  In the medical domain, the continuous stream of scientific research contains
contradictory results supported by arguments and counter-arguments. As medical
expertise occurs at different levels, part of the human agents have
difficulties to face the huge amount of studies, but also to understand the
reasons and pieces of evidences claimed by the proponents and the opponents of
the debated topic. To better understand the supporting arguments for new
findings related to current state of the art in the medical domain we need
tools able to identify arguments in scientific papers. Our work here aims to
fill the above technological gap.
  Quite aware of the difficulty of this task, we embark to this road by relying
on the well-known interleaving of domain knowledge with natural language
processing. To formalise the existing medical knowledge, we rely on ontologies.
To structure the argumentation model we use also the expressivity and reasoning
capabilities of Description Logics. To perform argumentation mining we
formalise various linguistic patterns in a rule-based language. We tested our
solution against a corpus of scientific papers related to breast cancer. The
run experiments show a F-measure between 0.71 and 0.86 for identifying
conclusions of an argument and between 0.65 and 0.86 for identifying premises
of an argument.
