发表机构
University of Passau; Chemnitz University of Technology（帕绍大学; 开姆尼茨工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一个端到端框架，结合DistilBERT嵌入、聚类预过滤和GPT-4o迭代提示，自动构建本体网络中的类型化语义链接，在ReproduceMeON上达到80.19%精确率和0.890 F1，显著优于相似性基线。
AI 中文摘要
在本体之间构建类型化且经过验证的语义链接，对于实现跨异构和跨学科知识领域的互操作性至关重要。然而，手动策展此类链接难以扩展。为应对这一挑战，我们提出了一种用于本体网络构建的端到端框架，该框架自动化了域内和域间关系的发现与生成。我们的方法结合了领域自适应的DistilBERT嵌入以获取密集上下文表示，基于聚类的预过滤以减少候选搜索空间，以及通过迭代提示工程驱动的GPT-4o关系生成，以产生语义丰富且可解释的链接。将该流程应用于ReproduceMeON——一个涵盖机器学习、显微镜、计算科学和实验工作流的33个本体的网络——该流程将约80万个原始概念对减少到9.5万个高质量候选。两位独立标注者对429个生成关系进行的人类专家验证得出总体精确率为80.19%（在高置信度标注上为91.49%），F1为0.890，且标注者间一致性显著。与五个基于相似性的基线（包括Sentence-BERT）的比较实验显示存在显著性能差距（最佳基线F1=0.581），而消融研究表明，仅基于相似性的方法在过滤后的候选集上无法区分有效与无效关系（AUC约0.5）。这些发现凸显了基于LLM的概念角色和领域语义推理对于准确关系构建的必要性。
英文摘要
Constructing typed, justified semantic links between ontologies is essential for enabling interoperability across heterogeneous and interdisciplinary knowledge domains. However, manually curating such links is difficult to scale. To address this challenge, we propose an end-to-end framework for ontology network construction that automates the discovery and generation of both intra-domain and inter-domain relationships. Our approach combines domain-adapted DistilBERT embeddings for dense contextual representation, clustering-based pre-filtering to reduce the candidate search space, and GPT-4o-driven relationship generation via iterative prompt engineering to produce semantically rich, interpretable links. Applied to ReproduceMeON - a network of 33 ontologies spanning machine learning, microscopy, computational science, and experimental workflow - the pipeline reduces approximately 800k raw concept pairs to 95k high-quality candidates. Human expert validation of 429 generated relationships by two independent annotators yields an overall precision of 80.19% (91.49% on high-certainty annotations) and an F1 of 0.890, with substantial inter-annotator agreement. Comparative experiments against five similarity-based baselines, including Sentence-BERT, show a substantial performance gap (best baseline F1 = 0.581), while an ablation study demonstrates that similarity-based methods alone fail to discriminate valid from invalid relationships (AUC approx 0.5) on the filtered candidate set. These findings highlight the necessity of LLM-based reasoning over concept roles and domain semantics for accurate relationship construction.
