Toward a Clearer Characterization of Neuro-Symbolic Frameworks: A Brief Comparative Analysis

Sania Sinha, Tanawan Premsri, Parisa Kordjamshidi
Proceedings of The 19th International Conference on Neurosymbolic Learning and Reasoning, PMLR 284:179-217, 2025.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v284-sinha25a, title = {Toward a Clearer Characterization of Neuro-Symbolic Frameworks: A Brief Comparative Analysis}, author = {Sinha, Sania and Premsri, Tanawan and Kordjamshidi, Parisa}, booktitle = {Proceedings of The 19th International Conference on Neurosymbolic Learning and Reasoning}, pages = {179--217}, year = {2025}, editor = {H. Gilpin, Leilani and Giunchiglia, Eleonora and Hitzler, Pascal and van Krieken, Emile}, volume = {284}, series = {Proceedings of Machine Learning Research}, month = {08--10 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v284/main/assets/sinha25a/sinha25a.pdf}, url = {https://proceedings.mlr.press/v284/sinha25a.html}, 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.} }
Endnote
%0 Conference Paper %T Toward a Clearer Characterization of Neuro-Symbolic Frameworks: A Brief Comparative Analysis %A Sania Sinha %A Tanawan Premsri %A Parisa Kordjamshidi %B Proceedings of The 19th International Conference on Neurosymbolic Learning and Reasoning %C Proceedings of Machine Learning Research %D 2025 %E Leilani H. Gilpin %E Eleonora Giunchiglia %E Pascal Hitzler %E Emile van Krieken %F pmlr-v284-sinha25a %I PMLR %P 179--217 %U https://proceedings.mlr.press/v284/sinha25a.html %V 284 %X 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.
APA
Sinha, S., Premsri, T. & Kordjamshidi, P.. (2025). Toward a Clearer Characterization of Neuro-Symbolic Frameworks: A Brief Comparative Analysis. Proceedings of The 19th International Conference on Neurosymbolic Learning and Reasoning, in Proceedings of Machine Learning Research 284:179-217 Available from https://proceedings.mlr.press/v284/sinha25a.html.

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