  With the rise of popular task automation or IoT platforms such as 'If This
Then That (IFTTT)', users can define rules to enable interactions between smart
devices in their environment and thereby improve their daily lives. However,
the rules authored via these platforms are usually tied to the platforms and
sometimes even to the specific devices for which they have been defined.
Therefore, when a user wishes to move to a different environment controlled by
a different platform and/or devices, they need to recreate their rules for the
new environment. The rise in the number of smart devices further adds to the
complexity of rule authoring since users will have to navigate an ever-changing
landscape of IoT devices. In order to address this problem, we need
human-computer interaction that works across the boundaries of specific IoT
platforms and devices. A step towards this human-computer interaction across
platforms and devices is the introduction of a high-level semantic model for
end-user IoT development, enabling users to create rules at a higher level of
abstraction. However, many users who already got used to the rule
representation in their favourite tool might be unwilling to learn and adapt to
a new representation. We present a method for translating proprietary rules to
a high-level semantic model by using natural language processing techniques.
Our translation enables users to work with their familiar rule representation
language and tool, and at the same time apply their rules across different IoT
platforms and devices.
