ontology

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Taxonomy and Ontology
Ian Bailey
ian@modelfutures.com

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Overview
• Attempt to compare the disciplines of Taxonomy
and Ontology
– What do they have in common ?
– Where do they differ ?
– How are they used ?
• Case Study: UK Defence Taxonomy
– In March 2009, MOD ran a small research project to
investigate how master reference data is best
provided to enterprise architects
– We took the UKDT and re-engineered large parts of it
into a formal ontology (based on IDEAS ontology)
• Assume the audience knows far more about
Taxonomy than I do

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Taxonomy and Ontology
• Several definitions for both, not all of them are consistent
• The types of taxonomy developed in UK Gov seem to be
about terminology
– Providing consistent terms to enable better discovery of
information and consistency of communication
– Usually implemented in software systems, but their goal is to
help humans find stuff and be more consistent
• Again, there are different flavours of ontology around
– They all seem to share the common trait of being models of
domain of interest
– Unlike a taxonomy, an ontology models the things of interest
and their relationships. The names of those things is of
secondary concern to the structure of the things
– Ontologies tend not to be for human consumption – not only
are they “computer-interpretable”, they are generally speaking
able to configure a system to do certain things

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T&O – Quick Example to Compare
• Barracks and garrisons taxonomy
– Descending by “narrower term”
– Aldershot Garrison narrower than Barracks and Garrisons
– Arnhem Barracks Aldershot narrower than Aldershot Garrison
• An ontology cares more about the
nature of these things
• Barracks and Garrisons is a type
• Aldershot Garrison is and individual
• Their relationship is type-instance
• Arnhem Barracks is also an individual
• Its relationship to Aldershot Garrison
is whole-part
• Making these distinctions allows for
computer systems to interpret reality in a
way that is closer to human
understanding

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Looking at it Another Way
• Venn Diagrams & Physical Structures
– Types (ovals) and Individuals (rectangles)
– Individuals and their parts
• Relationships are important
– What was simply narrower term in the taxonomy breaks down
into super-subtype (between types), type-instance (between
types and things of that type) and whole-part (between
individuals)
built estate
barracks and garrisons
Aldershot garrison
Arnhem Barracks,
Aldershot
Aldershot garrison
Arnhem Barracks
Browning Barracks
Brunevel Barracks
etc.
super-subtype
type-instance whole-part

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Why Bother ?
• This may seem like a lot of fuss…
• However, you can build systems on this stuff
• Super-Subtype Inheritance
– If we know Built Estate has a lat-long location, then we know
Barracks and Garrisons also have lat-long
• Type-Instance
– …and we also know that Aldershot has a specific lat-long value
• Whole-Part
– If we know Aldershot Garrison is in Hampshire then we know
Brunevel Barracks is also in Hampshire
• The point is that a certain degree of sophistication is
required in order that systems can make inferences that
can support business
– Allows automation of a number of processes that would
otherwise have been manual

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Names & Objects
• There are things in the real word (individuals,
types, relationships) and there are the names we
give them
Name SpaceObject Space
Built Estate
Aldershot garrison
Barracks and Garrisons
Arnhem Barracks, Aldershot
super-subtype
type-instance
type-instance
whole-part
named-by
named-by
named-by
named-by
narrower-term
narrower-term
narrower-term
Ontologies tend to become quite “webby”,
and this is a good thing. It better reflects
reality, is extensible, and can cope with very
complex concepts

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Synonyms and Homonyms
• The next level of sophistication for on ontology is to allow more
than one namespace
– Each object in the real world may have more than one name, each
belonging to different namespaces
– e.g. German, French and English names:
– Homonyms are simply the same text being use to describe two
different objects, but in two different namespaces
German Namespace
French Namespace
English Namespace “Hund”
“chien”
“dog”
named-by
named-by
named-by
Army Namespace
RAF Namespace
Navy Namespace “tank”
“tank”
“tank” named-by
named-by
named-by

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Take Care with Synonyms
• Some taxonomies can be quite loose with their “Alternative
Terms”
– Prime Minister <> Tony Blair
– Recycling <> Black Bin Bag
• Sometimes, what appear to be synonyms are actually
names applying to different states of something:
Person
Time
“Miss A Smith”
“Mrs
A Jones”
“Mrs A Evans”
• In the same way that we use whole-part to break
individuals into their physical parts, we can also break them
into temporal parts
• This is called 4D Ontology
• Each temporal part has a name

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Methodology
• There aren’t many formal methods for
developing ontologies
– Either done by navel-gazing academics agonising for
weeks over the essence of a concept
– …or hacked together by programmers
– Neither are ideal situations
• There is one methodology, designed for re-
engineering existing data into an ontology
– The BORO Method (Business Object Re-engineering
Ontology)
– Developed by Chris Partridge – ex KPMG legacy data
practice lead
– IDEAS upper ontology is developed using BORO

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BORO Flowchart
Select a
concept for
analysis
Does
it have spatial and
temporal
extent ?
Does it
have members ?
no
(not individual)
Add to model
yes
(individual)
yes
(type)
no
Does it
relate things ?
yes
(tuple)
no (if you’ve got to this stage, the concept needs to be broken down further)
what does it relate ?
Add these things to
the analysis
yes
what are the members ? Select some typical members and analyse these
START HERE

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Ontology in MOD – Country Codes
• Starting with the SCOPE geo taxonomy, we built an
ontology for locations
– Using the namespace concept, we allowed for multiple
names and identifiers for each geo-political entity
• e.g. ISO country codes, NATO country codes, US FIPS10-4 country
codes, names in English, German, etc.
– Also added borders information
whole-part
whole-part type-instance
named-by

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Ontology in MOD – EA Master Data
• Enterprise Architecture is multidisciplinary
– Business processes, org structures, systems modelling, etc.
• Need to encourage consistent terminology and structures
in EA
– Maximise re-use of existing architecture
• Used UK Defence Taxonomy as basis and produced an
ontology for MODAF users
– Defence Estates – bases, garrisons, barracks
– Equipment – types of platform, weapon, comms system, etc.
– Organisation structure – brigades, squadrons, etc.
• Also pulled in data from other sources
– Defence Framework (org structure of MOD)
– MOD website (military org structures)

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Where We Are, Where We’re Going
• IDEAS
– International upper ontology developed by defence ministries of UK,
US, Canada, Sweden and Australia
– Adopted by DoD as basis for DoD Architecture Framework v2.0
(DODAF DM2)
– Foundation released in April 2009
• UK MOD
– Continued involvement with ontology and IDEAS
– Michael Warner keeps tabs on projects
– Currently investigating use of IDEAS in MODAF (as the US did with
DoDAF)
– Other ontology projects around – esp. around intelligence and
counter-terror
• Ordnance Survey
– John Goodwin at OS
– Developing natural language notations for ontologies
– Will present at a future TIPS event

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Further Reading
• BORO & Ontology
– Cutter Paper
• http://www.cutter.com/offers/forensicIS.html
– Chris Partridge’s book
• “Business Objects: Re-Engineering for Re-Use”
• ISBN 978-0955060304
– 4D Ontology
• “How Things Persist”; Katherine Hawley
• ISBN 978-0199275434

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Contact
Ian Bailey
ian@modelfutures.com
www.modelfutures.com