Multimodal knowledge graphs (MKGs), which intuitively organize information in various
modalities, can benefit multiple practical downstream tasks, such as recommendation …

Statistical learners are leading towards auto-epistemic logic, but is it the right way to
progress in artificial intelligence (AI)? Ways to discover AI fit the senses and the intellect. The …

Knowledge Graph Completion (KGC) is crucial for addressing knowledge graph
incompleteness and supporting downstream applications. Many models have been …

Knowledge graphs (KG) are essential background knowledge providers in many tasks.
When designing models for KG-related tasks, one of the key tasks is to devise the …

Temporal Knowledge Graph (TKG) reasoning often involves completing missing factual
elements along the timeline. Although existing methods can learn good embeddings for …

Graph neural networks (GNNs), which effectively use topological structures in the
knowledge graphs (KG) to embed entities and relations in low-dimensional spaces, have …

A few models have tried to tackle the link prediction problem, also known as knowledge
graph completion, by embedding knowledge graphs in comparably lower dimensions …

Link prediction for knowledge graphs aims to obtain missing nodes in triples. In recent years,
link prediction methods have made specific achievements in knowledge graph embedding …

Tables contrast with unstructured text data by its structure to organize the information. In this
paper, we investigate the efficiency of various LLMs in interpreting tabular data through …

Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional
spaces and have become the de-facto standard for knowledge graph completion. Most …