March
29
through 31, 2014
The
hackathon
had participants from (reading from right to left): Australia,
Russia, Italy, France, England and the US (East and West coast).
The
following
participated actively in the hackathon:
AnettHoppe
BrandNiemann
JeffBraswell
MaxGillmore
MikeBennett
MikeDean
MirkoMorandini
TatyanaPoletaeva
Activities
Work
was a
combination of on screen discussion using shared diagrams and
ontology
visualization tool, and off-line working on individual ontologies
in Protégé by
the different participants. Other participants researched possible
ontologies
and data sources to use, and this initial research was used to
decide what area
of risk to focus on for this application. The plan was to have
enough
information to formally specify an “app” which might be used on
mobile devices.
We chose the context of travel risk. The application would provide
comparative
risk figures for a range of transportation modes against a single
specified
goal. In the example, the goal was to get from the user’s home in
Washington DC
to a conference venue in Austin, Texas by 9am on a given day. A
number of
different options were given for completing this goal. Risks would
then be
calculated as a product of probability and impact on that goal,
with
probability being determined as a simple actuarial application of
historical
data to present probabilities.
We
combined
concepts in the following areas:
1.
Trip data (extending an
existing Trajectory
ontology);
2.
Common risk concepts
(context-neutral) derived
from an existing risk ontology for open source development;
3.
Risk Assessment (impacts
etc.) – also extended
for positive versus negative outcomes of an event;
4.
Travel Adverse Events based
on available sources
of historical statistical data
(1), (2), (3) For Trip, Common
Risk and Risk
Assessment, the participants
created or adapted formal OWL ontologies in Protégé. (2) and (3)
were then
ingested into the Visual Ontology Modeler (VOM) tool from Thematix
Partners.
All ontologies were in OWL. Syntaxes used were N3, Turtle and the
Protege OWL file format. Diagrams
were created in the VOM tool for each ontology to better
understand the
content, and these were laid out along similar lines to the
available
conceptual diagrams in the reference sources for this work. The
aim was to
create an integrating ontology which would import these and define
the overall
application ontology.
(4) Travel Adverse Events
was a
bottom-up creation of the ontology directly from the available
data. This
ontology is very extensive and covers multiple modes of transport
and multiple
ultimate causes of delays, accidents and the like. A second round
of work
involved layering the common risk concepts such as for risk event
consequences
and impacts. This was then ingested into VOM and a set of diagrams
created for
the main concepts.
We
used the
on-line sessions to compare thinking about the core risk model and
converged on
a common conceptual framework which was implemented in whole or in
part in the
travel events, common concepts and risk assessment ontologies,
each of which
contained refinements and extensions to that model. The concepts
in the Trip
ontology were segregated between instance data for the example
application, and
common concepts for modes of transport (“trains, planes and
automobiles”). Most
of those common concepts were already in the Travel Adverse Events
ontology,
while some remained to be added. Those additional concepts were
for types of rental
car, types of aircraft body and other variables which were assumed
to be
related to the real-world risk of those travel modes. As a future
exercise, once
the ontology of these additional concepts is defined, it forms a
checklist of
sets of historical data to look for. Thus the top-down
appreciation of risk
factors meets the bottom-up modeling of actual available risk
statistics which
was carried out during the hackathon.
Outcome
At
the
completion of the hackathon, the following things are left as “an
exercise for
the reader”:
1.
Integrating the concepts
into a single ontology;
in the end all the concepts and patterns were agreed and
incorporated in the
individual ontologies and so this exercise would be a relatively
simple matter
of creating equivalent class assertions. If we were to do this as
a commercial
product we would re-define the modular structure of the complete
set of
ontologies to reflect the separate concerns.
2.
Additional risk factors in
the Trip ontology
(rental car types, aircraft body types etc.) which would then form
the basis
for looking for statistical data sets about these risks.
3.
Rolling up types of travel
event for which there
are statistics (such as bridge strikes, traffic jams) into broader
events which
are elements of the trajectory itself so as to describe events in
terms of
missed connections, failure to complete a leg of the journey etc.
As
things
stand we have sufficient information to specify the ontology and
design for a
simple travel risk mobile application in which the user may enter
a desired
time and destination and either enter different travel modes or
have these
calculated by existing applications which already do this; the
application
would return comparative risk figures for the different travel
options.
An
interesting
by-product of this work is that there are conceptual similarities
between the semantics of the risks in multi-stage journeys, and
the semantics
of financial credit risk and cashflow payment streams. The journey
concepts
provide a more accessible way of thinking about these concepts
even for the
financial industry participants. The similarities between journey
trajectories
and complex cashflow commitments seems to be amenable to the
creation of a
common ontology design pattern for the trajectory of cash based on
the
trajectories ontology.
Mike Bennett