Knowledge Graphs of Driving Scenes to Empower the Emerging Capabilities of Neurosymbolic AI

In the era of generative artificial intelligence (AI), neurosymbolic AI is emerging as a powerful approach for tasks spanning from perception to cognition. The use of neurosymbolic AI has been shown to achieve enhanced capabilities, including improved grounding, alignment, explainability, and reliability. However, due to its nascent stage, there is a lack of widely available real-world benchmark datasets that are tailored to neurosymbolic AI tasks. To address this gap and support the evaluation of current and future methods, we introduce DSceneKG (Driving Scenes Knowledge Graph), a suite of KGs of driving scenes that is built from real-world, high-quality scenes from multiple open autonomous driving datasets. In this article, we detail the construction process of DSceneKG and highlight its application in seven different tasks.

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Publication Details

Journal
IEEE Internet Computing
Published
2024-11-01
DOI
https://doi.org/10.1109/mic.2024.3494972
Citations
3
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
1.99

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article

Knowledge Graphs of Driving Scenes to Empower the Emerging Capabilities of Neurosymbolic AI

Amit Sheth, Cory Henson, Ruwan Wickramarachchi
3 citations
IEEE Internet Computing
Cell Image Analysis Techniques
1.99
article

Knowledge Graphs of Driving Scenes to Empower the Emerging Capabilities of Neurosymbolic AI

Amit Sheth, Cory Henson, Ruwan Wickramarachchi
article en
3 citations

Abstract

In the era of generative artificial intelligence (AI), neurosymbolic AI is emerging as a powerful approach for tasks spanning from perception to cognition. The use of neurosymbolic AI has been shown to achieve enhanced capabilities, including improved grounding, alignment, explainability, and reliability. However, due to its nascent stage, there is a lack of widely available real-world benchmark datasets that are tailored to neurosymbolic AI tasks. To address this gap and support the evaluation of current and future methods, we introduce DSceneKG (Driving Scenes Knowledge Graph), a suite of KGs of driving scenes that is built from real-world, high-quality scenes from multiple open autonomous driving datasets. In this article, we detail the construction process of DSceneKG and highlight its application in seven different tasks.

IEEE Internet ComputingVol. 28(6)
University of South Carolina (US), Robert Bosch (United States) (US)
National Science Foundation
Openalex Percentile: Top 11%
Cell Image Analysis Techniques
1.99
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