With Knowledge Graphs (KGs) at the center of numerous applications such as recommender
systems and question-answering, the need for generalized pipelines to construct and …

Foundation models in language and vision have the ability to run inference on any textual
and visual inputs thanks to the transferable representations such as a vocabulary of tokens …

With the support of advanced information and communication technologies and open
innovative design platforms, the emerging and blooming paradigm of mass personalization …

We study training of Graph Neural Networks (GNNs) for large-scale graphs. We revisit the
premise of using distributed training for billion-scale graphs and show that for graphs that fit …

Complex logical query answering (CLQA) is a recently emerged task of graph machine
learning that goes beyond simple one-hop link prediction and solves a far more complex …

Openly available, collaboratively edited Knowledge Graphs (KGs) are key platforms for the
collective management of evolving knowledge. The present work aims to provide an …

Detecting factual errors in textual information, whether generated by large language models
(LLM) or curated by humans, is crucial for making informed decisions. LLMs' inability to …

Data accuracy is a central dimension of data quality, especially when dealing with
Knowledge Graphs (KGs). Auditing the accuracy of KGs is essential to make informed …

There is an increasing adoption of machine learning for encoding data into vectors to serve
online recommendation and search use cases. As a result, recent data management …

Applications of large open-domain knowledge graphs (KGs) to real-world problems pose
many unique challenges. In this paper, we present extensions to Saga our platform for …