Knowledge Graphs for Autonomous Formulation

自主配方是指使用自主软件或机器人来确定大众市场消费品的组成和配方。用于进行这些实验的机器人生成海量数据以及不同数量和质量的元数据。探索这些数据对材料科学家来说非常重要，因为它可以包含关于复杂行为和特征的信息。我们提出引入知识图来提高可以从数据中提取的信息的质量。2012年由谷歌推广的知识图谱是由两个概念或类以及它们之间的关系构成的事实或三元组的集合。由于这些关系是由本体定义的，因此知识图中的知识可以进行推理。在机器人生成的实验数据的用例中，通过将这些数据存储在知识图中提供的结构可以允许材料科学家识别数据中更复杂的关系和行为。这可能涉及调查和预测改变不同实验参数可能对给定实验结果产生的影响等活动。这项工作旨在开发和实施一种过程，为在实验室环境中操作的机器人收集的数据自动生成知识图，目的是帮助材料科学家增强其产品开发流水线。

"Autonomous Formulation" refers to the use of the autonomous software or robots to determine the composition and recipe of mass-market consumer products. The robots used to carry out these experiments generate a vast amount data along with varying quantities and qualities of metadata. Exploring this data is of high importance to the material scientists as it can contain information regarding complex behaviours and characteristics. We proposed the introduction of knowledge graphs to enhance the quality of information that can be extracted from the data. Knowledge graphs, as popularised by Google in 2012, are a collection of facts, or triples, that are constructed with two concepts, or classes, and the relationship between them. As these relationships are defined by an ontology, the knowledge within a knowledge graph can be reasoned upon.Within the use case of experimental data generated by robots, the structure provided by storing this data in a knowledge graph can allow material scientists to identify more complex relationships and behaviours within the data. This can involve activities such as investigating and predicting the effect that changing different experimental parameters may have on the results of a given experiment. Insights of this nature can help increase the speed of development of products as well as uncover previously unknown pathways of research.This work aims to develop and implement a process to (semi)automatically generate a knowledge graph for data collected by robots operating in a laboratory setting, with the goal of assisting material scientists enhance their product development pipeline.