOwledge REasOning empowered Language Model (OREO-LM) OREO-LM consists of a novel Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs. We show significant performance gain, achieving state-of-art results in the Closed-Book setting.
  arXiv  Detail & Related papers  (2022-11-15T18:26:26Z)
• JAKET: Joint Pre-training of Knowledge Graph and Language Understanding [73.43768772121985]
  We propose a novel joint pre-training framework, JAKET, to model both the knowledge graph and language. The knowledge module and language module provide essential information to mutually assist each other. Our design enables the pre-trained model to easily adapt to unseen knowledge graphs in new domains.
  arXiv  Detail & Related papers  (2020-10-02T05:53:36Z)
• CoLAKE: Contextualized Language and Knowledge Embedding [81.90416952762803]
  We propose the Contextualized Language and Knowledge Embedding (CoLAKE) CoLAKE jointly learns contextualized representation for both language and knowledge with the extended objective. We conduct experiments on knowledge-driven tasks, knowledge probing tasks, and language understanding tasks.
  arXiv  Detail & Related papers  (2020-10-01T11:39:32Z)

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.