Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the
semantic web community's exploration into multi-modal dimensions unlocking new avenues …

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 …

Abstract Knowledge graphs, modeling multi-relational data, improve numerous applications
such as question answering or graph logical reasoning. Many graph neural networks for …

Recent advancements have witnessed the ascension of Large Language Models (LLMs),
endowed with prodigious linguistic capabilities, albeit marred by shortcomings including …

Critical information infrastructure (CII) is a critical component of national socioeconomic
systems and one of the primary targets of cyberattacks. Unfortunately, CII's security …

The advancement of Multi-modal Pre-training highlights the necessity for a robust Multi-
Modal Knowledge Graph (MMKG) representation learning framework. This framework is …

The task of inductive link prediction in (discrete) attributed multigraphs infers missing
attributed links (relations) between nodes in new test multigraphs. Traditional relational …

How to quickly obtain information about crisis events on social media such as Twitter and
Weibo is crucial for follow-up rescue work and the promotion of postdisaster reconstruction …

Abstract Domain-specific knowledge can significantly contribute to addressing a wide variety
of vision tasks. However, the generation of such knowledge entails considerable human …

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 …