This pattern for re-engineering non-ontological resources (PR-NOR) fits in the Schema Re-engineering Category proposed by [3]. The pattern defines a procedure that transforms the classification scheme components into ontology representational primitives. This pattern comes from the experience of ontology engineers in developing ontologies using classification schemes in several projects (SEEMP1, NeOn2, and Knowledge Web3). The pattern is included in a pool of patterns, which is a key element of our method for re-engineering non-ontological resources into ontologies [2]. The patterns generate the ontologies at a conceptualization level, independent of the ontology implementation language.
Re-engineering a classification scheme, which follows the adjacency list model, to design a taxonomy.
scheme which follows the adjacency list model. A classification scheme is a rooted tree of concepts, in which each concept groups entities by some particular degree of similarity.
The semantics of the hierarchical relation between
parents and children concepts may vary depending
of the context. The adjacency list data model [
onomy architectural pattern (AP-TX-01) [
Each category in the classification scheme is mapped to a class, and the semantics of the relationship between children and parent categories are mapped to subClassOf relations. Suppose that someone wants to build an ontology based on the water areas classification published by FAO. This classification scheme follows the adjacency list data model.
them according to some different criteria as environment, statistics, and jurisdiction, among others. This classification scheme is available at http://www.fao.org/figis/servlet/RefServlet 1. Create the Water area class. 2. Create the Environmental area class, and set up the subClassOf relation between the Environmental area class and the Water area class. 2.1. Create the Inland/marine class, and set up the subClassOf relation between the Inland/marine class and the Environmental area class. 2.2. Create the Ocean class, and set up the subClassOf relation between the Ocean class and the Environmental area class. 2.3. Create the North/South/Equatorial class, and set up the subClassOf relation between the North/South/Equatorial class and the Environmental area class. 3. Create the Fishing Statistical area class, and set up the subClassOf relation between the Fishing Statistical area class and the Water area class. 3.1. Create the FAO statistical area class, and set up the subClassOf relation between the FAO statistical area class and the Fishing Statistical area class. 3.2. Create the Areal grid system class, and set up the subClassOf relation between the Areal grid system class and the Fishing Statistical area class. 4. Create the Jurisdiction area class, and set up the subClassOf relation between the Jurisdiction area class and the Water area class.
This pattern is related to the architectural pattern TX-AP-01 [
This pattern was applied to re-engineer the ISTAT4, geography italian standard, into a Geography Ontology5, within the context of the SEEMP project. This standard is a classification scheme which consists of 4 divisions, 20 regions and 106 provinces. ISTAT is modelled following the adjacency list data model. Because of the number of divisions, regions and provinces of the ISTAT standard, it was not practical to create the ontology manually. Therefore, we created an 4 http://www.istat.it/ 5 The ontology is available at http://droz.dia.fi.upm.es/hrmontology/ ad-hoc wrapper, implemented in Java, that reads the data from the resource implementation and automatically creates the corresponding elements of the new ontology following the suggestion given by the pattern. 4
We have presented a pattern for transforming a classification scheme, which is
modelled following the adjacency list data model, into a taxonomy. The pattern
is included in a pool of patterns, which is a key element of our method for
re-engineering non-ontological resources into ontologies [
We plan to develop software libraries within a framework that implement the transformation process suggested by the pattern. Moreover, we will include external resources to improve the quality of the resultant ontologies. Finally, we need to calculate how much effort do we save re-engineering classification schemes using patterns compared with re-engineering classification schemes without them. Acknowledgments. This work has been partially supported by the European Comission projects NeOn(FP6-027595) and SEEMP(FP6-027347), as well as by an R+D grant from the UPM.