1. iME4d - BiOnym
A concept-mapping workflow for taxon names reconciliation
Friday 7 March 2014 – Rome
A concept-mapping workflow for taxon names reconciliation
Fabio Fiorellato, Edward Vanden Berghe, Gianpaolo Coro, Nicolas Bailly
2. Big Data make its way to biology
• Data volumes increase dramatically
– Management of large databases (millions of
records) easier
• no longer the realm of professional IT people• no longer the realm of professional IT people
– Biologists wake up to the advantages of
• Good data management, including preservation
• Data sharing
• Makes it possible to do science in a different
way
3. ‘Big Data’: Need for data integration
• Becoming a very realistic possibility
– Management of DBs of millions of records
• Needs integration of small, restricted-scope
datasets into massive databasesdatasets into massive databases
– Intra-discipline integration (homogenous)
– Inter-discipline integration (heterogeneous)
• Individual studies too small to inform on a scale
commensurate with problems humankind faces
– Evidence-based management of living resources
– Climate change, global warming…
4. iMarine biodiversity ‘ecosystem’
Taxon name enrichment
Taxon name reconciliationTaxon name access
Occurrence data access
Environmental data access
openModeller
AquaMaps
Distribution modelling
Occurrence data enrichment
Occurrence data reconciliation
5. Central role of taxon name reconciliation
Taxon name enrichment
Taxon name reconciliationTaxon name access
Occurrence data access
Environmental data access
openModeller
AquaMaps
Distribution modelling
Occurrence data enrichment
Occurrence data reconciliation
6. Taxonomic names are the keys…
• … Keys to bind together information on the
same taxon from different sources
• But there are problems• But there are problems
– Different research groups use different spellings
– Accidental misspellings
– Synonym, homonym reconciliation (but outside
scope of ByOnym)
7. Some people can’t type
• Asthenognathas inaefaipes
• Asthenognathus inaeqipes
• Asthenognathus maefaipes• Asthenognathus maefaipes
• Astheognathus inaequipes
• Asthenognathus inaeguipes
• Astheognathus inaeqinipes
• Asthenognathus inaequipes
8. Things can go wrong with Excel…
• Clupea harengus Linnaeus, 1758
• Clupea harengus Linnaeus, 1759
• Clupea harengus Linnaeus, 1760• Clupea harengus Linnaeus, 1760
• Clupea harengus Linnaeus, 1761
• Clupea harengus Linnaeus, 1762
• …
10. Taxonomic names are the keys…
• … Keys to bind together information on the
same taxon from different sources
• But there are problems• But there are problems
– Different research groups use different spellings
– Accidental misspellings
• Reconciliation is necessity, not luxury!!!
11. Existing systems…
• … Are not flexible
– We need flexibility, as our use case will dictate what the ‘optimal’
behaviour of the system is
• E.g. manual vs automatic systems
• … Are often coupled to a single ‘reference list’• … Are often coupled to a single ‘reference list’
– Using different tax. Scope for test and reference only increases
false positives
• E.g. TaxaMatch with IRMNG…
• …Don’t always have throughput needed for
large-scale projects
– Largest db appr. 20M names – too many pairs!
12. Our need
• A flexible, highly customisable, workflow-
based approach to taxon name matching
– User controls input
– Output can be used as input in other– Output can be used as input in other
processes
– Running on high performance computing
infrastructure
BiOnym!
13. Introduction to BiOnym
• As a workflow for taxon name mapping and reconciliation, it is
a real-world application of the concept-mapping principles
• It is focused on the domain of taxonomy, with an initial
restriction to marine species only
• Provides a full workflow (not only the concept mapping part)
• Tries to address - and possibly solve - many issues common to• Tries to address - and possibly solve - many issues common to
the taxonomic community
• Its key concept is “species taxonomy”, where concept
properties are the taxonomic atoms
• Is open to integration from third party components
• Takes advantage of the iMarine distributed infrastructure
14. The iMarine solution: existing state-of-the-art
• A general purpose concept mapping framework
(COMET) was already available in FAO:
– based on an existing FAO product (limited to the fishing
vessels domain) initially developed with the support of the
Japanese trust fund
– domain independent (can be tailored to any custom– domain independent (can be tailored to any custom
domain with little effort)
– provided with all the necessary building blocks and
components for general purpose usage
15. The iMarine solution: the quest for integration
• The integration of COMET inside iMarine was hailed
and expected.
• Its main challenges:
– Identify and define the custom domain (biological taxonomy)
– Design and implement:
• custom COMET matchlets (engine assigning similarity scores to pairs of names)
• additional, reusable tools for data interchange and data preparation
(DwCA converter, input parser, pre- and post-processors)
– Enable components to be easily distributed among worker nodes
inside the infrastructure
– Integration in the iMarine Statistical Manager
16. The iMarine solution: a success story
• The COMET integration inside iMarine, as part of the
BiOnym workflow, is an example of success story:
– Solving the integration challenges required limited effort
• Harvest names for input through iMarine tools
• Send output from BiOnym/COMET on to further tools
– The core matching capabilities of BiOnym were first made– The core matching capabilities of BiOnym were first made
available in June 2013
• Pre- and post-processing; parsing
• Matching through (a series of) matchlets, assigning a similarity
score to pairs of names
– The modular architecture enabled developers to add new
functionalities or improve existing ones with ease
17. BiOnym key concepts and features
• Its modular architecture is open to contribution and
alternatives
– Workflow stages can be plugged-in with custom business implementations
– Can leverage third party components (e.g. the input data parsing is available
both as an in-house component or as a wrapper of the GNI parser from
globalnames.org)
• Based on standard and open formats• Based on standard and open formats
– Reference data are synthesized from DWCA files
– Input data and matching results are expected and produced as CSV files
– Matching results can also be emitted as XML files in the COMET format
• High flexibility
– Multiple chained matchers, each with its own configuration and thresholds
– Third party matchers (e.g. Tony Rees’ TaxaMatch) can be seamlessly ‘wrapped’
and plugged in the workflow
– Support for collaborative matching results evaluation (expected soon)
20. Where are we?
• Infrastructure has largely been built
• User-friendly GUI is under development
• Evaluation
– Efficiency: speed of computations– Efficiency: speed of computations
• Parallel system, compares well with others
– Effectiveness: are the results OK?
• Ran experiments on different test datasets
– Deliberately introducing misspellings in known lists
– ‘Real’ misspellings manually corrected for other purposes
21. The Bionym Interface
Never mind the small print.
Step 1: Select your data
Step 2: Compose the
matching process. This
relies on infrastructure
resources
Step 3: review results. This
can be private and ‘for your
eyes only’, or public.
24. Where to from here?
• Validation
– Not in terms of quality of output but…
– Uptake by the biodiversity community
• Sustainability• Sustainability
– Who will take over maintenance after iMarine
ends?
• BiOnym is a tool, it is the means to an end
– Support Ecosystem Approach to Fisheries
25. iMarine biodiversity ‘ecosystem’
Taxon name enrichment
Taxon name reconciliationTaxon name access
Occurrence data access
Environmental data access
openModeller
AquaMaps
Distribution modelling
Occurrence data enrichment
Occurrence data reconciliation
26. BiOnym in its environment
Ecological modelling – Rich data management
Taxa Authority FileTaxa Authority File
Vernacular Names
Authority File
Vernacular Names
Authority File
Darwin Core ArchiveDarwin Core Archive
Based on the COMET Framework
developed by Fabio Fiorellato (FAO)
