  TMs are a pattern recognition approach that uses finite state machines for
learning and propositional logic to represent patterns. In addition to being
natively interpretable, they have provided competitive accuracy for various
tasks. In this paper, we increase the computing power of TMs by proposing a
first-order logic-based framework with Herbrand semantics. The resulting TM is
relational and can take advantage of logical structures appearing in natural
language, to learn rules that represent how actions and consequences are
related in the real world. The outcome is a logic program of Horn clauses,
bringing in a structured view of unstructured data. In closed-domain
question-answering, the first-order representation produces 10x more compact
KBs, along with an increase in answering accuracy from 94.83% to 99.48%. The
approach is further robust towards erroneous, missing, and superfluous
information, distilling the aspects of a text that are important for real-world
understanding.
