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 …

Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the
field of natural language processing and artificial intelligence, due to their emergent ability …

Knowledge Representation and Reasoning is a central, longstanding, and active area of
Artificial Intelligence. Over the years it has evolved significantly; more recently it has been …

Knowledge representation learning techniques process the knowledge graph, embedding
entities and relationships into a continuous dense low-dimensional vector space, and …

Tabular representation learning has recently gained a lot of attention. However, existing
approaches only learn a representation from a single table, and thus ignore the potential to …

Most knowledge graph completion (KGC) methods rely solely on structural information, even
though a large number of publicly available KGs contain additional temporal (validity time …

Purpose Despite ongoing research into archival metadata standards, digital archives are
unable to effectively represent records in their appropriate contexts. This study aims to …

Although Knowledge Graphs (KGs) are widely used, they suffer from hosting false
information. In the literature, many studies have been carried out to eliminate this deficiency …

Safety limitations in service robotics across various industries have raised significant
concerns about the need for robust mechanisms ensuring that robots adhere to safe …

While Knowledge Graphs (KGs) have long been used as valuable sources of structured
knowledge, in recent years, KG embeddings have become a popular way of deriving …