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Neurosymbolic System Profiling: A Template-Based Approach

https://doi.org/10.2139/ssrn.4508529
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11/11 checkable references clean · checked 2026-09-05

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

24 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 11 checked references that resolve
resolves10.4018/978-1-59904-849-9.ch146
Knowledge-Based Systems
resolves10.1016/j.knosys.2021.106989
Regularizing deep networks with prior knowledge: A constraint-based approach
resolves10.1007/s10489-021-02394-3
Modular design patterns for hybrid learning and reasoning systems
resolves10.1145/2333112.2333115
Ontology learning from text
resolves10.1109/ACCESS.2018.2870052
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
resolves10.6028/NIST.IR.8312
Four principles of explainable artificial intelligence
resolves10.1609/aaai.v32i1.11491
Anchors: High-Precision Model-Agnostic Explanations
resolves10.1016/j.artint.2021.103627
Knowledge graphs as tools for explainable machine learning: A survey
resolves10.18653/v1/D16-1019
Jointly Embedding Knowledge Graphs and Logical Rules
resolves10.3233/AIC-1994-7104
Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches
resolves10.1145/3514221.3517887
Explaining Link Prediction Systems based on Knowledge Graph Embeddings
The 24 references without a DOI — listed, not checked
no DOI — not checkedIntegrated Rule-Based Learning and Inference
no DOI — not checkedKnowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning
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no DOI — not checkedNeurosymbolic ntegration: The knowledge level approach
no DOI — not checkedref10
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no DOI — not checkedA Boxology of Design Patterns forHybrid Learningand Reasoning Systems
no DOI — not checkedModels and guidelines for integrating expert systems and neural networks
no DOI — not checkedref16
no DOI — not checkedInformed Machine Learning -A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems
no DOI — not checkedref18
no DOI — not checkedInductive reasoning about ontologies using conceptual spaces
no DOI — not checkedref22
no DOI — not checkedref23
no DOI — not checkedref25
no DOI — not checkedref27
no DOI — not checkedA case-based reasoning model powered by deep learning for radiology report recommendation
no DOI — not checkedD. Degree in Artificial Intelligence (2022) with European Mention from the Universidad Polit�cnica de Madrid, Spain. She is currently an Assistant Lecturer at the ETSI Sistemas Inform�ticos, Universidad Polit�cnica de Madrid. She has also been a visiting researcher at the Vrije Universiteit of Amsterdam. Her prime fields of research include neurosymbolic integration, knowledge representation, deep learning, knowledge integration and explainability
no DOI — not checkedHis main research line is Social and Explainable Artificial Intelligence for Smart Cities. His scientific production includes more than 80 publications, highlighting more than 30 articles in the JCR. He lectures deep learning and social network analysis among other courses; and, has been principal investigator in four educational innovation projects in data science. He has also participated in several European and National funding programs such as
no DOI — not checkedHe has been a visiting researcher with the University of Sunderland and Trinity College of Dublin. He is currently a member of the artificial intelligence lab workgroup and an Associate Professor of computing with the Departmento de Inteligencia Artificial, UPM's School of Computing. His major fields of study and research are the subsymbolic artificial intelligence, its synergies with the symbolic domain, and diverse applications such as the medical area
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