Visualizing Scientists’ Cognitive Representation of Materials Data through the Application of Ontology

Abstract

The introduction of data science as a viable new approach to research has led toward the establishment of materials informatics. However, issues relating to the infrastructure of data collection and organization in materials science have hindered the development of materials informatics. Issues related to data quality, conflicting terminologies between subfields, and inconsistent recording practices make it difficult to share data and implement data science. Furthermore, one can consider that scientific discoveries have occurred via the rules that are unconsciously defined by the scientist's mind, which has made scientific discovery an unintentional process. Here, ontology is proposed as a new way to structure databases as well as model scientific understandings of data. By implementing ontology during the database creation process, it not only becomes possible to define and visualize the experiences and knowledge held by researchers but also provides a way of creating a field-wide standard of defining data, the ability to incorporate data semantics, a method to increase the solid choice of descriptors for determining the materials' properties, and the space to merge databases in a more interactive and coherent manner. Ontology can also help improve database management by providing a way to incorporate new scientific discoveries into existing databases, which can have a positive effect on the search for new materials and material design.

An Ontology-Based Approach to Enable Data-Driven Research in the Field of NDT in Civil Engineering

“…To date, some scientific papers have been published that relate MS to semantic interoperability using ontologies. The need for further research in this field has been expressed multiple times, e.g., in terms of "interoperability" as an issue to be addressed explicitly [31,[34][35][36][37] or implicitly using the terms "data integration" [38,39], "lack of uniformity, data selectivity" [40], or "conflicting terminologies between subfields, inconsistent recording practices" [41].…”

A Perspective on Digital Knowledge Representation in Materials Science and Engineering

“…In addition, the application of inference-based techniques such as reasoning aims to enable the retrieval of implicit knowledge. [17,19,20,[23][24][25][26] While the technological basis can be directly established with existing methods, there is still a great demand for action, especially in data exchange and data sharing culture. This does not only require customized data management platforms with user-definable search, visualization, and analysis options but also community-driven MSE digitalization initiatives and platforms that promote this practice.…”

“…In addition, the application of inference‐based techniques such as reasoning aims to enable the retrieval of implicit knowledge. [ 17,19,20,23–26 ]…”

Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts

“…Here, graph theory is proposed as a means to represent the information and knowledge found within catalyst big data where the relationships within catalyst data are represented as complex networks. 18 Doing so would thus assist in revealing the underlying knowledge in catalyst big data in a comprehensive manner, leading towards a more informed way of designing catalysts.…”