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Keeping ontology development Agile
Robert StevensBioHealth Informatics GroupSchool of Computer Science
University of ManchesterOxford RoadManchester
Motivation
• Ontology development is long and difficult– Never really complete
• Getting the conceptualisations right is hard, because it’s hard
• Requirements change• The underlying science changes• The perfect is the enemy of the good• A counsel of perfection is a counsel of despair• Delivering early and often, changing as necessary
The Waterfall
Knowledge GatheringKnowledge Gathering
ConceptualisationConceptualisation
EncodingEncoding
EvaluationEvaluation
Spirals
Knowledge GatheringKnowledge Gathering
ConceptualisationConceptualisation
EncodingEncoding
EvaluationEvaluation
Elements of agile Programming
• Iterative and incremental• Evolving requirements and solutions• Self-organising and cross-functional teams• Short time boxes; rapid and responsive
development• Doing what is important first• Users embedded in the process and are first
class citizens• Test driven; regular and frequent builds and so
on
Experiences from the Software Ontology project and others
• A short, six month project to start a software ontology
• Provide a vocabulary to describe software used in bioinformatics analyses and more
• Centre around two requirements gathering workshops
• http://softwareontology.wordpress.com
SWO people
• Andy Brown (Manchester)
• James Malone (EBI)
• Helen Parkinson (EBI)
• Robert Stevens (Manchester)
Workshop Programme
• What do you want to say about software? – clustering exercise
• What questions do you want to ask about software in archives? – Clustering activity
• Use cases• Which of these features are most important?• The persona of our users• Describe some software…• Re-visit elements to re-prioritise and so on
Angels on the head of a pin
Rules of Engagement
• No death by PowerPoint
• Actually get a workshop to do some work
• No Ontologising
• Show some early results
Workshop Attendees
THE BRITISH LIBRARY
Bioinformaticians
Bio-ontologists
LibrariansSoftware
Preservation
Data Preservation
Service Registaries
Workflow Repositories
Publishers
Astronomical Data Archive
Biology
Project Managers
Flow of Events
• Develop Persona to drive other requirements gathering
• Gather an cluster features• Gather and cluster competency questions• Prioritise features• Describe some software (informally)• Develop axiom patterns for the ontology• Describe more software• Do each bit again (and again)
Persona Headings
• Age
• Background
• Dress Code
• Favourite food
• Work
• Story
Persona
Brenda the Brenda the BioinformaticianBioinformatician
Brenda the Brenda the BioinformaticianBioinformatician
Percy the Percy the ProfessorProfessorPercy the Percy the ProfessorProfessor
Archie the Software Archie the Software ArchitectArchitect
Rufus the Digital Rufus the Digital Preservation Preservation
ManagerManager
Rufus the Digital Rufus the Digital Preservation Preservation
ManagerManager
Adrian the PhD Adrian the PhD StudentStudent
Bender the RobotBender the RobotBender the RobotBender the Robot
Fergus the FunderFergus the FunderFergus the FunderFergus the Funder
Ollie the Operations Ollie the Operations Support GuySupport Guy
Features Gathered
SoftwareSoftware
Source Code Location
Interfaces
Dependencies
Supplier
Version
Life Cycle
Configure/run Parameters
AlgorithmsCost of Ownership
Platform
Licenses
ArchitectureData
Competency questions• What software works best with my dataset?• Does it do what I want or need it do data e.g. render a gif?• Which software tool created this data?• What software can perform task x?• Is it appropriate software for my task?• What are the primary inputs and outputs?• Is this software available as a web service?• What open source, maintained software can I use to process these
in this format?• Where can I get the software?• Is there a mailing list?• How and where has this software been used successfully in the
past?• http://softwareontology.wordpress.com/2011/04/01/user-sourced-
competency-questions-for-software/
Priority poker
• Estimate the cost and complexity of each feature• Attendees give effort of 0.5, 1, 5, 10, …, 40 and
negotiate• Each attendee has “money” to the value of half
mean effort cost of a feature• The attendees collectively “spend” money on
features…• The result is a priority list of those features the
collective are prepared to buy
Ontology Modules
Interfaces
Data
SWO
Algorithms
Licenses
Maturity
Objectives
OrganizationsVersions
Implementation route
Requirements
Priority Poker
Implementation of top
requirements
Evaluation
Requirements priorities change
• In workshop one “hardware platform” was not prioritised
• In workshop two it was
• Working through examples indicated that it was important
• Add some “doing” to the #”thinking”….
Populating the Ontology Modules
What we’ve Learnt
• Collaborative authoring doesn’t mean everyone writes axioms
• Not everyone has to be a trained ontologist• Can separate knowledge gatherring from
axiomatisation (esp. via spreadsheets)• Ontologising is important, but not all the time
and not for everyone• Priorities change• One can manage workshops to have everyone
contribute and avoid “wars of words”• Don’t try to achieve everything in one go
Questions
• What features and characteristics of an experiment must be recorded to enable replicability?
• What features and characteristics of an experiment need not be recorded to enable replicability?
• What questions would you like to be able to ask of a repository of experimental protocols?
