Materials and related systems ontologies: Organising knowledge with semantic technologies

In the field of natural language processing (NLP), an ontology is defined as a structured and formal representation of information about a particular domain. It defines the concepts, entities, relationships and properties within that domain and how they relate to each other. Ontologies are used to model and organise knowledge in a way that can be understood and processed by computers.

In the field of biomaterials, advances in computational and experimental techniques have led to significant advances in the development of new advanced materials and large amounts of related information, key aspects for innovation and socio-economic assets. In this context, ontologies are considered fundamental structures for organising all the data in a systematic and efficient way.

A brief description and characteristics of the most relevant existing ontologies in the field of materials and biomaterials are explained below [1].

EMMO (Elementary Multiperspective Material Ontology) [2]

The Elementary Multiperspective Material Ontology (EMMO) is the result of a multidisciplinary effort within the EMMC and it is considered as a top and middle level ontology aimed to organise and define the building blocks and knowledge of fundamental physics, chemistry and ontology behind materials science. Its base has been created from a very low level by extracting practical concepts from the applied sciences (physics and materials science).

MDO (Materials Design Ontology) [3]

Focusing on the field of materials design (more specifically in non-crystalline/periodic systems and single molecules), this ontology aims to define concepts and relationships in the field of computational material science. This ontology has the property of being modular, built from the concepts of structure, provenance and property, and is also structured from answers to competency questions provided by experts in the field, as well as from use cases.

DEB (the Device, Experimental scaffolds and medical Device ontology) [4]

The devices, experimental scaffolds, and biomaterials ontology (DEB) was created with the aim of structuring and organising information about biomaterials, their design, manufacture and biological testing, by using Natural Language Processing (NLP) and text analysis tools to identify relevant ontology terms from the diverse and very heterogeneous and properly curated literature on biomaterials. One of the most interesting aspects of DEB is its ability to add new fields to the ontology depending on the extracted terms that emerge from new research.

The terms included in the ontology were validated in two phases: First, a curated selection of articles was extracted to verify that all articles were easily findable with the terms present in the ontology. Second, domain experts were asked to repeat the task with their articles and topics of interest, suggesting new terms to add to the ontology.

ChEBI (Chemical Entities of Biological Interest) [5]

The ChEBI ontology is oriented towards molecular entities, with a focus on chemical compounds. The defined term is “molecular entity” (either natural or synthetic potentially bioactive products), understood as any “constitutionally or isotopically distinct atom, molecule, ion, ion pair, radical, radical ion, complex, conformer, etc., identifiable as a separately distinguishable entity”. The concepts and definitions described in the ontology are useful for structuring information where individual molecular systems and their components are involved.

CHMO (CHemical Methods Ontology) [6]

CHMO, the chemical methods ontology, is a mature ontology for experimental procedures that describes the methods used to extract relevant data from chemical experiments, including characterisation (mass spectrometry, electron microscopy), material processing and isolation (ionisation, chromatography, electrophoresis), material synthesis and fabrication (epitaxy, continuous vapour deposition), and the instrumentation used in the experiments, such as mass spectrometers and chromatography columns. CHMO is intended to be a complementary ontology to the Ontology for Biomedical Investigations (OBI)

MSEO (the Materials Science and Engineering Ontology) [7]

MSEO utilises the Common Core Ontology stack and aims to provide materials scientists and engineers with semantic data management tools that can be easily implemented in other scientific domains, to represent their experiments and resulting data. The main goal is to create machine- and human-readable semantic data which can be easily digested by other science domains.

Author: Miguel Rodríguez


[1] Fabio Le Piane a,b , Matteo Baldoni a , Mauro Gaspari b and Francesco Mercuri a,*. MAMBO: a lightweight ontology for multiscale materials and applications.

[2] E. Ghedini and G. Schmitz, EMMO the EUROPEAN MATERIALS MODELLING ONTOLOGY, EMMC Workshop on Interoperability in Materials Modelling (2017), 7–8.

[3] H. Li, R. Armiento and P. Lambrix, An Ontology for the Materials Design Domain, in: The Semantic Web – ISWC 2020, J.Z. Pan, V. Tamma, C. d’Amato, K. Janowicz, B. Fu, A. Polleres, O. Seneviratne and L. Kagal, eds, Springer International Publishing, Cham, 2020, pp. 212–227. ISBN 978-3-030-62466-8. doi:10.1007/978-3-030-62466-8_14.

[4] O. Hakimi, J.L. Gelpi, M. Krallinger, F. Curi, D. Repchevsky and M.P. Ginebra, The Devices, Experimental Scaffolds, and Biomaterials Ontology (DEB): A Tool for Mapping, Annotation, and Analysis of Biomaterials Data, Advanced Functional Materials 30

(2020), 1909910. doi:10.1002/ADFM.201909910.

[5] K. Degtyarenko, P.D. matos, M. Ennis, J. Hastings, M. Zbinden, A. Mcnaught, R. Alcántara, M. Darsow, M. Guedj and M. Ashburner, ChEBI: A database and ontology for chemical entities of biological interest, Nucleic Acids Research 36 (2008), 344–350.


[6] Chemical Methods Ontology, Ontology Lookup Service, EMBL-EBI.
[7] T. Hanke, Material Science and Engineering Ontology, Materials Open Laboratory MatPortal.


Ontology, Biomaterials, Materials, Text Mining, Materials Information Extraction.