What does a robot know about pizza?

Enter the word “pizza” into the Google search bar and in less than a second you are served up structured data with uniform descriptions of pizza recipes, local restaurants, and even nutritional information. How can Google retrieve so much information from just 5 simple letters?

The answer is that Google is not simply searching for webpages that contain the group of letters “pizza”. Instead, behind the scenes there exists a knowledge graph with nodes that represent real world things like people and places and…well, pizza.

In that graph, the concept of pizza is described by a persistent and unique identifier (/m/0663v) that provides not only information about pizza but also how it relates to other concepts in the graph. That knowledge can be expressed as three positional statements called triples that look something like <Pizza origin Italy> or <Pizza calories 266>. Go ahead and try it for yourself:

You’ve just entered the Semantic Web.

The Semantic Web

The Semantic Web aims to make the content on the Web more machine-readable and understandable by computers. It is based on the concept of “linked data” that describe things using standardized vocabularies and ontologies to allow for easier integration and reuse of data across different applications and domains.

Semantic Web concepts have had success in things like searching for information about people, places, and events. These graph-based approaches are one of the technologies behind social networks and even personal digital assistants like Siri.

But the adoption of Semantic Web approaches in science and scientific data has been slow. While some fields like Genomic Research have provided excellent examples of how the approach can be leveraged, there simply hasn’t been enough momentum to create the necessary ontologies for electrochemistry and batteries.

Until now…


BattINFO provides a shared vocabulary and taxonomy that defines the properties, attributes, and relationships of battery-related concepts, such as cell chemistry, cell design, and performance metrics. This can enable more accurate and consistent data collection, analysis, and interpretation, as well as better comparison and benchmarking of different battery technologies and applications.

Moreover, an ontology can support the development of automated tools for battery design, optimization, and control, such as machine learning models, simulation software, and decision support systems. By leveraging the semantic richness and logic of an ontology, these tools can reason about the interdependencies and trade-offs between different battery parameters and objectives and generate insights and recommendations that are both reliable and actionable.

BattINFO is defined as part of the larger Elementary Multi-Perspective Materials Ontology (EMMO) and is scheduled for release in conjunction with the first stable release of EMMO. The release of BattINFO fulfils one of the 12 key demonstrators defined for the BIG-MAP project, namely “Development of a community-wide European Battery Interface Ontology”.

GitHub Repository:

10 DECEMBER 2023