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 graphs play a vital role in numerous artificial intelligence tasks, yet they
frequently face the issue of incompleteness. In this study, we explore utilizing Large …

Introduction Constructing an accurate and comprehensive knowledge graph of specific
diseases is critical for practical clinical disease diagnosis and treatment, reasoning and …

Neural symbolic knowledge graph (KG) reasoning offers a promising approach that
combines the expressive power of symbolic reasoning with the learning capabilities inherent …

Abstract Multimodal Argument Mining (MAM) is a recent area of research aiming to extend
argument analysis and improve discourse understanding by incorporating multiple …

The integration of Large Language Models (LLM) with Knowledge Representation Learning
(KRL) signifies a significant advancement in the field of artificial intelligence (AI), enhancing …

Sparse Knowledge Graphs (KGs), frequently encountered in real-world applications, contain
fewer facts in the form of (head entity, relation, tail entity) compared to more populated KGs …

Background: As fundamental documents in clinical trials, clinical trial protocols are intended
to ensure that trials are conducted according to the objectives set by researchers. The …

In recent decades, causal inference has been a significant research topic in various fields,
including statistics, computer science, education, public policy, and economics. Most causal …

Abstract Knowledge graph completion (KGC) involves inferring missing entities or
relationships within a knowledge graph, playing a crucial role across various domains …