  International maritime crime is becoming increasingly sophisticated, often
associated with wider criminal networks. Detecting maritime threats by means of
fusing data purely related to physical movement (i.e., those generated by
physical sensors, or hard data) is not sufficient. This has led to research and
development efforts aimed at combining hard data with other types of data
(especially human-generated or soft data). Existing work often assumes that
input soft data is available in a structured format, or is focused on
extracting certain relevant entities or concepts to accompany or annotate hard
data. Much less attention has been given to extracting the rich knowledge about
the situations of interest implicitly embedded in the large amount of soft data
existing in unstructured formats (such as intelligence reports and news
articles). In order to exploit the potentially useful and rich information from
such sources, it is necessary to extract not only the relevant entities and
concepts but also their semantic relations, together with the uncertainty
associated with the extracted knowledge (i.e., in the form of probabilistic
knowledge graphs). This will increase the accuracy of and confidence in, the
extracted knowledge and facilitate subsequent reasoning and learning. To this
end, we propose Maritime DeepDive, an initial prototype for the automated
construction of probabilistic knowledge graphs from natural language data for
the maritime domain. In this paper, we report on the current implementation of
Maritime DeepDive, together with preliminary results on extracting
probabilistic events from maritime piracy incidents. This pipeline was
evaluated on a manually crafted gold standard, yielding promising results.
