Semantic Technologies to Model Battery Data and Knowledge

Abstract

Data-intensive battery research is here to stay. With open data incentives on the rise and simpler software interfaces granting access to powerful machine learning algorithms, researchers are uncovering a wealth of insights from battery data. However, data without context are just unusable digital bits. Battery data must be appropriately described to render it interoperable and reusable for traditional and data-intensive research. This requires overcoming numerous challenges associated to data heterogeneity, inconsistency, standardization, and meaning. Semantic technologies address these challenges by providing a unified and flexible framework for integrating and reasoning about data from heterogeneous sources.In this contribution, we will introduce Semantic Technologies and how they enable both human and software agents to understand and reuse battery data. We will present the tools we are developing to i) encourage the adoption of a common standard vocabulary to describe the battery domain[1], ii) connect research resources (datasets, experts, reports, publications, projects) within an accessible platform[2], and iii) reduce the effort required to upgrade data with semantic context. Overall, these tools take concrete steps towards guaranteeing compliance with FAIR data principles. We will further outline plans for future development, including strategies for reaching out to the global battery landscape, facilitate the curation of battery data for data-driven research, and enable knowledge exploration via natural language models.[1] S. Clark et al., "Toward a Unified Description of Battery Data," Adv. Energy Mater., vol. 2102702, 2021.[2] https://github.com/BIG-MAP/Demo-BatteryDataSemanticSearch