With the growth of data and intelligent technologies, the healthcare sector opened numerous
technology that enabled services for patients, clinicians, and researchers. One major hurdle …

In recent years, we have witnessed the proliferation of knowledge graphs (KG) in various
domains, aiming to support applications like question answering, recommendations, etc. A …

Modern graphs exert colossal time and space pressure on graph analytics applications. In
2022, Facebook social graph reaches 2.91 billion users with trillions of edges. Many …

Graph neural networks have been widely used to learn node representations for many real-
world static graphs. In general, they learn node representations by recursively aggregating …

Real-world applications frequently seek to solve a general form of the Entity Matching (EM)
problem to find associated entities. Such scenarios include matching jobs to candidates in …

Abstract Biomedical Entity Linking (BM-EL) task, which aims to match biomedical mentions
in articles to entities in a certain knowledge base (eg, the Unified Medical Language …

In this article, we investigate the degree of explainability of graph neural networks (GNNs).
The existing explainers work by finding global/local subgraphs to explain a prediction, but …

Many recent studies of Entity Alignment (EA) use Graph Neural Networks (GNNs) to
aggregate the neighborhood features of entities and achieve better performance. However …

In this work, we use semantic knowledge sources, such as cross-domain knowledge graphs
(KGs) and domain-specific ontologies, to enrich structured data for various AI applications …

Recently, graph contrastive learning proposes to learn node representations from the
unlabeled graph to alleviate the heavy reliance on node labels in graph neural networks …