Feb. 12, 2024, 5:42 a.m. | Jeffrey Sardina Luca Costabello Christophe Gu\'eret

cs.LG updates on arXiv.org arxiv.org

Knowledge Graphs (KGs) have become increasingly common for representing large-scale linked data. However, their immense size has required graph learning systems to assist humans in analysis, interpretation, and pattern detection. While there have been promising results for researcher- and clinician- empowerment through a variety of KG learning systems, we identify four key deficiencies in state-of-the-art graph learning that simultaneously limit KG learning performance and diminish the ability of humans to interface optimally with these learning systems. These deficiencies are: 1) …

analysis become challenges cs.ai cs.lg data detection empowerment graph graph learning graphs humans interpretation knowledge knowledge graph knowledge graphs learning systems researcher scale systems through

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Encounter Data Management Professional

@ Humana | Work at Home - Kentucky

Pre-sales Manager (Data, Analytics & AI)

@ Databricks | Stockholm, Sweden

Lecturer / Senior Lecturer - Medical Imaging

@ Central Queensland University | Mackay, QLD, AU

Intern - Research Engineer

@ Plus | Santa Clara, CA