发表机构
Allen Institute for AI; University of Washington; Allen Institute（艾伦人工智能研究所; 华盛顿大学; 艾伦研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Asterism通过分层本体提取论文中的概念关系三元组，支持研究者以不同粒度聚合观察并构建符合偏好的理论，在实地部署和两个案例中验证了其发现新机制与生成可检验假说的能力。
AI 中文摘要
理论将许多独立的观察纳入一个包含新假说的框架中。构建此类理论的研究者必须综合分散在许多论文中的观察，这些论文描述了相关概念但往往使用不同术语。哪些概念最重要也取决于研究者的偏好和研究问题。近期方法利用大型语言模型（LLM）扩展理论综合，但将选择和研究者的直觉自动化掉了。我们提出Asterism，它从数百篇论文中提取观察作为概念-关系三元组，并将概念统一到分层本体中。研究者利用该本体策划证据图，并在不同粒度级别聚合观察，以聚焦于特定感兴趣现象的理论形成。在一项实地部署（n=10）中，研究者从观察出发构建理论，并保持概念和假说符合其偏好。在两个案例研究中，免疫学和农业研究团队发现了其标准分析之外的机制，并构建了值得后续实验验证的假说。
英文摘要
A theory draws many independent observations into one framework with novel hypotheses. A researcher building such a theory must synthesize observations scattered across many papers, each describing related concepts but often in different terms. Which concepts matter most also depends on their preferences and research questions. Recent approaches scale theory synthesis with LLMs, but automate away choices and intuitions from researchers. We present Asterism, which extracts observations from hundreds of papers as concept-relation triples, with concepts unified in a hierarchical ontology. Researchers curate an evidence graph using the ontology and aggregate observations at different levels of granularity to focus theory formation on specific phenomena of interest. In a field deployment (n=10), researchers worked from observations to theories, and kept concepts and hypotheses fitting their preferences. In two case studies, teams of immunology and agriculture researchers discovered mechanisms outside their standard analyses and constructed hypotheses worth follow-up experiments.
