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On the Integration of Knowledge Graphs into Deep Learning Models for a More Comprehensible AI—Three Challenges for Future Research
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1006/ijhc.1995.1081openalex-snapshot
Toward principles for the design of ontologies used for knowledge sharing?
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Choose Your Neuron: Incorporating Domain Knowledge Through Neuron-Importance
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Learning representations by back-propagating errors
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Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems
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Interpreting Black-Box Classifiers Using Instance-Level Visual Explanations
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Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
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Marrying Uncertainty and Time in Knowledge Graphs
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Explainable Cross-Domain Recommendations Through Relational Learning
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Linked Data: Evolving the Web into a Global Data Space
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Explicit Knowledge-based Reasoning for Visual Question Answering
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Semantic Clustering Using Multiple Ontologies
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The knowledge graph as the default data model for learning on heterogeneous knowledge
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DBpedia – A large-scale, multilingual knowledge base extracted from Wikipedia
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Semantic Web for the Legal Domain: The next step
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DeepTox: Toxicity Prediction using Deep Learning
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Sanity Checks for Saliency Maps
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Artificial Intelligence
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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