Tools/PCS2OWL

= Deriving Valid OWL Ontologies from Product and Services Classification Standards =

PCS2OWL is a generic method and toolset for deriving valid OWL ontologies from product classification standards and catalog group systems. The resulting ontologies are compatible with the GoodRelations ontology and can be used to publish rich product model master data and offering information on the Semantic Web.

To date, multiple categorization standards and proprietary category systems are supported. A list of available product ontologies for which parser modules are provided is available at

Implementation


PCS2OWL uses a generic architecture with pluggable import/parser modules. This way the tool is able to handle various classification standards in parallel taking advantage of a single common code base for data processing and serialization. Each newly added classification standard or catalog group system requires a new import module to be supplied/implemented.

PCS2OWL applies the GenTax algorithm for deriving consistent OWL ontologies from hierarchical classification as found in product classification standards and catalog group systems. Similar approaches have already been described in and. This project is an advancement of.

In addition to the extraction of OWL classes from hierarchical classifications, PCS2OWL implements a method to automatically discriminate different feature types and create OWL properties from them (object properties (quantitative or qualitative nature), datatype properties).

The converter allows for hash-based or slash-based deployments of the derived ontologies.This either creates a bunch of mini RDF files or a single big RDF dump. Moreover, the tool creates a navigable documentation consisting of a set of HTML pages.

Optionally, hierarchy keys can be obfuscated using hash keys if for example competitive reasons require a classification standard to protect class and property identifiers.

Acknowledgements
PCS2OWL is available under the terms of the GNU Lesser General Public License. The work on this project has been supported by the German Federal Ministry of Research (BMBF) by a grant under the KMU Innovativ program as part of the Intelligent Match project (FKZ 01IS10022B).

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