Integration of numeric and symbolic information for semantic image interpretation

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Logic Tensor Networks for Semantic Image Interpretation (2017, methodology): “Thus, recent SII systems have tried to combine, or even integrate, visual features obtained from data with symbolic knowledge in the form of logical axioms [Zhu et al., 2014; Chen et al., 2012; Donadello and Serafini, 2016].”
From LSAT: The Progress and Challenges of Complex Reasoning (2021, background): “They design a set of rules or templates to identify basic units for reasoning as symbols, such as the quantities and arithmetic signs for numerical reasoning [54, 55, 56], relevant triples and paths from knowledge graph for commonsense reasoning [57], etc.”
Semantic Image Interpretation - Integration of Numerical Data and Logical Knowledge for Cognitive Vision (2018, unknown): “A1 The knowledge bases and the datasets for the experiments in [27]: https://dkm.”