Elementary Multiperspective Materials Ontology (EMMO)

Introduction

The Elementary Multiperspective Materials Ontology (EMMO) is the official top-level ontology of the European Materials Modelling Council (EMMC). It provides a common, physics-based foundation for describing materials, processes, and measurements in a way that connects scientific knowledge with digital data.

Put simply, EMMO is a semantic framework for science — a bridge between how humans reason about the world and how computers represent it. It defines a shared, logically consistent structure that supports interoperability between different domains, such as chemistry, physics, engineering, and data science.

Why EMMO?

Scientific information is diverse. A single experiment might involve:

• physical samples and instruments,

• abstract models and mathematical relations,

• measurements stored in databases,

• and textual descriptions in publications.

Traditionally, these pieces live in separate systems with little semantic connection. EMMO provides a unifying layer that allows us to describe all of them — matter, models, data, and meaning — in one coherent framework.

By grounding everything in a common ontology, EMMO enables machines to understand how a measurement, a simulation, and a physical object are related. This makes it possible to link data from different sources and automate tasks that would otherwise require manual interpretation.

Philosophical roots (in plain language)

EMMO is based on ideas from both philosophy and physics. From philosophy, it borrows the concept of ontology — a systematic account of what exists and how things relate. From physics, it adopts the principle that every entity we describe should be anchored to physical reality: a particle, a field, a process, or an observation.

The result is an ontology that models the world from multiple perspectives:

• Physical — how things exist and interact in space and time.

• Semiotic — how we represent those things (data, symbols, language).

• Mathematical — how we quantify and model their behaviour.

Hence the name Elementary Multiperspective Materials Ontology.

Core principles

1. Reality-based Every class in EMMO ultimately refers to something that can be observed, measured, or represented.

2. Graph-based logic Relationships between entities (part-of, has-participant, represents, etc.) are defined using RDF and OWL, ensuring logical consistency and reasoning.

3. Modularity EMMO is designed as a foundation that other ontologies can extend — such as domain-electrochemistry, domain-battery, and domain-chemical-substance.

4. Interoperability Because it follows Semantic Web standards, EMMO can integrate seamlessly with datasets, software, and databases across scientific fields.

What EMMO provides

• A consistent vocabulary for describing physical systems, materials, and processes.

• A framework for connecting experimental, modelling, and simulation data.

• Logical definitions that enable automated reasoning, data validation, and semantic interoperability across projects.

• A solid base for developing domain ontologies that inherit common meaning.

Relation to other ontologies

EMMO sits at the top of the ontology hierarchy. Other ontologies — such as the Domain Electrochemistry Ontology or the Battery Ontology — extend EMMO to specialise its concepts for specific fields.

For example:

::

EMMO → defines Matter, Process, Property

↓

Domain Electrochemistry → defines Electrode, Electrolyte, IonTransport

↓

Domain Battery → defines Cell, Separator, Charging, Discharging

This layered approach ensures that all domain ontologies remain compatible with each other and with the broader Semantic Web ecosystem.

The EMMO provides a common foundation for domain ontologies that describe specific scientific and engineering systems.

Why this matters for researchers

By using EMMO as a common foundation, data and models from different disciplines can speak the same language. This supports:

• FAIR data — findable, accessible, interoperable, reusable

• Cross-domain linking — connecting experiments, simulations, and literature

• Automation — enabling reasoning, query, and workflow integration

EMMO is not an abstract philosophical exercise — it is a practical tool for building a digital ecosystem for science, where knowledge can flow freely between people, software, and data.

Contents

• Units
  • Expressing Units in EMMO
  • Unit Features
  • Supported Systems of Units
• Properties and Quantities
  • Introduction
  • Understanding Properties in EMMO
  • Quantities: Measurable Properties
  • Linking Properties to Entities
  • Why this structure matters
  • Reasoning and Validation
  • Summary
• Measurements in EMMO
  • Overview of Measurement Concepts
  • Example: Measuring the mass of a sample
  • Alternative Representations
  • Measurement Participants
  • Linking to Workflows
  • Best Practices
  • Summary
• Processes in EMMO
  • Overview of Processes
  • Representing Processes
  • Mereology of Processes
  • Workflows and Tasks
  • Workflows vs. SubProcesses
  • Object Properties for Processes
  • Best Practices
  • Summary
• Describing Wholes and Parts
  • Introduction
  • The Holistic Perspective in EMMO
  • Using hasPart
  • Specialized Subproperties of hasPart
  • When to choose which relation
  • Example: describing a battery cell
  • Reasoning across part hierarchies
  • Best Practices
  • Summary
• Naming Conventions