Abstract Recent work has shown learning systems can use logical background knowledge to compensate for a lack of labeled training data. Many methods by creating loss function that encodes this knowledge. However, often the logic is discarded after training, even if it still helpful at test time. Instead, we ensure neural network predictions satisfy refining with an extra computation step. We in...

We provide a wide-ranging study of the scenario where subset relations in relational vocabulary is visible to user—that is, their complete contents are known—while remaining invisible. also have background theory—invariants given by logical sentences—that may relate invisible ones, and constrain both isolation. want determine whether some other information, as positive existential formula, can ...

Abstract Specialized transformers-based models (such as BioBERT and BioMegatron) are adapted for the biomedical domain based on publicly available corpora. As such, they have potential to encode large-scale biological knowledge. We investigate encoding representation of knowledge in these models, its utility support inference cancer precision medicine—namely, interpretation clinical significanc...

The use of external background knowledge can be beneficial for the task matching schemas or ontologies automatically. In this paper, we exploit six general-purpose graphs as sources task. are evaluated by applying three different exploitation strategies. We find that explicit strategies still outperform latent ones and choice strategy has a greater impact on final alignment than actual dataset ...