This paper describes an approach to represent terminologies in the machine-readable format of the Semantic Web, which improves the interoperability between terminological resources and opens up new possibilities yet to be discovered. The study’s motivation stems from the realization that the existing formalisms, such as SKOS or OntoLex-lemon, might not adequately capture the information within authoritative terminological resources. Therefore, we identified model requirements by formulating a set of Competency Questions derived from the analysis of terminological resources across various fields and domains, in line with the ontology development methodologies adopted in this work. During this analysis, we faced different representation challenges such as the various sources of term descriptions and the quality indicators related to terms. Consequently, we propose Termlex, a proposal based on the OntoLex-lemon model that combines the conceptual structure of the SKOS model with the lexical information as modelled in OntoLex-lemon. In Termlex, we define new classes and properties to cover the specific needs of terminological resources coming from a variety of approaches. The paper concludes with the instantiation of the Termlex model through three different use cases that follow different modelling approaches as a validation attempt.
This paper describes an approach to represent terminologies in the machine-readable format of the Semantic Web, which improves the interoperability between terminological resources and opens up new possibilities yet to be discovered. The study’s motivation stems from the realization that the existing formalisms, such as SKOS or OntoLex-lemon, might not adequately capture the information within authoritative terminological resources. Therefore, we identified model requirements by formulating a set of Competency Questions derived from the analysis of terminological resources across various fields and domains, in line with the ontology development methodologies adopted in this work. During this analysis, we faced different representation challenges such as the various sources of term descriptions and the quality indicators related to terms. Consequently, we propose Termlex, a proposal based on the OntoLex-lemon model that combines the conceptual structure of the SKOS model with the lexical information as modelled in OntoLex-lemon. In Termlex, we define new classes and properties to cover the specific needs of terminological resources coming from a variety of approaches. The paper concludes with the instantiation of the Termlex model through three different use cases that follow different modelling approaches as a validation attempt. Read More
Related posts:
Towards the definition of an information quality metric for information fusion models
Extreme Low-Visibility Events Prediction Based on Inductive and Evolutionary Decision Rules: An Explicability-Based Approach
Representing multilingual terminologies with OntoLex-Lemon
An ontology-based deep learning approach for triple classification with out-of-knowledge-base entities
Topic-oriented text features can match visual deep models of video memorability
A case-based reasoning model powered by deep learning for radiology report recommendation