Ontology Generation using Large Language Models

• URL: http://arxiv.org/abs/2503.05388v1
• Date: Fri, 07 Mar 2025 13:03:28 GMT
• Title: Ontology Generation using Large Language Models
• Authors: Anna Sofia Lippolis, Mohammad Javad Saeedizade, Robin Keskisärkkä, Sara Zuppiroli, Miguel Ceriani, Aldo Gangemi, Eva Blomqvist, Andrea Giovanni Nuzzolese,
• Abstract summary: We present and evaluate two new prompting techniques for automated ontology development: Memoryless CQbyCQ and Ontogenia.<n>Trials show that OpenAI o1-preview with Ontogenia produces of sufficient quality to meet the requirements of engineers.
• Score: 1.0037949839020768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ontology engineering process is complex, time-consuming, and error-prone, even for experienced ontology engineers. In this work, we investigate the potential of Large Language Models (LLMs) to provide effective OWL ontology drafts directly from ontological requirements described using user stories and competency questions. Our main contribution is the presentation and evaluation of two new prompting techniques for automated ontology development: Memoryless CQbyCQ and Ontogenia. We also emphasize the importance of three structural criteria for ontology assessment, alongside expert qualitative evaluation, highlighting the need for a multi-dimensional evaluation in order to capture the quality and usability of the generated ontologies. Our experiments, conducted on a benchmark dataset of ten ontologies with 100 distinct CQs and 29 different user stories, compare the performance of three LLMs using the two prompting techniques. The results demonstrate improvements over the current state-of-the-art in LLM-supported ontology engineering. More specifically, the model OpenAI o1-preview with Ontogenia produces ontologies of sufficient quality to meet the requirements of ontology engineers, significantly outperforming novice ontology engineers in modelling ability. However, we still note some common mistakes and variability of result quality, which is important to take into account when using LLMs for ontology authoring support. We discuss these limitations and propose directions for future research.

Related papers

• Assessing the Capability of Large Language Models for Domain-Specific Ontology Generation [1.099532646524593]
  Large Language Models (LLMs) have shown significant potential for ontology engineering. We investigate the generalizability of two state-of-the-art LLMs, DeepSeek and o1-preview, by generating from a set of competency questions. Our findings show that the performance of the experiments is remarkably consistent across all domains.
  arXiv  Detail & Related papers  (2025-04-24T09:47:14Z)
• A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems [93.8285345915925]
  Reasoning is a fundamental cognitive process that enables logical inference, problem-solving, and decision-making. With the rapid advancement of large language models (LLMs), reasoning has emerged as a key capability that distinguishes advanced AI systems. We categorize existing methods along two dimensions: (1) Regimes, which define the stage at which reasoning is achieved; and (2) Architectures, which determine the components involved in the reasoning process.
  arXiv  Detail & Related papers  (2025-04-12T01:27:49Z)
• Trust at Your Own Peril: A Mixed Methods Exploration of the Ability of Large Language Models to Generate Expert-Like Systems Engineering Artifacts and a Characterization of Failure Modes [0.0]
  We present results from an empirical exploration, where a human expert-generated SE artifact was taken as a benchmark.<n>We then adopted a two-fold mixed-methods approach to compare AI generated artifacts against the benchmark.<n>We find that while the two-material appear very similar, AI generated artifacts exhibit serious failure modes that could be difficult to detect.
  arXiv  Detail & Related papers  (2025-02-13T17:05:18Z)
• Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field [0.0]
  This paper offers an analysis of the ability of large models to identify semantic relationships between different research topics.<n>We developed a gold standard based on the IEEE Thesaurus to evaluate the task.<n>Several models have achieved outstanding results, including Mixtral-8x7B, Dolphin-Mistral, and Claude 3-7B.
  arXiv  Detail & Related papers  (2024-12-11T10:11:41Z)
• A RAG Approach for Generating Competency Questions in Ontology Engineering [1.0044270899550196]
  With the emergence of Large Language Models (LLMs), there arises the possibility to automate and enhance this process.<n>We present a retrieval-augmented generation (RAG) approach that uses LLMs for the automatic generation of CQs.<n>We conduct experiments using GPT-4 on two domain engineering tasks and compare results against ground-truth CQs constructed by domain experts.
  arXiv  Detail & Related papers  (2024-09-13T13:34:32Z)
• MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs [55.20845457594977]
  Large language models (LLMs) have shown increasing capability in problem-solving and decision-making.<n>We present a process-based benchmark MR-Ben that demands a meta-reasoning skill.<n>Our meta-reasoning paradigm is especially suited for system-2 slow thinking.
  arXiv  Detail & Related papers  (2024-06-20T03:50:23Z)
• GLUECons: A Generic Benchmark for Learning Under Constraints [102.78051169725455]
  In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. We model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints.
  arXiv  Detail & Related papers  (2023-02-16T16:45:36Z)
• Enhancing Identification of Structure Function of Academic Articles Using Contextual Information [6.28532577139029]
  This paper takes articles of the ACL conference as the corpus to identify the structure function of academic articles. We employ the traditional machine learning models and deep learning models to construct the classifiers based on various feature input. Inspired by (2), this paper introduces contextual information into the deep learning models and achieved significant results.
  arXiv  Detail & Related papers  (2021-11-28T11:21:21Z)
• High Dimensional Level Set Estimation with Bayesian Neural Network [58.684954492439424]
  This paper proposes novel methods to solve the high dimensional Level Set Estimation problems using Bayesian Neural Networks. For each problem, we derive the corresponding theoretic information based acquisition function to sample the data points. Numerical experiments on both synthetic and real-world datasets show that our proposed method can achieve better results compared to existing state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-12-17T23:21:53Z)
• A Diagnostic Study of Explainability Techniques for Text Classification [52.879658637466605]
  We develop a list of diagnostic properties for evaluating existing explainability techniques. We compare the saliency scores assigned by the explainability techniques with human annotations of salient input regions to find relations between a model's performance and the agreement of its rationales with human ones.
  arXiv  Detail & Related papers  (2020-09-25T12:01:53Z)
• Machine Learning in Nano-Scale Biomedical Engineering [77.75587007080894]
  We review the existing research regarding the use of machine learning in nano-scale biomedical engineering. The main challenges that can be formulated as ML problems are classified into the three main categories. For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations.
  arXiv  Detail & Related papers  (2020-08-05T15:45:54Z)

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.