Knowledge Engineering in the Age of Neurosymbolic Systems

Abstract

The field of knowledge engineering is experiencing a substantial impact from the rapid growth and widespread adoption of Neurosymbolic Systems (NeSys). In this paper, we investigate how NeSys are already used in knowledge engineering practices leading to the emergence of the new area of neurosymbolic knowledge engineering. To that end, we apply a data-driven analysis based on data collected in a large scale Systematic Mapping Study about systems used to create knowledge resource by employing NeSy approaches that combine Machine Learning and Semantic Web components. We characterise several aspects of this novel field, including specific approaches to knowledge engineering with NeSys identified from the data, the maturity of these systems as well as the main Machine Learning and Semantic Web modules used. Additionally, we also provide concrete examples of neurosymbolic knowledge engineering systems. We conclude with an overview of research challenges such as the need for new methodologies, increased auditability, and considering the impact of human users in neurosymbolic knowledge engineering.