Recent years have seen impressive performance of transformer-based models on different
natural language processing tasks. However, it is not clear to what degree the transformers …

Deep graph learning has achieved remarkable progresses in both business and scientific
areas ranging from finance and e-commerce, to drug and advanced material discovery …

Neuro-Symbolic Artificial Intelligence–the combination of symbolic methods with methods
that are based on artificial neural networks–has a long-standing history. In this article, we …

This paper studies learning logic rules for reasoning on knowledge graphs. Logic rules
provide interpretable explanations when used for prediction as well as being able to …

Neural link predictors are immensely useful for identifying missing edges in large scale
Knowledge Graphs. However, it is still not clear how to use these models for answering …

When prompted with a few examples and intermediate steps, large language models (LLMs)
have demonstrated impressive performance in various reasoning tasks. However, prompting …

Deep learning and symbolic reasoning are complementary techniques for an intelligent
system. However, principled combinations of these techniques have limited scalability …

Recent work on neuro-symbolic inductive logic programming has led to promising
approaches that can learn explanatory rules from noisy, real-world data. While some …

While large language models (LLMs), such as GPT-3, appear to be robust and general, their
reasoning ability is not at a level to compete with the best models trained for specific natural …

Recent research suggests that systematic generalization in natural language understanding
remains a challenge for state-of-the-art neural models such as Transformers and Graph …