Neuro-Symbolic Frameworks: Conceptual Characterization and Empirical Comparative Analysis

2025-09-08

AI Summary

Neuro-symbolic (NeSy) research suffers from fragmented frameworks, lack of standardized tooling, and high development barriers—most existing efforts prioritize algorithmic innovations over declarative modeling support. This paper introduces the first multidimensional analytical framework for NeSy systems, systematically comparing DeepProbLog, Scallop, and DomiKnowS along three axes: symbolic representation formalisms, neural integration mechanisms, and differentiable inference capabilities. We unify probabilistic programming, differentiable logical reasoning, and declarative modeling within a coherent computational architecture, exposing fundamental limitations in expressive power and problem-solving scope across these frameworks. Our analysis provides principled guidance for NeSy framework selection and advances the design of unified, interpretable, data-efficient, and user-friendly NeSy platforms.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 - extit{DeepProbLog}, extit{Scallop}, and extit{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.

Problem

• Characterizing technical facets of existing neurosymbolic frameworks
• Addressing lack of user-friendly tools and unifying frameworks
• Identifying expressivity of frameworks in solving diverse problems

Innovation

• Combines neural learning with symbolic reasoning
• Uses DeepProbLog Scallop DomiKnowS frameworks
• Enhances explainability and data efficiency