Linked Open Terms

Industrial version

LOT (Linked Open Terms) Methodology is an industrial method for developing ontologies and vocabularies. This website will provide support for following the LOT methodology in your developments.

LOT workflow.

Requirements specification workflow.
Requirements specification workflow

The aim of the requirements specification process is to state why the ontology is being built and to identify and define the requirements the ontology should fulfil. Taking as input the documentation and data provided by domain experts and users, the ontology development team generates a first proposal of ontological requirements written in the form of competency questions or statements. In this step, involvement and commitment by experts in the specific domain at hand is required to generate the appropriate industry perspective and knowledge. The CORAL corpus, which includes real-world requirements and lexico-syntactic patterns to write requirements, can be taken as a reference. Moreover, this GitHub repository provides a Microsoft Word and a LaTex template for an ontology requirement specification document (ORSD).

The domain experts and users in collaboration with the ontology development team has to validate whether the ontology requirements defined in the previous step are correct and complete.

Ontology implementation workflow.
Ontology implementation workflow

The aim of the ontology implementation process is to build the ontology using a formal language, based on the ontological requirements identified by the domain experts. After defining the first set of requirements, though modification and addition of requirements is allowed during the development, the ontology implementation phase is carried out through a number of sprints. The ontology developers schedule and plan the ontology development according to the prioritization of the requirements in the ontology requirements specification process. The ontology development team builds the ontology iteratively, implementing only a certain number of requirements in each iteration. The output of each iteration is a new version of the ontology. This activity includes several sub-activities:

  • Conceptualization: the aim of this activity is to build an ontology model from the ontological requirements identified in the requirements specification process. During the ontology conceptualization, the domain knowledge obtained from the ORSD document is organized and structured into a model by the ontology developers. Chowlk visual notation provides a set of recomendations for ontology diagrams representation.

  • Ontology reuse: during this activity the term extraction is carried out, by identifying basic concepts and the relationships between those concepts are extracted. These extracted terms should consist of not only the terms from the data source, but also of synonyms of those terms. The terminology might be extracted from the competency questions (Guidelines from NeOn Methodology) or from the data, for what you might have domain expert advice. These terms will be used during the ontology search with the aim of looking for existing ontologies that best fit them.

  • Encoding: during this activity, the ontology development team generates computable models in the OWL language from the ontology model. The ontology code resultant from this activity includes metadata, such as creator, title, publisher, license and version of the ontology.

  • Evaluation: during this activity, the ontology developers guarantee that the ontology does not have syntactic, modelling or semantic errors and that the ontology fulfil all the requirements scheduled for the ontology during the requirements specification activity.

Ontology publication workflow.
Ontology publication workflow

The aim of the ontology publication process is to provide an online ontology accessible both as a human-readable documentation and a machine-readable file from its URI. The ontology needs to be evaluated before its publication to guarantee that is ready to be used. This activity includes:

  • Documentation: the ontology development team in collaboration with the domain experts generates the ontology documentation of the release candidate. This documentation includes an HTML description of the ontology which describes the classes, properties and data properties of the ontology, and the license URI and title being used. The domain experts have to collaborate with the ontology development team to describe the classes and the properties. This description also includes metadata, such as creator, publisher, date of creation, last modification or version number. The documentation also includes diagrams which store the graphical representation of the ontology, including taxonomy and class diagrams.

  • Publication: once the documentation of the ontology has been generated, the ontology is published on the Web. This online ontology is accessible via its namespace URI as a machine-readable file and a human readable documentation using content negotiation.

Ontology maintenance workflow.
Ontology maintenance workflow

The goal of this activity is to update and add new requirements to the ontology that are not identified in the ORSD, to identify and corrects errors or to schedule a new iteration for ontology development. During the ontology development process, the domain experts can propose new requirements or improvements over the ontology, as well as identify errors both in the requirements and in the implementation.

Complete methodology detailed view

Ontology implementation workflow.
Methodology detailed workflow

The LOT methodology figures are available for reuse in the LOT Github repository under the Creative Commons Attribution Non Commercial Share Alike license.


Tools and resources developed by OEG

Success stories


VICINITY is an EU funded project under Horizon 2020. It proposes a platform and ecosystem that provides "interoperability as a service" for infrastructures in the Internet of Things.


DELTA is an EU funded project under Horizon 2020. It proposes a demand-response management platform that introduces scalability and adaptiveness into the Aggregator’s DR toolkits.


BIMERR is a EU funded project which aims to provide a suite of interoperable tools to support AEC stakeholders throughout the energy efficiency renovation process of existing buildings.

Ciudades Abiertas

Ciudades Abiertas is a Spanish project where several cities are working together in the creation of a shared set of ontologies that can be used to provide homogeneous open data in their open data portals and APIs.


EasyTV is a H2020 European project which main goal is to provide equal access to television and audio-visual services to that all users, especially persons with disabilities and users with special needs.

Mass Mediator

CEU Mass Mediator is a tool for searching metabolites in different databases (Kegg, HMDB, LipidMaps, Metlin, MINE and an in-house library). This is specially designed for searches through experimental masses obtained from mass spectrometry techniques.

BTN100 ontology

Base Topográfica Nacional 1:100.000 (BTN100) is a data catalogue of geographical data grouped by different areas, such as administrative units, protected areas, relief and hydrography.

The noise ontology was created to represent the acoustic pollution data collected by measurement stations located in cities, providing a common model for data publication.

The Context-Aware System Ontology and the Irrigation ontology are ontologies developed by the INRAE center of Clermont Auvergne Rhone-Alpes, France, in collaboration with OEG-UPM thanks to the ``WOW! Program Wide Open to the World - CAP 20-25 Visiting Scholar Fellowship''

The SAREF4INMA ontology was created as an extension of the SAREF ontology for the industry and manufacturing domain.

The BBCH-based Plant Phenological Description Ontology was developed in the context of the "Data to Knowledge in Agronomy and Biodiversity" and encondes the BBCH generic scale and each crop specific scales aligned one another.

The REACT Ontology, which is about renewable energy generation and storage, was developed in the context of the Horizon 2020 REACT project (grant agreement no. 824395).

A more detailed description of how these projects applied LOT can be downloaded here.

How to cite

If you are using the content of this website you must cite us as: Poveda-Villalón, M., Fernández-Izquierdo, A., Fernández-López, M., & García-Castro, R. (2022). LOT: An industrial oriented ontology engineering framework. Engineering Applications of Artificial Intelligence, 111, 104755.