Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

Mining valuable hidden knowledge from large-scale data relies on the support of reasoning
technology. Knowledge graphs, as a new type of knowledge representation, have gained …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Abstract Knowledge Graphs (KG) are multi-relational graphs consisting of entities as nodes
and relations among them as typed edges. Goal of the Question Answering over KG (KGQA) …

Large language models~(LLM) like ChatGPT have become indispensable to artificial
general intelligence~(AGI), demonstrating excellent performance in various natural …

In this paper, we study how to improve the zero-shot reasoning ability of large language
models~(LLMs) over structured data in a unified way. Inspired by the study on tool …

Existing question answering datasets focus on dealing with homogeneous information,
based either only on text or KB/Table information alone. However, as human knowledge is …

Knowledge base question answering (KBQA) aims to answer a question over a knowledge
base (KB). Recently, a large number of studies focus on semantically or syntactically …

State-of-the-art machine learning methods exhibit limited compositional generalization. At
the same time, there is a lack of realistic benchmarks that comprehensively measure this …

Entity alignment is the task of linking entities with the same real-world identity from different
knowledge graphs (KGs), which has been recently dominated by embedding-based …