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Survey on Applications of Neurosymbolic Artificial Intelligence

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arxiv 2209.12618 v1 pith:GFMW4AFF submitted 2022-09-08 cs.AI cs.SC

classification cs.AIcs.SC
keywords applicationsneurosymbolicartificialfieldintelligencereasoningrecentvarious
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In recent years, the Neurosymbolic framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance. This success is due to its stellar performance combined with attractive properties, such as learning and reasoning. The new emerging Neurosymbolic field is currently experiencing a renaissance, as novel frameworks and algorithms motivated by various practical applications are being introduced, building on top of the classical neural and reasoning problem setting. This article aims to provide a comprehensive review of significant recent developments in real-world applications of Neurosymbolic Artificial Intelligence. Specifically, we introduce a taxonomy of common Neurosymbolic applications and summarize the state-of-the-art for each of those domains. Furthermore, we identify important current trends and provide new perspectives pertaining to the future of this burgeoning field.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Neuro-Symbolic Frameworks: Conceptual Characterization and Empirical Comparative Analysis

    cs.AI 2025-09 conditional novelty 5.0 of 10

    A facet-based comparison of DeepProbLog, Scallop, and DomiKnowS with efficiency measurements on four toy tasks, identifying challenges for future neurosymbolic frameworks.

  2. Neuro-Symbolic AI in 2024: A Systematic Review

    cs.AI 2025-01 conditional novelty 4.0 of 10

    A systematic review of 158 Neuro-Symbolic AI papers finds research concentrated in learning and inference, with explainability, trustworthiness, and Meta-Cognition as underrepresented gaps.

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