Conversational Services for Multi-Agency Situational Understanding.
2017
Recent advances in cognitive computing technology, mobile
platforms, and context-aware user interfaces have made it
possible to envision multi-agency situational understanding
as a ‘conversational’ process involving human and machine
agents. This paper presents an integrated approach to information
collection, fusion and sense-making founded on
the use of natural language (NL) and controlled natural language
(CNL) to enable agile human-machine interaction and
knowledge management. Examples are drawn mainly from
our work in the security and public safety sectors, but the approaches
are broadly applicable to other governmental and
public sector domains. Key use cases for the approach are
highlighted: rapid acquisition of actionable information, low
training overhead for non-technical users, and inbuilt support
for the generation of explanations of machine-generated outputs.
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