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In this paper, we present our position for a neuralsymbolic integrationstrategy, arguing in favor of a hybrid representation to promote an effectiveintegration. Such description differs from others fundamentally, since itsentities aim at representing AI models in general, allowing to describe bothnonsymbolic and symbolic knowledge, the integration between them and theircorresponding processors. Moreover, the entities also support representingworkflows, leveraging traceability to keep track of every change applied tomodels and their related entities (e.g., data or concepts) throughout thelifecycle of the models.

Effective Integration of Symbolic and Connectionist Approaches through a Hybrid Representation