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