Application of causality-first functional decomposition trees to data science and materials informatics

The promotion of open science, in which data generated from experimental and theoretical calculations are released in accordance with the FAIR principle to reuse the data, is a worldwide trend. It is desirable to use metadata with vocabulary of common or interconvertible conceptual structures to publish data. Furthermore, the conceptual structure of many data schemas is created to form a hierarchical structure, but since there is no single conceptual structure, it is more convenient to create the conceptual structure based on a method that allows terms to be easily added or modified.

One of the vocabulary creation methods is ontology, which manifests domain tacit knowledge and decomposes concepts to distinguish objects. However, as an approach opposite to ontology, it is also possible to create conceptual structures in the process of integrating device functions to achieve specific objectives. One of these purpose-driven methods is the functional decomposition tree [1], which deals with the functions of artifacts. Furthermore, the same framework can be applied to the behavior of humans and others [2]. In this presentation, examples of conceptual structure creation using the functional decomposition trees will be presented, which facilitate explanation and modification of hierarchical structures outside the domain by directly associating vocabulary with functions. In addition, as a method of thinking manifestation to explain device functions to achieve specific objectives, an example is presented in which the functional decomposition tree is used to associate explanatory variables with objective variables for the application of machine learning methods from atomic structures [3].