Machine reading aims to automatically extract knowledge
from text. It is a long-standing goal of AI and holds the
promise of revolutionizing Web search and other fields. In
this paper, we analyze the core challenges of machine reading
and show that statistical relational AI is particularly well
suited to address these challenges. We then propose a unifying
approach to machine reading in which statistical relational
AI plays a central role. Finally, we demonstrate the
promise of this approach by presenting OntoUSP, an end-toend
machine reading system that builds on recent advances in
statistical relational AI and greatly outperforms state-of-theart
systems in a task of extracting knowledge from biomedical
abstracts and answering questions.