| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 509033 | Computers in Industry | 2014 | 8 Pages |
•Most of the ontologies developed for manufacturing, appeared as the result of intuitive or non methodological processes.•Current ontological methodologies proposed to date emphasize in development from scratch.•Reusing was stressed in our case, thus previous ontologies were considered, beside of the current discarding approach.•With the proposed methodology we fill the gap between intuitive and methodological development.•A large set of data about machines was analyzed by means of text crawling tools.
Adapting to user's requirements is a key factor for enterprise success. Despite the existence of several approaches that point in this direction, simplifying integration and interoperability among users, suppliers and the enterprise during product lifecycle, is still an open issue. Ontologies have been used in some manufacturing applications and they promise to be a valid approach to model manufacturing resources of enterprises (e.g. machinery and raw material). Nevertheless, in this domain, most of the ontologies have been developed following methodologies based on development from scratch, thus ontologies previously developed have been discarded. Such ontological methodologies tend to hold the interoperability issues in some level. In this paper, a method that integrates ontology reuse with ontology validation and learning is presented. An upper (top-level) ontology for manufacturing was used as a reference to evaluate and to improve specific domain ontology. The evaluation procedure was based on the systemic methodology for ontology learning (SMOL). As a result of the application of SMOL, an ontology entitled Machine of a Process (MOP) was developed. The terminology included in MOP was validated by means of a text mining procedure called Term Frequency–Inverse Document Frequency (TF–IDF) which was carried out on documents from the domain in this study. Competency questions were performed on preexisting domain ontologies and MOP, proving that this new ontology has a performance better than the domain ontologies used as seed.
