The research on fair machine learning (ML) has been growing due to the high demand for
unbiased and fair ML models for objective decision-making. Most of this research has been …

Graph representation learning (GRL) on attribute-missing graphs, which is a common yet
challenging problem, has recently attracted considerable attention. We observe that existing …

The ability to reason, plan and solve highly abstract problems is a hallmark of human
intelligence. Recent advancements in artificial intelligence, propelled by deep neural …

Siamese network plays an important role in many artificial intelligence domains, but there
requires more exploration of applying Siamese structure to graph neural network. This paper …

This article contributes to the field of matching techniques by introducing a new algorithm
based on labor market data enrichment. This approach is able to collect and balance the …

API integration is crucial for enterprise systems, as it enables seamless interaction between
applications within workflows. However, the diversity and complexity of the API landscape …

In this article, we present a framework to assess the challenges in the climate-related data
landscape. From our perspective, we describe challenges and opportunities for innovation …

Data integration still remains largely rule-driven and lacks universal automation. In this work,
we propose a general approach to modeling and integrating entities from structured data …

In today's big data environment, development of robust knowledge discovery solutions
depends on integration of data from various sources. For example, intelligence agencies …