It is extremely difficult to track down the original source of sensitive data from a variety of
sources in the cloud during transit and processing. For instance, data provenance, which …

Cybersecurity knowledge graphs, which represent cyber-knowledge with a graph-based
data model, provide holistic approaches for processing massive volumes of complex …

Knowledge graphs (KGs) have rapidly emerged as an important area in AI over the last ten
years. Building on a storied tradition of graphs in the AI community, a KG may be simply …

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 …

Abstract Knowledge graph question answering (KGQA) has recently received a lot of
attention and many innovative methods have been proposed in this area, but few have been …

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 …

In this work, we develop a derived information framework to semantically annotate how a
piece of information can be obtained from others in a dynamic knowledge graph. We encode …

Background Biomedical translational science is increasingly using computational reasoning
on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome …

Abstract Machine learning (ML) has already fundamentally changed several businesses.
More recently, it has also been profoundly impacting the computational science and …

Sequential recommendation systems seek to learn users' preferences to predict their next
actions based on the items engaged recently. Static behavior of users requires a long time to …