Effective Integration of Symbolic and Connectionist Approaches through a Hybrid Representation

In this paper, we present our position for a neural - symbolic integration strategy, arguing in favor of a hybrid representation to promote an effective integration. Such description differs from others fundamentally, since its entities aim at repre senting AI models in general, allowing to describe both non - symbolic and symbolic knowledge, the integration between them and their corresponding processors. Moreover, the entities also support representing workflows, leveraging traceability to keep track of every change applied to models and their related entities (e.g., data or concepts) throughout the lifecycle of the models.