知识图谱中假设美德的形式化：一个通用理论框架及其在文献发现实验中的验证
Formalising Hypothesis Virtues in Knowledge Graphs: A General Theoretical Framework and its Validation in Literature-Based Discovery Experiments
摘要：
我们介绍了一种发现信息学的方法，使用所谓的知识图谱作为基本的表示结构。知识图谱是一个总括术语，它包含了以图形形式对大量松散结构的知识进行易处理表示的各种方法。它主要用于Web和链接开放数据环境，但也适用于处理知识表示的任何其他领域。从我们的方法的角度来看，发现信息学的挑战，知识图谱对应于假设。我们提出了一个框架内正式所谓的假设美德知识图谱。该框架基于科学哲学的经典著作，自然地从信息量大的基本概念发展到与特定美德相对应的可操作的措施规范。因此，这些措施可以用来确定细化子集的知识图谱，具有较大的相对潜力，使发现。我们通过基于文献的发现实验验证了所提出的框架。实验结果表明了本文工作的实用性和相对于传统方法的优越性。相关方法。
英文摘要：
We introduce an approach to discovery informatics that uses so called knowledge graphs as the essential representation structure. Knowledge graph is an umbrella term that subsumes various approaches to tractable representation of large volumes of loosely structured knowledge in a graph form. It has been used primarily in the Web and Linked Open Data contexts, but is applicable to any other area dealing with knowledge representation. In the perspective of our approach motivated by the challenges of discovery informatics, knowledge graphs correspond to hypotheses. We present a framework for formalising so called hypothesis virtues within knowledge graphs. The framework is based on a classic work in philosophy of science, and naturally progresses from mostly informative foundational notions to actionable specifications of measures corresponding to particular virtues. These measures can consequently be used to determine refined sub-sets of knowledge graphs that have large relative potential for making discoveries. We validate the proposed framework by experiments in literature-based discovery. The experiments have demonstrated the utility of our work and its superiority w.r.t. related approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
