Product Lifecycle Management (PLM) has always required robust solutions for representing product data models. Product data models enable information exchange across different organizations, actors, processes and stages in the product lifecycle. In this context, standardization of models plays a key role, since it ensures interoperability between the different systems that support information exchange. These standard models need to support diverse domain-specific requirements from the multitude of disciplines involved during a product's lifecycle. Due to this diversity, challenges are to (1) develop multidisciplinary reusable models, (2) extend them to support new requirements over time (new products, new regulations, new materials, new processes) and (3) implement the resulting gigantic information models. ISO 10303, the reference standard for PLM-related data models provides two mechanisms that enable specialization of generic product data to address some of these challenges. In this paper we introduce the need for dynamic PLM-related information models, detail the existing ISO 10303 method and identify its limitations. We then present a methodology for enhancing that method using the Web Ontology Language (OWL) and ontologies for representing product data models and the SPARQL Inference Notation (SPIN), a new Semantic Web technology, for validating product data and overcoming OWL limitations.