This paper proposes a collaborative methodology for developing semantic data models. The proposed methodology for the semantic model development follows a “meet-in-the-middle” approach. On the one hand, the concepts emerged in a bottom-up fashion from analyzing the domain and interviewing the domain experts regarding their data needs. On the other hand, it followed a top-down approach whereby existing ontologies, vocabularies and data models were analyzed and integrated with the model. The identified elements were then fed to a multiphase abstraction exercise in order to get the concepts of the model. The derived model is also evaluated and validated by domain experts. The methodology is applied on the creation of the Cancer Chemoprevention semantic model that formally defines the fundamental entities used for annotating and describing inter-connected cancer chemoprevention related data and knowledge resources on the Web. This model is meant to offer a single point of reference for biomedical researchers to search, retrieve and annotate linked cancer chemoprevention related data and web resources. The model covers four areas related to Cancer Chemoprevention: i concepts from the literature that refer to cancer chemoprevention, ii facts and resources relevant for cancer prevention, iii collections of experimental data, procedures and protocols and iv concepts to facilitate the representation of results related to virtual screening of chemopreventive agents.
['Dimitris Zeginis', 'Ali Hasnain', 'Nikolaos Loutas', 'Helena F. Deus', 'Ronan Fox', 'Konstantinos A. Tarabanis']
A collaborative methodology for developing a semantic model for interlinking Cancer Chemoprevention linked-data sources