  In this paper, we present our position for a neuralsymbolic 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 representing AI models in general, allowing to describe both
nonsymbolic 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.
