Large Language Models (LLMs) have taken Knowledge Representation--and the world--by
storm. This inflection point marks a shift from explicit knowledge representation to a renewed …

Language modeling studies the probability distributions over strings of texts. It is one of the
most fundamental tasks in natural language processing (NLP). It has been widely used in …

Many mathematical models have been leveraged to design embeddings for representing
Knowledge Graph (KG) entities and relations for link prediction and many downstream tasks …

Machine learning (ML), especially deep neural networks, has achieved great success, but
many of them often rely on a number of labeled samples for supervision. As sufficient …

Conceptualization, or viewing entities and situations as instances of abstract concepts in
mind and making inferences based on that, is a vital component in human intelligence for …

Dialogue summarization is abstractive in nature, making it suffer from factual errors. The
factual correctness of summaries has the highest priority before practical applications. Many …

Knowledge graph (KG) refinement refers to the process of filling in missing information,
removing redundancies, and resolving inconsistencies in KGs. With the growing popularity …

For many years, link prediction on knowledge graphs (KGs) has been a purely transductive
task, not allowing for reasoning on unseen entities. Recently, increasing efforts are put into …

Abstract Knowledge graph completion (KGC) can enhance the completeness of the
knowledge graph (KG). Traditional transductive KGC assumes that all entities and relations …

Abstract Knowledge graphs (KG) are used for many downstream tasks in artificial
intelligence (AI). However, owing to accuracy issues associated with information extraction …