Network embedding plays a significant role in a variety of applications. To capture the
topology of the network, most of the existing network embedding algorithms follow a …

Knowledge graphs (KGs) have been widely used to enhance complex question answering
(QA). To understand complex questions, existing studies employ language models (LMs) to …

Finding the proper depth $ d $ of a graph convolutional network (GCN) that provides strong
representation ability has drawn significant attention, yet nonetheless largely remains an …

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 …

Finding node correspondence across networks, namely multi-network alignment, is an
essential prerequisite for joint learning on multiple networks. Despite great success in …

Knowledge graph question answering aims to identify answers of the query according to the
facts in the knowledge graph. In the vast majority of the existing works, the input queries are …

This paper investigates the few-shot temporal reasoning capability within the hyperbolic
space. The goal is to forecast future events for newly emerging entities within temporal …

Question answering over knowledge graph (KGQA) aims at answering natural language
questions posed over knowledge graphs (KGs). Moreover, multi-hop KGQA requires …

Knowledge graphs (KGs), which store an extensive number of relational facts (head,
relation, tail), serve various applications. While many downstream tasks highly rely on the …

Few-shot question answering (QA) aims at precisely discovering answers to a set of
questions from context passages while only a few training samples are available. Although …