Abstract
Automated knowledge graph (KG) construction is essential for navigating the rapidly expanding body of scientific literature. However, existing approaches struggle to recognize long multi-word entities, often fail to generalize across domains, and typically overlook the hierarchical nature of scientific knowledge. While general-purpose large language models (LLMs) offer adaptability, they are computationally expensive and yield inconsistent accuracy on specialized tasks. As a result, current KGs are shallow and inconsistent, limiting their utility for exploration and synthesis. We propose a two-stage framework for scalable, zero-shot scientific KG construction. The first stage, Z-NERD, introduces (i) Orthogonal Semantic Decomposition (OSD), which promotes domain-agnostic entity recognition by isolating semantic "turns" in text, and (ii) a Multi-Scale TCQK attention mechanism that captures coherent multi-word entities through n-gram-aware attention heads. The second stage, HGNet, performs relation extraction with hierarchy-aware message passing, explicitly modeling parent, child, and peer relations. To enforce global consistency, we introduce two complementary objectives: a Differentiable Hierarchy Loss to discourage cycles and shortcut edges, and a Continuum Abstraction Field (CAF) Loss that embeds abstraction levels along a learnable axis in Euclidean space. This is the first approach to formalize hierarchical abstraction as a continuous property within standard Euclidean embeddings, offering a simpler alternative to hyperbolic methods. We release SPHERE (this https URL), a multi-domain benchmark for hierarchical relation extraction. Our framework establishes a new state of the art on SciERC, SciER, and SPHERE, improving NER by 8.08% and RE by 5.99% on out-of-distribution tests. In zero-shot settings, gains reach 10.76% for NER and 26.2% for RE.
Abstract (translated)
自动化知识图谱（KG）构建对于快速增长的科研文献的探索至关重要。然而，现有方法难以识别长多词实体，通常无法跨领域泛化，且往往忽视科学知识的层次性。虽然通用大语言模型（LLMs）具有适应性，但它们在专业任务上计算成本高昂且准确率不稳定。因此，当前KG普遍浅层且不一致，限制了其用于探索和综合的效用。我们提出一个可扩展的零样本科学KG构建两阶段框架。第一阶段Z-NERD引入了（i）正交语义分解（OSD），通过隔离文本中的语义“转向”来促进领域无关实体识别；（ii）多尺度TCQK注意力机制，通过n-gram感知注意力头捕获连贯的多词实体。第二阶段HGNet通过层次感知消息传递执行关系抽取，显式建模父级、子级和同级关系。为强化全局一致性，我们引入两个互补目标：可微层次损失（抑制循环和捷径边）以及连续抽象场（CAF）损失（在欧氏空间的可学习轴上嵌入抽象层级）。这是首个将层次抽象形式化为标准欧氏嵌入中连续属性的方法，为双曲方法提供了更简单的替代方案。我们发布了SPHERE（此https链接），一个用于层次关系抽取的多领域基准。我们的框架在SciERC、SciER和SPHERE上建立了新的最先进水平，在分布外测试中将NER提升8.08%、RE提升5.99%。在零样本设置中，NER和RE的增益分别达到10.76%和26.2%。
URL
https://arxiv.org/abs/2603.23136
