Towards a Semantic Approach for Linked Dataspace, Model and Data Cards

The vast majority of artifcial intelligence practitioners overlook the importance of documentation when building and publishing models and datasets. However, due to the recent trend in the explainability and fairness of AI models, several frameworks have been proposed such as Model Cards, and Data Cards, among others, to help in the appropriate re-usage of those models and datasets. In addition, because of the introduction of the dataspace concept for similar datasets in one place, there is potential that similar Model Cards, Data Cards, Service Cards, and Dataspace Cards can be linked to extract helpful information for better decision-making about which model and data can be used for a specifc application. This paper reviews the case for considering a Semantic Web approach for exchanging Model/Data Cards as Linked Data or knowledge graphs in a dataspace, making them machine-readable. We discuss the basic concepts and propose a schema for linking Data Cards and Model Cards within a dataspace. In addition, we introduce the concept of a dataspace card which can be a starting point for extracting knowledge about models and datasets in a dataspace. This helps in building trust and reuse of models and data among companies and individuals participating as publishers or consumers of such assets.