Pathway toward prior knowledge-integrated machine learning in engineering

However, and data-driven approaches have existed in parallel, these pre-defined, symbolic-based rigid clarity limits their mirroring the ongoing AI debate on symbolism versus ability to handle cases that lack information definition connectionism. Research for process development to context or do not fit the rigid rule constraints. Such integrate both sides to transfer and utilize domain inflexible architectures and organizational limitations knowledge in the data-driven process is rare. This study become increasingly pronounced in the face of objectives emphasizes efforts and prevailing trends to integrate in multidisciplinary challenges that demand more multidisciplinary domain professions into machine comprehensive and nuanced definitions of system acknowledgeable, data-driven processes in a two-fold modeling, such as sustainability (Westermann and Evins organization: examining information uncertainty sources 2019). The advantages of pre-defined, context-based rules in knowledge representation and exploring knowledge transform into significant drawbacks of performing decomposition with a three-tier knowledge-integrated efficient searching, manipulating, and validating elements machine learning paradigm.