Special issue: NeSy 2025 Extended Papers To the Editors, We wish to submit our scientific article, titled "Neuro-LENS: a neuro-symbolic framework integrating incomplete background knowledge and deep learning." Our work extends the paper accepted to NeSy 2025, which proposed a neuro-symbolic approach for handling uncertainty in image scene classification, by combining modal logic and evidence theory with deep learning models. Here, we insert our previous work into a wider theoretical framework which investigates the integration of a neural component and an evidence-based symbolic component. We present three different strategies: neural-to-symbolic chaining, where the neural component extracts features to be fed to the symbolic component, symbolic-to-neural chaining, where the symbolic component generates additional features for the neural component, and parallel neural and symbolic integration, where both components produce features which are exploited in the clauses of logical rules. The strategies which are introduced in this paper are applied to tabular and time series data, demonstrating the adaptability of the framework to different use cases and data types. We believe that this manuscript contributes to the field by investigating the potential of integrating deep learning and evidence theory, allowing to tackle commonly faced challenges in real-world datasets, e.g. incomplete knowledge, data scarcity and data imbalance. We confirm that this manuscript has not been published elsewhere and is not under consideration by another journal. The authors have no conflict of interest to declare. Thank you for considering our work, we look forward to your feedback. Kind regards, Giulia Murtas (on behalf of all co-authors)