From Prompt to Graph: Comparing LLM-Based Information Extraction Strategies in Domain-Specific Ontology Development
National University of Singapore（新加坡国立大学）；Comptech i Skillingaryd AB（Comptech i Skillingaryd AB公司）；Lappeenranta University of Technology（拉彭兰塔理工大学）；California State University Northridge（加州州立大学北岭分校）
Xuan Liu, Ziyu Li, Mu He, Ziyang Ma, Xiaoxu Wu, Gizem Yilmaz, Yiyuan Xia, Bingbing Li, He Tan, Jerry Ying Hsi Fuh, Wen Feng Lu, Anders E. W. Jarfors, Per Jansson
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11 pages,8 figures,3 tables,presented at International Conference on Industry of the Future and Smart Manufacturing,2025
Ontologies are essential for structuring domain knowledge, improving accessibility, sharing, and reuse. However, traditional ontology construction relies on manual annotation and conventional natural language processing (NLP) techniques, making the process labour-intensive and costly, especially in specialised fields like casting manufacturing. The rise of Large Language Models (LLMs) offers new possibilities for automating knowledge extraction. This study investigates three LLM-based approaches, including pre-trained LLM-driven method, in-context learning (ICL) method and fine-tuning method to extract terms and relations from domain-specific texts using limited data. We compare their performances and use the best-performing method to build a casting ontology that validated by domian expert.
Zhejiang University（浙江大学）；AZFT Joint Lab for Knowledge Engine（AZFT知识引擎联合实验室）；Hangzhou Innovation Center（杭州创新中心）；Alibaba Group（阿里巴巴集团）；Tecent（腾讯）
Few-shot Learning (FSL) is aimed to make predictions based on a limited number of samples. Structured data such as knowledge graphs and ontology libraries has been leveraged to benefit the few-shot setting in various tasks. However, the priors adopted by the existing methods suffer from challenging knowledge missing, knowledge noise, and knowledge heterogeneity, which hinder the performance for few-shot learning. In this study, we explore knowledge injection for FSL with pre-trained language models and propose ontology-enhanced prompt-tuning (OntoPrompt). Specifically, we develop the ontology …
Automatic Construction of a Legal Citation Graph from 100 Million Ukrainian Court Decisions: Large-Scale Extraction, Topological Analysis, and Ontology-Driven Clustering
TextMineX: Data, Evaluation Framework and Ontology-guided LLM Pipeline for Humanitarian Mine Action
NEC Laboratories Europe（NEC欧洲实验室）；University of Stuttgart（斯图加特大学）；VAGO Solutions（VAGO解决方案）；Zurich University of Applied Sciences（苏黎世应用科学大学）；CAIR, Ss. Cyril and Methodius University of Skopje, North Macedonia（CAIR，斯科普耶塞尔维亚·梅托迪乌斯大学，北马其顿）
Predicting Microbial Ontology and Pathogen Risk from Environmental Metadata with Large Language Models
Department of Electrical and Computer Engineering, Drexel University, Philadelphia, United States（电气与计算机工程系，德雷塞尔大学，费城，美国）；MODULABS, Seoul, Korea（MODULABS，首尔，韩国）
University of Calabria（卡拉布里亚大学）；AGH University of Science and Technology（AGH科技大学）
Homogeneous unstructured data (HUD) are collections of unstructured documents that share common properties, such as similar layout, common file format, or common domain of values. Building on such properties, it would be desirable to automatically process HUD to access the main information through a semantic layer -- typically an ontology -- called semantic view. Hence, we propose an ontology-based approach for extracting semantically rich information from HUD, by integrating and extending recent technologies and results from the fields of classical information extraction, table recognition, o…
Feasibility of Structuring Stress Documentation Using an Ontology-Guided Large Language Model
College of Nursing, Seoul National University（首尔国立大学护理学院）；The Research Institute of Nursing Science, Seoul National University（首尔国立大学护理科学研究所）；Center of Human-Caring Nurse Leaders for the Future by the Brain Korea (BK 21) four project, College of Nursing, Seoul National University（首尔国立大学护理学院，韩国脑计划（BK21）第四阶段未来人文关怀护理领军人才中心）；College of Medicine, Seoul National University（首尔国立大学医学院）
Ministry of Higher Education and Scientific Research（高等教育与科学研究部）；University Tunis El manar（突尼斯埃尔马纳尔大学）；Faculty of Sciences of Tunis（突尼斯理学院）
The work presented in this master thesis consists of extracting a set of events from texts written in natural language. For this purpose, we have based ourselves on the basic notions of the information extraction as well as the open information extraction. First, we applied an open information extraction(OIE) system for the relationship extraction, to highlight the importance of OIEs in event extraction, and we used the ontology to the event modeling. We tested the results of our approach with test metrics. As a result, the two-level event extraction approach has shown good performance results…
IBM Research（IBM研究院）；IIT Guwahati（印度理工学院古瓦哈提分校）；IBM Research Almaden（IBM阿尔马登研究院）
A person ontology comprising concepts, attributes and relationships of people has a number of applications in data protection, didentification, population of knowledge graphs for business intelligence and fraud prevention. While artificial neural networks have led to improvements in Entity Recognition, Entity Classification, and Relation Extraction, creating an ontology largely remains a manual process, because it requires a fixed set of semantic relations between concepts. In this work, we present a system for automatically populating a person ontology graph from unstructured data using neura…
We propose an Ontology-Based Information Extraction (OBIE) system to automate the extraction of the criteria and values applied in Land Use Suitability Analysis (LUSA) from bylaw and regulation documents related to the geographic area of interest. The results obtained by our proposed LUSA OBIE system (land use suitability criteria and their values) are presented as an ontology populated with instances of the extracted criteria and property values. This latter output ontology is incorporated into a Multi-Criteria Decision Making (MCDM) model applied for constructing suitability maps for differe…
OntoLearner: A Modular Python Library for Ontology Learning with Large Language Models
TIB – Leibniz Information Centre for Science and Technology（TIB – 莱布尼茨科学与技术信息中心）；L3S Research Center, Leibniz University of Hannover（L3S研究中心，莱布尼茨汉诺威大学）；IBM Research（IBM研究院）
East China Normal University（华东师范大学）；Fudan University（复旦大学）
Prompt-based methods have become increasingly popular among information extraction tasks, especially in low-data scenarios. By formatting a finetune task into a pre-training objective, prompt-based methods resolve the data scarce problem effectively. However, seldom do previous research investigate the discrepancy among different prompt formulating strategies. In this work, we compare two kinds of prompts, name-based prompt and ontology-base prompt, and reveal how ontology-base prompt methods exceed its counterpart in zero-shot event argument extraction (EAE) . Furthermore, we analyse the pote…
Module extraction - the task of computing a (preferably small) fragment M of an ontology T that preserves entailments over a signature S - has found many applications in recent years. Extracting modules of minimal size is, however, computationally hard, and often algorithmically infeasible. Thus, practical techniques are based on approximations, where M provably captures the relevant entailments, but is not guaranteed to be minimal. Existing approximations, however, ensure that M preserves all second-order entailments of T w.r.t. S, which is stronger than is required in many applications, and …
Heterogeneous LLM Methods for Ontology Learning (Few-Shot Prompting, Ensemble Typing, and Attention-Based Taxonomies)
Skolkovo Institute of Science and Technology（斯克尔科沃科学与技术研究院）；BIMAI-Lab, Biomedically Informed Artificial Intelligence Laboratory, University of Sharjah（沙迦大学生物医学引导人工智能实验室）；Ivannikov Institute for System Programming of the Russian Academy of Sciences（俄罗斯科学院伊万诺夫系统编程研究所）；Lomonosov Moscow State University（莫斯科罗蒙诺索夫莫斯科大学）；AIRI Institute（AIRI研究所）
OntoLogX: Ontology-Guided Knowledge Graph Extraction from Cybersecurity Logs with Large Language Models
Department of Information Engineering, University of Brescia, Italy（博洛尼亚大学信息工程系，意大利）；Department of Computer Science, University of Turin, Italy（都灵大学计算机科学系，意大利）；School of Computer Science and Informatics, Cardiff University, United Kingdom（卡迪夫大学计算机科学与信息学学院，英国）；Department of Electronics and Computer Science, University of Southampton, United Kingdom（南安普顿大学电子与计算机科学系，英国）
Understanding large ontologies is still an issue, and has an impact on many ontology engineering tasks. We describe a novel method for identifying and extracting conceptual components from domain ontologies, which are used to understand and compare them. The method is applied to two corpora of ontologies in the Cultural Heritage and Conference domain, respectively. The results, which show good quality, are evaluated by manual inspection and by correlation with datasets and tool performance from the ontology alignment evaluation initiative.
OntoMetric: An Ontology-Driven LLM-Assisted Framework for Automated ESG Metric Knowledge Graph Generation
School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；Faculty of Sciences, Engineering and Technology, The University of Adelaide（阿德莱德大学科学、工程与技术学院）；School of Information Systems and Technology Management, University of New South Wales（新南威尔士大学信息系统与技术管理学院）
Kazan Federal University（喀山联邦大学）；Research Institute of Applied Semiotics of Tatarstan Academy of Sciences（鞑靼斯坦科学院应用符号学研究所）
In this paper, we present an ontology of mathematical knowledge concepts that covers a wide range of the fields of mathematics and introduces a balanced representation between comprehensive and sensible models. We demonstrate the applications of this representation in information extraction, semantic search, and education. We argue that the ontology can be a core of future integration of math-aware data sets in the Web of Data and, therefore, provide mappings onto relevant datasets, such as DBpedia and ScienceWISE.
Peking University（北京大学）；IER Business Development Center（IER业务发展中心）
Ontology can be used for the interpretation of natural language. To construct an anti-infective drug ontology, one needs to design and deploy a methodological step to carry out the entity discovery and linking. Medical synonym resources have been an important part of medical natural language processing (NLP). However, there are problems such as low precision and low recall rate. In this study, an NLP approach is adopted to generate candidate entities. Open ontology is analyzed to extract semantic relations. Six-word vector features and word-level features are selected to perform the entity lin…
We design a new technique for the distributional semantic modeling with a neural network-based approach to learn distributed term representations (or term embeddings) - term vector space models as a result, inspired by the recent ontology-related approach (using different types of contextual knowledge such as syntactic knowledge, terminological knowledge, semantic knowledge, etc.) to the identification of terms (term extraction) and relations between them (relation extraction) called semantic pre-processing technology - SPT. Our method relies on automatic term extraction from the natural langu…
FlyAOC: Evaluating Agentic Ontology Curation of Drosophila Scientific Knowledge Bases
University of Michigan（密歇根大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, Berkeley（加州大学伯克利分校）；University of Washington（华盛顿大学）
