Document-level relation extraction (DocRE) poses the challenge of identifying relationships
between entities within a document. Existing approaches rely on logical reasoning or …

Neurosymbolic artificial intelligence (AI) is an increasingly active area of research that
combines symbolic reasoning methods with deep learning to leverage their complementary …

Researchers are working towards automation of ontology construction to manage the ever-
growing data on the web. Currently, there is a shift from the use of machine learning …

Explainability is an essential reason limiting the application of neural networks in many vital
fields. Although neuro-symbolic AI hopes to enhance the overall explainability by leveraging …

Neuro-symbolic AI is an effective method for improving the overall performance of AI models
by combining the advantages of neural networks and symbolic learning. However, there are …

Relation extraction (RE) is a well-known NLP application often treated as a sentence-or
document-level task. However, a handful of recent efforts explore it across documents or in …

Relation extraction is the task of extracting relationships from input text, where input can be a
sentence, document, or multiple documents. This task has been popular for decades and is …