Factory Data Model

A suitable factory data model must be able to cover and integrate heterogeneous knowledge domains, while guaranteeing extensibility. Herein, a modular ontology-based Factory Data Model [7] is adopted to formalize the information that is in particular relevant to the design and management of factories and manufacturing systems. The ontology reuse, even if often applied in a limited fashion, represents a key best practice that was followed in this work. Indeed, already existing ontologies have been reused, integrated, and extended.

OWL Ontology - Modular Architecture

The modular data model architecture adopted by VLFT consists of OWL ontology modules. An OWL ontology can be serialized in various formats (e.g. Turtle, RDF/XML, OWL/XML) and can be opened with different ontology editors (e.g. Protégé).
The ontology architecture consists of the following modules (available in this repository):
  • list, an ontology defining the set of entities used to describe the OWL list pattern [1]
  • express, ontology mapping the concepts of EXPRESS language (International Organization for Standardization, 2004) to OWL [1]
  • IFC4_ADD1, the ifcOWL ontology that is converted from the IFC standard defined in EXPRESS language [2]
  • time, an ontology defining concepts related to time [3]
  • fsm, an ontology defining the concepts required for modelling finite state machines [4]
  • sosa, the Sensor, Observation, Sample, and Actuator (SOSA) Core Ontology [5]
  • ssn, the Semantic Sensor Network Ontology [5]
  • statistics, an ontology that defines probability distributions and descriptive statistics concepts [6]
  • expression, an ontology modelling algebraic and logical expressions [6]
  • osph, an ontology modelling Object States and Performance History, while integrating the ontology modules fsm, statistics, ssn, sosa, expression [6]
  • IFC4_ADD1_extension, an ontology module integrating osph and IFC_ADD1_rules modules, while adding general purpose extensions to IFC_ADD1 [7]
  • factory, a specialization of IFC_ADD1 with definitions related to production processes, part types, manufacturing systems and machine tools [7]
Ontology modules in the Factory Data Model

Modules and Prefixes

The following table reports the list of prefixes that have been defined with reference to vocabularies and ontology modules. All modules are available online at the same address, except fsm that can be found at∼dolog/fsm/fsm.owl and ifc that can be found at
The whole set of ontology modules can be found also in the "repository" folder of the OntoGui installation.
Each ontology module contains the definition of OWL classes and properties and some examples are given in the next subsection.

Object Typing pattern

Ontology modules that are based on the IFC standard (i.e. ifcOWL) take advantage of pattern of object typing thanks to the definition of two main classes: ifc:IfcObject and ifc:IfcTypeObject.
Class ifc:IfcTypeObject (and its subclasses, e.g. fa:MachineToolType) can be used to define “specific information about a type, being common to all occurrences of this type”. Instances of ifc:IfcTypeObject are represented by sets of properties which apply to all the associated instances of ifc:IfcObject and its subclasses. Object types can be however directly instantiated without being assigned to instances of ifc:IfcObject [8][9]. This approach is useful whenever the user needs to be generic or non-committal while defining the resources needed or used to execute a process. The link between instances of ifc:IfcObject and ifc:IfcTypeObject is realized via properties ifcext:typesObject and ifcext:isDefinedByType.
In practical applications, subclasses of ifc:IfcTypeObject are instatiated to populate a catalog of models/types/templates (e.g. machine models in the catalog of a machine tool builder), whereas subclasses of ifc:IfcObject are instatiated to define the occurrences composing a specific factory configuration.


  1. 1.
    Pauwels P, Terkaj W (2016) EXPRESS to OWL for construction industry: Towards a recommendable and usable ifcOWL ontology. Automation in Construction, 63:100–133. ISSN: 0926-5805. doi:10.1016/j.autcon.2015.12.003
  2. 2.
    Pauwels P, Krijnen T, Terkaj W, Beetz J (2017) Enhancing the ifcOWL ontology with an alternative representation for geometric data. Automation in Construction, 80:77-94. ISSN: 0926-5805. doi:10.1016/j.autcon.2017.03.001
  3. 3.
    Time Ontology in OWL,
  4. 4.
    Dolog P (2004) Model-Driven Navigation Design for Semantic Web Applications with the UML-Guide. In Proc. ICWE, pages 75–86, 2004.
  5. 5.
    Semantic Sensor Network Ontology,
  6. 6.
    Terkaj W, Schneider GF, Pauwels P (2017) Reusing Domain Ontologies in Linked Building Data: the Case of Building Automation and Control. Proceedings of the 8th Workshop Formal Ontologies Meet Industry, Joint Ontology Workshops 2017, CEUR Workshop Proceedings, vol. 2050.
  7. 7.
    Terkaj W, Gaboardi P, Trevisan C, Tolio T, Urgo M (2019) A digital factory platform for the design of roll shop plants. CIRP Journal of Manufacturing Science and Technology, 26:88-93. ISSN: 1755-5817. doi:10.1016/j.cirpj.2019.04.007
  8. 8.
    Liebich, T., Adachi, Y., Forester, J., Hyvarinen, J., Richter, S., Chipman, T., Weise, M. & Wix, J. (2013). Industry foundation classes IFC4 official release.
  9. 9.
    Borgo S, Sanfilippo EM, Sojic A, Terkaj W (2015) Ontological Analysis and Engineering Standards: An Initial Study of IFC. In: Ebrahimipour V, Yacout S (eds) Ontology Modeling in Physical Asset Integrity Management. Springer: 17-43.