Generative Knowledge Graph Construction (KGC) refers to those methods that leverage the
sequence-to-sequence framework for building knowledge graphs, which is flexible and can …

Abstract Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that combining
deep learning with symbolic reasoning will lead to stronger AI than either paradigm on its …

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 graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …

Semantic processing is a fundamental research domain in computational linguistics. In the
era of powerful pre-trained language models and large language models, the advancement …

Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier
reasoning. A desired subgraph is crucial as a small one may exclude the answer but a large …

Current high-performance semantic segmentation models are purely data-driven sub-
symbolic approaches and blind to the structured nature of the visual world. This is in stark …

Knowledge base question answering (KBQA) aims to answer a question over a knowledge
base (KB). Early studies mainly focused on answering simple questions over KBs and …

In recent years, neural systems have demonstrated highly effective learning ability and
superior perception intelligence. However, they have been found to lack effective reasoning …

Reasoning over natural language is a long-standing goal for the research community.
However, studies have shown that existing language models are inadequate in reasoning …