A Comparative Analysis of NeSy Frameworks and What's Next?

Sania Sinha, Tanawan Premsri, Parisa Kordjamshidi

Keywords: Neurosymbolic, Comparing NeSy frameworks, Combining learning and reasoning

TL;DR: This paper chooses a few major and generic NeSy frameworks and tries to analyse and compare them. The goal is to provide insight about what is needed for the next generation of NeSy frameworks.

Abstract: Neurosymbolic (NeSy) frameworks combine neural representations and learning with symbolic representations and reasoning. Combining the reasoning capacities, explainability, and interpretability of symbolic processing with the flexibility and power of neural computing allows us to solve complex problems with more reliability while being data-efficient. However, this recently growing topic poses a challenge to developers with its learning curve, lack of user-friendly tools, libraries, and unifying frameworks. In this paper, we characterize the technical facets of existing NeSy frameworks, such as the symbolic representation language, integration with neural models, and the underlying algorithms. A majority of the NeSy research focuses on algorithms instead of providing generic frameworks for declarative problem specification to leverage problem solving. To highlight the key aspects of Neurosymbolic modeling, we showcase three generic NeSy frameworks - $\textit{DeepProbLog}$, $\textit{Scallop}$, and $\textit{DomiKnowS}$. We identify the challenges within each facet that lay the foundation for identifying the expressivity of each framework in solving a variety of problems. Building on this foundation, we aim to spark transformative action and encourage the community to rethink this problem in novel ways.

Track: Main Track

Paper Type: Long Paper

Resubmission: No

Submission Number: 47