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Ontology Collection

Was sind Ontologien?

 

Wissen kann in verschiedenen Formen vorliegen, z. B. in wissenschaftlichen Veröffentlichungen, Rohdaten und sogar Bildern. In den meisten Fällen kann Wissen nur dann vollständig verstanden werden, wenn der Kontext klar ist. So ist beispielsweise eine Tabelle mit Konzentrationen in einem Reaktor nur dann nützlich, wenn der Benutzer den Reaktor und die verwendeten Stoffe kennt. Oft gibt es keine allgemeine, formale Konzeptualisierung des dargestellten Wissens. Infolgedessen können die Daten nur im Kontext ihrer Darstellung verstanden werden. Eine Ontologie ist eine explizite Spezifikation einer Konzeptualisierung. So werden Ontologien verwendet, um Beziehungen zwischen Konzepten darzustellen und eine detailliertere Darstellung der Beziehungen zwischen diesen Konzepten zu liefern.

Sie möchten lernen, wie man Ontologien verwendet? NFDI4Cat unterstützt Sie hier mit einem Leitfaden mit den grundlegenden Schritten, um den Einstieg zu erleichtern.

Schritt 1: Die Domäne
Definieren und Abgrenzen des relevanten Bereichs. Dieser Schritt ist notwendig, um sicherzustellen, dass die Ontologie gut festgelegt ist und die Grenzen zu anderen Domänen klar sind.

Schritt 2: Begriffe Sammeln
Erfassung der wichtigsten Konzepte zur Beschreibung des definierten und abgegrenzten Bereichs. Anschließend sollten diese Konzepte in Diskussionen mit möglichst vielen Experten überprüft werden. Diese Diskussionen sollten auch Aufschluss über die hierarchische Struktur der Konzepte geben.

Schritt 3: Die Suche
Als erstes sollten die bestehenden Ontologien für die im vorherigen Schritt gesammelten Konzepte gesucht werden. Sie können dies entweder manuell tun oder über eine API auf Websites wie Ontology Lookup Service or BioPortal .

Schritt 4: Erweitern
Sie haben eine Ontologie gefunden, die die meisten der für Ihren Bereich relevanten Begriffe enthält? Dann vervollständigen Sie diese Ontologie um die fehlenden Begriffe.

Schritt 5: Publikation und Iteration
Die Entwicklung von Ontologien ist ein gemeinschaftlicher Prozess. Sobald Sie fertig sind, sollten Sie daher Ihre Ontologie gemäß den FAIR-Grundsätzen mit anderen Forschern teilen. Iteration im Allgemeinen und bei jedem vorgestellten Schritt wird dringend empfohlen, bis die Ontologie für Ihre Zwecke ausreichend ist.

Auf der Suche nach der richtigen Ontologie

Um das Problem der Suche nach der richtigen Ontologie zu lösen und den Prozess zu erleichtern, bietet NFDI4Cat einen Überblick über die wichtigsten Ontologien im Bereich der Katalyseforschung. Die Ontologien sind nach ihrer Position in der digitalen Wertschöpfungskette der Katalyse gruppiert:

  • Katalysedaten (Synthese-, Operando, Leistungs- und Charakterisierungsdaten),
  • Wärme-, Transport- und kinetische Daten,
  • Prozessgestaltung und
  • Energie- und Kostendaten:

Die wichtigsten Top-Level-Ontologien sind ebenfalls verfügbar.

AFO

Allotrope Foundation Ontologies

Performance Data

The AFO is an ontology suite that provides a standard vocabulary and semantic model for the representation of laboratory analytical processes. The AFO suite is aligned at the upper layer to the Basic Formal Ontology (BFO). The core domains modeled include: Equipment, Material, Process, and Results.

Website | Link to Ontology file
Utilizes Top level Ontology : BFO
BFO

Basic Formal Ontology

Top Level Ontology

The Basic Formal Ontology (BFO) is a small, upper level ontology that is designed for use in supporting information retrieval, analysis and integration in scientific and other domains. BFO is a genuine upper ontology. Thus it does not contain physical, chemical, biological or other terms which would properly fall within the coverage domains of the special sciences. BFO is used by more than 250 ontology-driven endeavors throughout the world.

Website | Link to Ontology file
CAO

Chemical Analysis Ontology

Characterisation Data Heat, Transport and Kinetic Data

The Chemical Analysis Ontology (CAO) is organized using the BFO framework with entries primarily under concepts (not complete by any means), material entities, information content entities (data items), roles, and processes. Currently, the ontology is more of a vocabulary in that development of predicates (ontology properties) that relate subjects to objects has not been a focal point.

Website | Link to Ontology file
Utilizes Top level Ontology : BFO
ChEBI

Chemical methods ontology

Synthesis Data

CHMO, the chemical methods ontology, describes methods used to collect data in chemical experiments, such as mass spectrometry and electron microscopy prepare and separate material for further analysis, such as sample ionisation, chromatography, and electrophoresis synthesise materials, such as epitaxy and continuous vapour deposition It also describes the instruments used in these experiments, such as mass spectrometers and chromatography columns. It is intended to be complementary to the Ontology for Biomedical Investigations (OBI).

Website | Link to Ontology file
Utilizes Top level Ontology : BFO
CHEMINF

Chemical Information Ontology

Characterisation Data

The Chemical Information Ontology (CHEMINF) aims to establish a standard in representing chemical information. In particular, it aims to produce an ontology to represent chemical structure and to richly describe chemical properties, whether intrinsic or computed.

Website | Link to Ontology file
Utilizes Top level Ontology : BFO
CHMO

Chemical methods ontology

Synthesis Data Operando Data Performance Data

CHMO, the chemical methods ontology, describes methods used to collect data in chemical experiments, such as mass spectrometry and electron microscopy prepare and separate material for further analysis, such as sample ionisation, chromatography, and electrophoresis synthesise materials, such as epitaxy and continuous vapour deposition It also describes the instruments used in these experiments, such as mass spectrometers and chromatography columns. It is intended to be complementary to the Ontology for Biomedical Investigations (OBI).

Website | Link to Ontology file
Utilizes Top level Ontology : BFO
CIF-Ontology

Crystallography domain ontology

Characterisation Data

A crystallography domain ontology based on EMMO and the CIF core dictionary. It is implemented as a formal language.

Website | Link to Ontology file
Utilizes Top level Ontology : EMMO
DOLCE

Descriptive Ontology for Linguistic and Cognitive Engineering

Top Level Ontology

DOLCE is a foundational ontology developed and maintained by the ISTC-CNR Laboratory for Applied Ontology . It was originally developed within the WonderWeb project and was conceived as the first module of the WonderWeb Foundational Ontologies Library (WFOL) together with OCHRE and BFO. DOLCE has remained stable after its first release in 2002/2003. It is formally specified in first-order logic (FOL).

Website | Link to Ontology file
EMMO

European Materials & Modelling Ontology

Top Level Ontology

EMMO is a multidisciplinary effort to develop a standard representational framework (the ontology) for applied sciences. It is based on physics, analytical philosophy and information and communication technologies. It has been instigated by materials science and provides the connection between the physical world, the experimental world (materials characterisation) and the simulation world (materials modelling). It is released under a Creative Commons CC BY 4.0 license.

Website | Link to Ontology file
ISO 15926

ISO 15926 interoperability standard

Heat, Transport and Kinetic Data Process Design Energy and Cost Data

The full title of ISO15926 is “Industrial Automation Systems and Integration of Life-cycle Data for Process Plants, including Oil and Gas Production Facilities.” It is about the standardization of the flow of information. The word “Integration” in the title means that by using ISO 15926 all applications can exchange information with each other without having to modify the applications in any way.

Website | Link to Ontology file
Utilizes Top level Ontology : ISO 15926-14
ISO 15926-14

ISO 15926 Part 14: Data model adapted for OWL2 direct semantics

Top Level Ontology

The primary purpose of ISO 15926 is to provide a foundation ontology to support the integration and sharing of data related to the lifecycle of a process plant in such a way that it is consistent, unambiguous, and minimizing the number of ways something could be expressed. Further purpose of ISO 15926-14 is to meet needs for OWL 2 ontologies that are based on ISO 15926-2, that enable efficient reasoning and that capture lifecycle information. A specific purpose is to demonstrate lifecycle modelling through a representation of the lifecycle model of ISO/IEC 81346-1. Another specific purpose is to exemplify how this standard can be used to develop industrial ontologies through various real-world use cases from industy.

Website | Link to Ontology file
OntoCAPE

Ontology for computer aided process engineering

Heat, Transport and Kinetic Data Process Design Energy and Cost Data

OntoCAPE is a large-scale ontology for the domain of Computer Aided Process Engineering (CAPE). Represented in a formal, machine-interpretable ontology language, OntoCAPE captures consensual knowledge of the process engineering domain in a generic way such that it can be reused and shared by groups of people and across software systems. On the basis of OntoCAPE, novel software support for various engineering activities can be developed; possible applications include the systematic management and retrieval of simulation models and design documents, electronic procurement of plant equipment, mathematical modeling, as well as the integration of design data from distributed sources.

Website | Link to Ontology file
OntoCompChem

Computational Chemistry Ontology

Synthesis Data Operando Data

Linked-data framework for connecting species in chemical kinetic reaction mechanisms with quantum calculations. A mechanism can be constructed from thermodynamic, reaction rate, and transport data that has been obtained either experimentally, computationally, or by a combination of both. In order to implement this approach, two existing ontologies, namely OntoKin, for representing chemical kinetic reaction mechanisms, and OntoCompChem, for representing quantum chemistry calculations, are extended.

Website | Link to Ontology file
OntoKin

Ontology for Chemical Kinetic Reaction Mechanisms

Operando Data Performance Data

An ontology for capturing both data and the semantics of chemical kinetic reaction mechanisms has been developed. Such mechanisms can be applied to simulate and understand the behavior of chemical processes, for example, the emission of pollutants from internal combustion engines. An ontology development methodology was used to produce the semantic model of the mechanisms, and a tool was developed to automate the assertion process. As part of the development methodology, the ontology is formally represented using a web ontology language (OWL), assessed by domain experts, and validated by applying a reasoning tool. The resulting ontology, termed OntoKin, has been used to represent example mechanisms from the literature. OntoKin and its instantiations are integrated to create a knowledge base (KB), which is deployed using the RDF4J triple store. The use of the OntoKin ontology and the KB is demonstrated for three use cases—querying across mechanisms, modeling atmospheric pollution dispersion, and as a mechanism browser tool. As part of the query use case, the OntoKin tools have been applied by a chemist to identify variations in the rate of a prompt NOx formation reaction in the combustion of ammonia as represented by four mechanisms in the literature. Merged with OntoCAPE.

Website | Link to Ontology file
OSMO

Ontology for simulation, modelling, and optimization

Operando Data Heat, Transport and Kinetic Data Process Design Energy and Cost Data

OSMO is an ontologization and extension of MODA, a workflow metadata standard that constitutes a mandatory requirement within a number of European calls and projects in the context of materials modelling. OSMO was developed within the Horizon 2020 project VIMMP (Virtual Materials Marketplace) and is part of a larger effort in ontology engineering driven by the European Materials Modelling Council, with the European Materials and Modelling Ontology (EMMO) as its core.

Website | Link to Ontology file
Utilizes Top level Ontology : EMMO
REX

REX

Performance Data Characterisation Data

An ontology of physico-chemical processes, i.e. physico-chemical changes occurring in course of time. This ontology is not well structured and lacks of properties. Also the design principle is not clear. Nevertheless it provides a good amount of concepts important to macroscopic and microscopic physical processes in form of a more hierarchical structure.

Website | Link to Ontology file
RXNO

Name Reaction Ontology

Synthesis Data

RXNO is the name reaction ontology. It contains more than 500 classes representing organic name reactions such as the Diels–Alder cyclization and the Cannizzaro reaction to their roles in an organic synthesis.

Website | Link to Ontology file
Utilizes Top level Ontology : BFO
SBO

Systems Biology Ontology

Operando Data Performance Data Characterisation Data

The Systems Biology Ontology is a set of controlled, relational vocabularies of terms commonly used in Systems Biology, and in particular in computational modelling. It consists of seven orthogonal vocabularies defining: reaction participants roles (e.g. „substrate“), quantitative parameters (e.g. „Michaelis constant“), classification of mathematical expressions describing the system (e.g. „mass action rate law“), modelling framework used (e.g. „logical framework“), the nature of the entity (e.g. „macromolecule“), the type of interaction (e.g. „process“), as well as a branch to define the different types of metadata that may be present within a model.

Website | Link to Ontology file
Utilizes Top level Ontology : BFO

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