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[ontology-summit] Hackathon Short Report: "Ontology Design Patterns and

To: Ontology Summit 2013 discussion <ontology-summit@xxxxxxxxxxxxxxxx>
From: Mike Bennett <mbennett@xxxxxxxxxxxxxxx>
Date: Wed, 02 Apr 2014 03:04:51 +0100
Message-id: <533B7043.4070107@xxxxxxxxxxxxxxx>

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:











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.



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

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