Effective Integration of Symbolic and Connectionist Approaches through a Hybrid Representation
This work addresses the problem of AI model integration and traceability for researchers and practitioners, but it appears incremental as it builds on existing neuralsymbolic integration strategies.
The paper tackles the challenge of integrating symbolic and connectionist AI approaches by proposing a hybrid representation that can describe both types of knowledge and their processors, along with workflows for traceability throughout the model lifecycle.
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.