With the rapid proliferation of scientific literature, versatile academic knowledge services
increasingly rely on comprehensive academic graph mining. Despite the availability of …

Language models have shown promising performance on the task of translating natural
language questions into SQL queries (Text-to-SQL). However, most of the state-of-the-art …

Graph Foundation Model (GFM) is a new trending research topic in the graph domain,
aiming to develop a graph model capable of generalizing across different graphs and tasks …

Existing graph neural networks (GNNs) are mostly applied to representation scenes with
complete graph structure. However, the graph structures of complex systems from the real …

Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for deep learning on
heterogeneous graphs. Typical HGNNs require repetitive message passing during training …

Knowledge graphs (KGs) have achieved significant attention in recent years, particularly in
the area of the Semantic Web as well as gaining popularity in other application domains …

Heterogeneous Graph Neural Networks (HGNNs) have achieved promising results in
various heterogeneous graph learning tasks, owing to their superiority in capturing the …

Entity linking, a crucial task in the realm of natural language processing, aims to link entity
mentions in a text to their corresponding entities in the knowledge base. While long …

Over 55% of author names in PubMed are ambiguous: the same name is shared by different
individual researchers. This poses significant challenges on precise literature retrieval for …

The increasing number of academic practitioners has resulted in a significantly increased
volume of scientific papers, attracting considerable interest among researchers examining …