Whole-Tale: The Experience of Research

Whole Tale: The Experience of Research
… through reproducible, computational narratives
YesWorkflow: Revealing workflow, provenance from scripts
Kurator: Automating data cleaning workflows
EulerX: Agreeing to disagree about variant taxonomies
Bertram Ludäscher
ludaesch@illinois.edu
BCoN Workshop
2018-02-13..14 U Kansas
Director, Center for Informatics Research in Science & Scholarship (CIRSS)
School of Information Sciences (iSchool@Illinois)
& National Center for Supercomputing Applications (NCSA)
& Department of Computer Science (CS@Illinois)
1

Whole Tale: The next step in the evolution of
the scholarly article: The “Living” Paper
• 1st Generation:
– narrative (prose)
• 2nd Generation: plus …
– name .. identify .. include (access to) data
• 3rd Generation: plus …
– name .. reference .. include code (software) ..
– and provenance … and exec environment (containers)
Ludäscher: Whole-Tale++ 2
Whole Tale
Whole Tale Dashboard

Whole Tale: What’s in a name?
(1) Whole Tale ⇔ Whole Story:
◦ Support (computational / data) scientists
◦ … along the complete research lifecycle
◦ ... from experiment to (new kind of) publication
◦ ... and back!
(2) Whole Tale ⇔ for the Long Tail of Science
–Easy sharing of your computational narratives, data, and
exec-env since 2017!
–Power applications for everyone!
3Ludäscher: Whole-Tale++

The Whole Tale: Merging Science
and Cyberinfrastructure Pathways
NSF-DIBBS award (5 years, 5 institutions)
•Illinois (NCSA & iSchool)
•Bertram Ludäscher (PI), MT Campbell (PM) [Kandace Turner],
Victoria Stodden (coPI), Matt Turk (coPI), Kacper Kowalik (sw-
architect), Craig Willis (dev)
•U of Chicago
•Kyle Chard (coPI), Mihael Hategan (dev)
•UT Austin/TACC
•Niall Gaffney (coPI), Siva Kulasekaran (dev)
•U Notre Dame
•Jarek Nabrzyski (coPI), Ian Taylor (dev), Adam Brinckman (dev)
•UCSB/NCEAS
•Matt Jones (coPI), Bryce Mecum (dev)
4

Whole Tale Motivation
• Can't reproduce result because:
• Don't know how to run analysis
• Can't get the software running
• Can't pay for the computer or compute
power the result was computed on
Source: Bryce Mecum, WT team @NCEAS
5

Whole Tale Vision
Addressing reproducibility
6
Data Code
Execution
Environment
Article

Whole Tale Vision
• Living publication
(data + code + environment)
• Facilitate reproducibility
• Encourage investigation of results making it easy to
recreate the environment the result was created in
Article
7

Whole Tale Vision
Addressing reproducibility
Article
Tale
+
8

Whole Tale Vision
Tale
Data
{ Code
D1PROV
9

WT Architecture
10
Ludäscher: Whole-Tale++
https://dashboard.
wholetale.org

DEMO: (re-)use existing tale or …
11
https://dashboard.wholetale.org

… Create a New Tale!
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Running with RStudio: Locally or on WT…
18
You’re up and running quickly on Whole Tale !!

Maybe you just want to use WT to learn
R for Data Science ...
19

Another example Tale:
LIGO gravitational wave detection
(tutorial Jupyter notebook)
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New & Upcoming Features in WT ...
• Add your own Frontends (e.g. OpenRefine, ..)
• Persistent, shared or personal files:
– /data/ (registered/external data, read-only, associated with a tale)
– /home/ (your own data, r/w, associated with all your tales)
– /workspace/ (shared r/w data, associated with a tale, across all users)
• WT “Derived Tales”:
– take a tale; modify it to your liking; and publish as a derived work
• WT “Take-Out”:
– Want to run your tales elsewhere?
– Take-out your tale and run on your on (or cloud) platform
• WT “Scale-Out”:
– If the WT-dashboard isn’t enough è run your own WT system!
• WT Provenance support:
– … via DataONE provenance tools, ProvONE model (W3C PROV extension)
– … via YesWorkflow
• Interest in joining a WT Biodiversity Informatics Working Group!?
– We already have: archaeology & ecology, astronomy, materials science
– Your input wanted! (is WT developing something useful for you?)
– Try out WT, create some examples (in R, Python, ...) and provide feedback!
– => possibility to fund a summer intern!

Additional Material …
… teasers ahead!
29

Provenance is: keeping records …
• Grand Canyon’s rock layers are a record of the early geologic history of North America.
The ancestral puebloan granaries at Nankoweap Creek tell archaeologists about more
recent human history. (By Drenaline, licensed under CC BY-SA 3.0)
• Not shown: computational archaeologists reconstructing past climate from multiple tree-
ring databases è computational provenance is key for transparency & reproducibility
Ludäscher: Workflows & Provenance => Understanding 30

... and provenance is:
Understanding what happened!
Zrzavý, Jan, David Storch, and Stanislav
Mihulka. Evolution: Ein Lese-Lehrbuch.
Springer-Verlag, 2009.
Author: Jkwchui (Based on
drawing by Truth-seeker2004)
Ludäscher: Workflows & Provenance => Understanding
31

Computational Provenance …
• Origin, processing history of artifacts
– data products, figures, ...
– also: underlying workflow
è understand methods, dataflow, and dependencies
Ludäscher: Workflows & Provenance => Understanding 32
Climate Change Impacts
in the United States
U.S. National Climate Assessment
U.S. Global Change Research Program

YesWorkflow: How does the LIGO script
produce its results??
33

YesWorkflow: Prospective & Retrospective
Provenance … (almost) for free!
• YW annotations in
a (Python, R, …)
script recreate a
workflow view
from the script …
cassette_id
sample_score_cutoff
sample_spreadsheet
file:cassette_{cassette_id}_spreadsheet.csv
calibration_image
file:calibration.img
initialize_run
run_log
file:run/run_log.txt
load_screening_results
sample_namesample_quality
calculate_strategy
rejected_sample accepted_sample num_images energies
log_rejected_sample
rejection_log
file:/run/rejected_samples.txt
collect_data_set
sample_id energy frame_number
raw_image
file:run/raw/{cassette_id}/{sample_id}/e{energy}/image_{frame_number}.raw
transform_images
corrected_image
file:data/{sample_id}/{sample_id}_{energy}eV_{frame_number}.img
total_intensitypixel_count corrected_image_path
log_average_image_intensity
collection_log
file:run/collected_images.csv
YW!
34
@BEGIN .. @END ..
@IN .. @OUT ..
@URI .. @LOG ..

GetModernClimate
PRISM_annual_growing_season_precipitation
SubsetAllData
dendro_series_for_calibration
dendro_series_for_reconstruction CAR_Analysis_unique
cellwise_unique_selected_linear_models
CAR_Analysis_union
cellwise_union_selected_linear_models
CAR_Reconstruction_union
raster_brick_spatial_reconstruction raster_brick_spatial_reconstruction_errors
CAR_Reconstruction_union_output
ZuniCibola_PRISM_grow_prcp_ols_loocv_union_recons.tif ZuniCibola_PRISM_grow_prcp_ols_loocv_union_errors.tif
master_data_directory prism_directory
tree_ring_datacalibration_years retrodiction_years
Paleoclimate Reconstruction (openSKOPE.org)
• … explained using YesWorkflow!
Kyle B., (computational) archaeologist:
"It took me about 20 minutes to comment. Less
than an hour to learn and YW-annotate, all-told."

Data Curation Workflows
(Filtered-Push … Kepler … Kurator projects)
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http://kurator.acis.ufl.edu/kurator-web/

38
http://kurator.acis.ufl.edu/kurator-web/

DwCA Taxon Lookup
Workflow
• Declare inputs, outputs, and
steps of a script (or wf) with
YW annotations to ...
– communicate provenance
graphically (via graphviz)
– combine different forms of
provenance
– query provenance
• Simple YW annotations in
comments:
– @BEGIN Step, @END Step
– @IN Data, @OUT Data
– @URI Template, @LOG Pattern
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Taxon Lookup Workflow:
Data View and Process View
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The story of
two individual
records
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• One took the GBIF
route, while …
• … the other went
all WORMS!
Non-
Marine?
è GBIF
Marine?
è
WORMS

The aggregate story ..
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• How many records were
observed as inputs or outputs
of workflow steps?
• Were there any NULL values?
How many?

YesWorkflow Summary
• Lightweight YW annotations can
be added easily to your scripts to
reap workflow benefits
– Documentation of what’s
important
– Visualization of dependencies
– Querying provenance (prospective,
retrospective, and hybrid)
– Independent of system or language
used (R, Python, MATLAB, workflow
tools, …)
è make provenance actionable
è provenance for self!
=> github.com/yesworkflow-org/yw
=> try.yesworkflow.org
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Demo Time
44
(Disclaimer) https://github.com/idaks/dataone-ahm-2016-poster
https://github.com/idaks/wt-prov-summer-2017
https://github.com/yesworkflow-org/yw-idcc-17

DataONE: Search and Provenance Display
45

DataONE: Search and Provenance Display
46

Adding YesWorkflow to DataONE
Yaxing’s script with
inputs & output
products
Christopher’s
YesWorkflow
model
Christopher using
Yaxing’s outputs as
inputs for his script
Christopher’s results
can be traced back all
the way to Yaxing’s
input
47

Yi-Yun Cheng1, Nico Franz2, Jodi Schneider1, Shizhuo Yu3, Thomas Rodenhausen4, Bertram Ludäscher1
1 School of Information Sciences, University of Illinois at Urbana-Champaign; 2 School of Life Sciences, Arizona State University;
3 Department of Computer Science, University of California at Davis; 4 School of Information, University of Arizona
Agreeing to Disagree: Reconciling Conflicting Taxonomic Views
using a Logic-based Approach
Acknowledgments
Support of the authors’ research through the National Science
Foundation is kindly acknowledged (DEB-1155984, DBI-1342595, and
DBI-1643002). The authors thank Professor Kathryn La Barre for her
comments and suggestions. We would also like to thank Dr. Laetitia
Navarro and Jeff Terstriep for help with creating map overlays in QGIS.
CONCLUSION
• Our logic-based taxonomy alignment approach can be used to solve
crosswalking issues
We will be able to mitigate the membership condition problems that
occur in equivalent crosswalking.
• RCC-5 approach preserves the original taxonomies while providing an
alignment view
We can solve data integration problems that happen in the more
coarse-grained relative crosswalking, which otherwise is subjected to
information loss.
• Our study also underscores the benefits of designing different
alignment workflows (Bottom up vs. Top-down) to match the needs
of specific taxonomy alignment problems
Bottom-up approach: seems to work well whenever we have non-
overlapping relationships at the leaf-level (lowest-level) articulations,
and we are not sure how the higher-level concepts should be aligned.
Top-down approach: seems favorable when there is an expectation of
certain higher-level articulations in conjunction with under-specified,
complex, and often overlapping leaf-level relations.
RELATED WORK
• Taxonomy Alignment Problems (TAP)
Taxonomies T1, T2 are inter-linked via a set of input articulations A,
defined as RCC-5 relations, to yield a “merged” taxonomy T3 .
• Euler/X
Articulations – a constraint or rule that defines a relationship (a set
constraint) between two concepts from different taxonomies .
Region Connection Calculus (RCC-5)
Possible Worlds – When encoding and solving TAPs via ASP, the
different answer sets represent alternative taxonomy merge solutions
or possible worlds (PWs).
INTRODUCTION
Tina: Hey Amy, can you recommend a signature dish from where you
live?
Amy: Oh, definitely the half-smokes from the Northeast! They are
these tasty half-pork and half-beef sausages.
Tina: What a coincidence! We have half-smokes in the South, too!
Where do you live in the Northeast? New York? Boston?
Amy: Wrong guesses! Where do you live in the South?
Tina and Amy together: Washington, D.C.
[The two of them look at each other, confused.]
“In the face of incompatible information or data structures among
users or among those specifying the system, attempts to create
unitary knowledge categories are futile. Rather, parallel or multiple
representational forms are required…” (Bowker & Star, 2000).
CASE 1 RESULTS: CEN vs. NDC
• State-level alignments are all congruent (Bottom-up)
• Inferred new articulations for regional-level alignments
CASE 2 RESULTS: CEN vs. TZ
Figure 3. (Left) CEN-NDC taxonomy alignment problem with 49 input articulations between TCEN and TNDC
Figure 4. (Right) The unique possible world (PW) T3 reconciling TCEN and TNDC via inferred relationships
Figure 1. National Diversity Council map (NDC) vs. Census Bureau map (CEN)
• Github link:
https://github.com/EulerProject/ASIST17
• Email: yiyunyc2@illinois.edu
West
Southwest Southeast
Midwest North-
east
West
South
Midwest North-
east
Pacific
Mountain
Central
Eastern
West
South
Midwest
North-
east
RESEARCH DESIGN
Step 1. Supply input taxonomies T1 and T2
Step 2. Formulate RCC-5 articulations between T1 and T2
Step 3. Iteratively edit articulations in Euler/X
Y X X YX Y X Y X Y
Congruence
X == Y
Inclusion
X > Y
Inverse Inclusion
X < Y
Overlap
X>< Y
Disjointness
X ! Y
T1
T2
T1
T2
Inconsistent (N=0)
Ambiguous (N>1)
T3
Add/Edit
Articulations A
Euler/X
N Possible Worlds
N=1 N=0 or N>1
R1
R2
R3
R4
R5
R6
R7
R8
R9
CEN.Midwest
CEN.USA
TZ.USA
CEN.West
CEN.Northeast
TZ.EasternCEN.Midwest
TZ.EasternCEN.South
CEN.South
CEN.South*TZ.Central
TZ.CentralCEN.Midwest
CEN.SouthTZ.Eastern
CEN.SouthTZ.Mountain
TZ.Central
CEN.MidwestTZ.Eastern
TZ.MountainCEN.South
TZ.Mountain
CEN.MidwestTZ.Mountain
TZ.MountainCEN.Midwest
CEN.Midwest*TZ.Mountain
CEN.MidwestTZ.Central
TZ.MountainCEN.West
CEN.Midwest*TZ.Eastern
CEN.West*TZ.Mountain
CEN.South*TZ.Mountain
CEN.SouthTZ.Central
TZ.Eastern
CEN.South*TZ.Eastern
CEN.Midwest*TZ.Central
TZ.CentralCEN.South
TZ.Pacific
CEN.WestTZ.Mountain
Nodes
CEN 4
newComb 18
comb 1
TZ 4
Edges
input 6
inferred 37
CEN.IL NDC.IL==
CEN.IN NDC.IN
==
CEN.RI NDC.RI==
CEN.IA NDC.IA==
CEN.WV NDC.WV
==
CEN.KS NDC.KS==
CEN.KY NDC.KY==
CEN.TX
NDC.TX
==
CEN.Northeast
CEN.VT
CEN.MA
CEN.ME
CEN.CT
CEN.PA
CEN.NY
CEN.NH
CEN.NJ
CEN.South
CEN.TN
CEN.MS
CEN.MD
CEN.DC
CEN.DE
CEN.VA
CEN.FL
CEN.AR
CEN.AL
CEN.OK
CEN.SC
CEN.LA
CEN.GA
CEN.NC
CEN.ID NDC.ID==
NDC.TN==
CEN.WY NDC.WY==
NDC.VT==
NDC.MS==
CEN.MT NDC.MT==
NDC.MA
==
CEN.USA
CEN.Midwest
CEN.West
NDC.ME==
NDC.MD==
CEN.MI NDC.MI==
CEN.MN NDC.MN==
NDC.DC==
NDC.DE==
CEN.OR NDC.OR==
CEN.OH NDC.OH==
NDC.VA==
NDC.FL==
NDC.AR==
CEN.AZ NDC.AZ==
NDC.AL==
NDC.OK
==
NDC.CT==
CEN.CO NDC.CO
==
CEN.CA NDC.CA==
CEN.SD NDC.SD
==
NDC.SC==
CEN.MO
CEN.ND
CEN.NE
CEN.WI
NDC.LA==
NDC.MO==
CEN.UT NDC.UT==
NDC.GA==
NDC.PA==
CEN.NV
CEN.NM
CEN.WA
NDC.NY==
NDC.NV==
NDC.NM==
NDC.WA
==
NDC.NH==
NDC.NJ==
NDC.ND==
NDC.NE==
NDC.WI==
NDC.NC==
NDC.West
NDC.Midwest
NDC.Northeast
NDC.Southeast
NDC.USA
NDC.Southwest
Nodes
CEN 54
NDC 55
Edges
isa_CEN 53
isa_NDC 54
Art. 49
CEN.West
NDC.Southwest
CEN.USA
NDC.USA
CEN.Northeast
NDC.Northeast
CEN.South
NDC.Southeast
NDC.West
CEN.DC
NDC.DC
CEN.NM
NDC.NM
CEN.ND
NDC.ND
CEN.Midwest
NDC.Midwest
CEN.AZ
NDC.AZ
CEN.CA
NDC.CA
CEN.MT
NDC.MT
CEN.MA
NDC.MA
CEN.IN
NDC.IN
CEN.NV
NDC.NV
CEN.MD
NDC.MD
CEN.CT
NDC.CT
CEN.NH
NDC.NH
CEN.KY
NDC.KY
CEN.PA
NDC.PA
CEN.CO
NDC.CO
CEN.WA
NDC.WA
CEN.MI
NDC.MI
CEN.VA
NDC.VA
CEN.WI
NDC.WI
CEN.NE
NDC.NE
CEN.SD
NDC.SD
CEN.MN
NDC.MN
CEN.MS
NDC.MS
CEN.ID
NDC.ID
CEN.WV
NDC.WV
CEN.NY
NDC.NY
CEN.NJ
NDC.NJ
CEN.UT
NDC.UT
CEN.ME
NDC.ME
CEN.IL
NDC.IL
CEN.TN
NDC.TN
CEN.VT
NDC.VT
CEN.GA
NDC.GA
CEN.DE
NDC.DE
CEN.NC
NDC.NC
CEN.OK
NDC.OK
CEN.MO
NDC.MO
CEN.SC
NDC.SC
CEN.AR
NDC.AR
CEN.TX
NDC.TX
CEN.LA
NDC.LA
CEN.OH
NDC.OH
CEN.IA
NDC.IA
CEN.KS
NDC.KS
CEN.RI
NDC.RI
CEN.WY
NDC.WY
CEN.FL
NDC.FL
CEN.OR
NDC.OR
CEN.AL
NDC.AL
Nodes
CEN 3
NDC 4
comb 51
Edges
input 61
inferred 3
overlapsinferred 3
CEN.Northeast
TZ.Eastern
<
CEN.Midwest
><
TZ.Mountain
><
TZ.Pacific
!
CEN.South
><
><
!
TZ.Central
><
CEN.USA
CEN.West
TZ.USA
==
!
><
!
Nodes
CEN 5
TZ 5
Edges
isa_CEN 4
isa_TZ 4
Art. 12
CEN.Midwest
CEN.USA
TZ.USA
TZ.Eastern
TZ.Central
TZ.Mountain
CEN.South
CEN.Northeast
CEN.West TZ.Pacific
Nodes
CEN 4
comb 1
TZ 4
Edges
input 7
overlapsinput 6
overlapsinferred 1
R1
R2
R3
R4
R5
R6 R7
R8
R9
Figure 2. The process of aligning
taxonomies T1 and T2 with Euler/X
Figure 5. Top-down
input alignments
between TCEN and TTZ
Figure 6. The unique
PW for the TCEN with
TTZ alignment
Figure 10. Combined concepts
solution for TCEN and TTZ
taxonomy CEN Census_Regions
(USA Northeast Midwest South West)
(Northeast CT MA ME NH NJ NY PA RI VT)
(Midwest IL IN IA KS MI MN MO NE ND OH
SD WI)
(South AL AR DE DC FL GA KY LA MD MS NC
OK SC TN TX VA WV)
(West AZ CA CO ID MT NV NM OR UT WA WY)
taxonomy NDC
National_Diversity_Council
(USA Midwest Northeast Southeast
Southwest West)
(Northeast CT DC DE MD MA ME NH NJ NY
PA RI VT)
(Midwest IA IL IN KS MI MN MO ND NE OH
SD WI)
(Southeast AL AR FL GA KY LA MS NC SC
TN VA WV)
(Southwest AZ NM OK TX)
(West CA CO ID MT NV OR WA WY UT)
articulations CEN NDC
[CEN.AL equals NDC.AL]
[CEN.AR equals NDC.AR]
[CEN.AZ equals NDC.AZ]
[CEN.CA equals NDC.CA]
[CEN.CO equals NDC.CO]
[CEN.CT equals NDC.CT]
[CEN.DC equals NDC.DC]
[CEN.DE equals NDC.DE]
[CEN.FL equals NDC.FL]
[CEN.GA equals NDC.GA]
[CEN.IA equals NDC.IA]
[CEN.ID equals NDC.ID]
[CEN.IL equals NDC.IL]
[CEN.IN equals NDC.IN]
[CEN.KS equals NDC.KS]
[CEN.KY equals NDC.KY]
[CEN.LA equals NDC.LA]
[CEN.MA equals NDC.MA]
[CEN.MD equals NDC.MD]
[CEN.ME equals NDC.ME]
[CEN.MI equals NDC.MI]
[CEN.MN equals NDC.MN]
...
Quick Scan!
taxonomy CEN Census_Regions
(USA Midwest South West Northeast)
taxonomy TZ Time_Zone
(USA Pacific Mountain Central Eastern)
articulations CEN TZ
[CEN.Midwest disjoint TZ.Pacific]
[CEN.Midwest overlaps TZ.Eastern]
[CEN.Midwest overlaps TZ.Mountain]
[CEN.Northeast is_included_in TZ.Eastern]
[CEN.South disjoint TZ.Pacific]
[CEN.South overlaps TZ.Central]
[CEN.South overlaps TZ.Eastern]
[CEN.South overlaps TZ.Mountain]
[CEN.USA equals TZ.USA]
[CEN.West disjoint TZ.Central]
[CEN.West disjoint TZ.Eastern]
[CEN.West overlaps TZ.Mountain]
48

For another time?
Non-unitary syntheses
of systematic knowledge
Nico Franz
School of Life Sciences, Arizona State University
CIRSS Seminar – Center for Informatics Research in Science and Scholarship
February 17, 2017 – iSchool, University of Illinois Urbana-Champaign
@ http://www.slideshare.net/taxonbytes/franz-2017-uiuc-cirss-non-unitary-syntheses-of-systematic-knowledge
49
Tracing taxonomic names (concepts!) over time …

Taxonomic concept alignment, Andropogon glomeratus-virginicus
complex, spanning across 11 classifications authored 1889-2015
• 36 unique taxonomic names
• 88 taxonomic concept labels
Þ name sec. author strings
• Alignment by A.S. Weakley
Þ row position = congruence
• 1/36 names with unique 1 : 1
name : meaning cardinality
across all classifications
• Andropogon virginicus
• Source: Franz et al. 20161
1 Franz et al. 2016. Names are not good enough: reasoning over taxonomic change in the Andropogon complex.
Semantic Web Journal (IOS). doi:10.3233/SW-160220

http://taxonbytes.org/wp-content/uploads/2014/10/Peet-BIGCB-2014-Changing-Perspectives-on-Plant-Distributions.pdf51

Use case 1.a. Aligning Microcebus + Mirza sec. MSW3 (2005)
"Taxonomic concept labels"
identify input concept regions
RCC–5 articulations provided
for each species-level concept
• Input visualization: MSW3 (2005) versus MSW2 (1993)
Source: Franz et al. 2016. Two influential primate classifications logical aligned. doi:10.1093/sysbio/syw023
52

• Alignment visualization: "grey means taxonomically congruent"
53

One name &
congruent region
Many names &
congruent region
One name &
non-congruent regions
Many names &
non-congruent regions
New names &
exclusive regions
• Application of coverage constraint: parent-to-parent articulations (><) are
fully defined by alignment signal propagated from their respective children.
è Sensible when complete sampling of children is intended.
• Alignment visualization: "grey means taxonomically congruent"
54

1 in 3 names is unreliable across MSW2/MSW3 classifications
Source: Franz et al. 2016. Two influential primate classifications logical aligned. doi:10.1093/sysbio/syw023
55

The 'consensus' The
'bible'
The (formerly)
federal
'standard'
The 'best', latest
regional flora
"Controllingthetaxonomicvariable"
Expert views
are in
conflict
"Just bad"
Source: Franz et al. 2016. Controlling the taxonomic variable: […]. RIO Journal. doi:10.3897/rio.2.e10610

The 'consensus' The
'bible'
The (formerly)
federal
'standard'
The 'best', latest
regional flora
Impact:
Name-based aggregation has created
a novel synthesis that nobody believes in
"Just bad"

The 'consensus' The
'bible'
The (formerly)
federal
'standard'
The 'best', latest
regional flora
"Just
bad"
Expert views
are
reconciled
Solution:
Instead of aggregating
an artificial 'consensus',
build translation services

Leaving taxon and species headaches …
• To illustrate Euler think of a simpler use case:
• Agreeing to disagree!
• … when there are multiple, legitimate
perspectives
• Sorting things out!
– Euler as a taxon concept (& name) “microscope” ...
– .. or “time machine” ?

Two Taxonomies: NDC vs CEN
“…in the face of incompatible information or data structures among users or among those
specifying the system, attempts to create unitary knowledge categories are futile. Rather, parallel
or multiple representational forms are required” [Bowker & Star, 2000, p.159]
West
Southwest Southeast
Midwest North-
east
West
South
Midwest North-
east
National Diversity Council map (NDC) US Census Buero map (CEN)
Source: Yi-Yun (Jessica) Cheng (PhD student, iSchool @ Illinois)

The taxonomies
• The Census Regions Map (CEN), consists of four regions: West,
Midwest, Northeast, and South, i.e., the contiguous 48 states
and Washington D.C.
West
South
Midwest
North-
east
61

The taxonomies
• The National Diversity Council Map (NDC), consists of five
regions: West, Southwest, Midwest, Northeast, Southeast, the
48 states and Washington D.C.
NDC (with states)
West
Southwest Southeast
Midwest North-
east
• NDC splits South into
SW and SE
• Do NDC and CEN
agree on “West”?
“Midwest”? …
• How can we sort this
out?

Sorting things out …
CEN.Midwest
CEN.USA
CEN.South CEN.West CEN.Northeast NDC.Northeast
NDC.USA
NDC.Southeast NDC.Midwest NDC.Southwest NDC.West
Nodes
CEN 5
NDC 6
Edges
is_a (CEN) 4
is_a (NDC) 5
CEN.South
NDC.Northeast
o
NDC.Southwest
o
NDC.Southeast>
CEN.Midwest
NDC.Midwest=
CEN.USA
CEN.West
CEN.Northeast
NDC.USA
=
!
o
NDC.West
>
<
CEN.Midwest
CEN.USA
NDC.USA
Nodes
CEN 5
NDC 6
Edges
is_a (CEN) 4
is_a (NDC) 5
• Given:
– taxonomies T1, T2
– and relations T1 ~ T2
(articulations, alignment)
• Find:
– merged taxonomy T3
• Such that:
– T1, T2 are preserved
– all pairwise relations are
explicit
T1 T2
63

5 ways to relate concepts (regions)
• Idea: relate concepts X and Y with
articulations
• Articulation Language: Region
Connection Calculus (RCC5): congruence,
inclusion, inverse inclusion, overlap,
disjointness
Y X X YX Y X Y X Y
Congruence
X == Y
Inclusion
X > Y
Inverse Inclusion
X < Y
Overlap
X>< Y
Disjointness
X ! Y
CEN.South
NDC.Northeast
><
NDC.Southwest
><
NDC.Southeast>
CEN.Midwest
NDC.Midwest==
CEN.USA
CEN.West
CEN.Northeast
NDC.USA
==
!
><
NDC.West
>
<

Merged taxonomy T3
CEN.South
NDC.Northeast
NDC.Southwest
CEN.USA
NDC.USA
CEN.West
CEN.Northeast
NDC.Southeast
NDC.West
CEN.Midwest
NDC.Midwest
con
is_a
overla
CEN.Midwest
CEN.USA
NDC.USA
Nodes
CEN 5
NDC 6
Edges
is_a (CEN) 4
is_a (NDC) 5
CEN.Midwest
CEN.USA
NDC.USA
Nodes
CEN 5
NDC 6
Edges
is_a (CEN) 4
is_a (NDC) 5
CEN.South
NDC.Northeast
><
NDC.Southwest
><
NDC.Southeast>
CEN.Midwest
NDC.Midwest==
CEN.USA
CEN.West
CEN.Northeast
NDC.USA
==
!
><
NDC.West
>
<
5
6
4
5
s 9
T1 T2
T1 ~ T2 T3

How we align two taxonomies T1 and T2
• Step 1. Supply input taxonomies T1
and T2
• Step 2. Describe the relationships
between T1 and T2
• Step 3. Iteratively edit articulations
in Euler/X
T1
T2
T1
T2
Inconsistent (N=0)
Ambiguous (N>1)
T3
Add/Edit
Articulations A
Euler/X
N Possible Worlds
N=1 N=0 or N>1
• … but where do the articulations
come from??
– expert opinion
– automatically derived from data

Case 1: Census Region vs. National
Diversity Council
West
South
Midwest
North-
east
NDC (with states)
West
Southwest Southeast
Midwest North-
east
CEN NDC
come from??
– expert input
67

CEN.IL NDC.IL==
CEN.IN NDC.IN
==
CEN.RI NDC.RI==
CEN.IA NDC.IA==
CEN.WV NDC.WV
==
CEN.KS NDC.KS==
CEN.KY NDC.KY==
CEN.TX
NDC.TX
==
CEN.Northeast
CEN.VT
CEN.MA
CEN.ME
CEN.CT
CEN.PA
CEN.NY
CEN.NH
CEN.NJ
CEN.South
CEN.TN
CEN.MS
CEN.MD
CEN.DC
CEN.DE
CEN.VA
CEN.FL
CEN.AR
CEN.AL
CEN.OK
CEN.SC
CEN.LA
CEN.GA
CEN.NC
CEN.ID NDC.ID==
NDC.TN==
CEN.WY NDC.WY==
NDC.VT==
NDC.MS==
CEN.MT NDC.MT==
NDC.MA
==
CEN.USA
CEN.Midwest
CEN.West
NDC.ME==
NDC.MD==
CEN.MI NDC.MI==
CEN.MN NDC.MN==
NDC.DC==
NDC.DE==
CEN.OR NDC.OR==
CEN.OH NDC.OH==
NDC.VA==
NDC.FL==
NDC.AR==
CEN.AZ
NDC.AZ==
NDC.AL==
NDC.OK
==
NDC.CT==
CEN.CO NDC.CO
==
CEN.CA NDC.CA==
CEN.SD NDC.SD
==
NDC.SC==
CEN.MO
CEN.ND
CEN.NE
CEN.WI
NDC.LA==
NDC.MO==
CEN.UT NDC.UT==
NDC.GA==
NDC.PA==
CEN.NV
CEN.NM
CEN.WA
NDC.NY==
NDC.NV==
NDC.NM==
NDC.WA
==
NDC.NH==
NDC.NJ==
NDC.ND==
NDC.NE==
NDC.WI==
NDC.NC==
NDC.West
NDC.Midwest
NDC.Northeast
NDC.Southeast
NDC.USA
NDC.Southwest
Nodes
CEN 54
NDC 55
Edges
sa_CEN 53
sa_NDC 54
Art. 49
CEN.IL NDC.IL==
CEN.IN NDC.IN
==
CEN.IA NDC.IA==
CEN.WV NDC.WV
==
CEN.KS NDC.KS==
CEN.TX
NDC.TX
==
CEN.South
CEN.TN
CEN.MS
CEN.AL
CEN.OK
CEN.SC
CEN.LA
CEN.GA
CEN.NC
NDC.TN==
NDC.MS==
CEN.USA
CEN.Midwest
CEN.MI NDC.MI==
CEN.MN NDC.MN==
CEN.OH NDC.OH==
CEN.AZ
NDC.AZ==
NDC.AL==
NDC.OK
==
CEN.SD NDC.SD
==
NDC.SC==
CEN.MO
CEN.ND
CEN.NE
CEN.WI
NDC.LA==
NDC.MO==
NDC.GA==
CEN.NM
NDC.NM==
NDC.ND==
NDC.NE==
NDC.WI==
NDC.NC==
NDC.Midwest
NDC.Southeast
NDC.USA
NDC.Southwest
Nodes
CEN 54
NDC 55
Edges
isa_CEN 53
isa_NDC 54
Art. 49
68

CEN.West
NDC.Southwest
CEN.USA
NDC.USA
CEN.Northeast
NDC.Northeast
CEN.South
NDC.Southeast
NDC.West
CEN.DC
NDC.DC
CEN.NM
NDC.NM
CEN.ND
NDC.ND
CEN.Midwest
NDC.Midwest
CEN.AZ
NDC.AZ
CEN.CA
NDC.CA
CEN.MT
NDC.MT
CEN.MA
NDC.MA
CEN.IN
NDC.IN
CEN.NV
NDC.NV
CEN.MD
NDC.MD
CEN.CT
NDC.CT
CEN.NH
NDC.NH
CEN.KY
NDC.KY
CEN.PA
NDC.PA
CEN.CO
NDC.CO
CEN.WA
NDC.WA
CEN.MI
NDC.MI
CEN.VA
NDC.VA
CEN.WI
NDC.WI
CEN.NE
NDC.NE
CEN.SD
NDC.SD
CEN.MN
NDC.MN
CEN.MS
NDC.MS
CEN.ID
NDC.ID
CEN.WV
NDC.WV
CEN.NY
NDC.NY
CEN.NJ
NDC.NJ
CEN.UT
NDC.UT
CEN.ME
NDC.ME
CEN.IL
NDC.IL
CEN.TN
NDC.TN
CEN.VT
NDC.VT
CEN.GA
NDC.GA
CEN.DE
NDC.DE
CEN.NC
NDC.NC
CEN.OK
NDC.OK
CEN.MO
NDC.MO
CEN.SC
NDC.SC
CEN.AR
NDC.AR
CEN.TX
NDC.TX
CEN.LA
NDC.LA
CEN.OH
NDC.OH
CEN.IA
NDC.IA
CEN.KS
NDC.KS
CEN.RI
NDC.RI
CEN.WY
NDC.WY
CEN.FL
NDC.FL
CEN.OR
NDC.OR
CEN.AL
NDC.AL
Nodes
CEN 3
NDC 4
comb 51
Edges
input 61
inferred 3
overlapsinferred 3
CEN.Northeast
CEN.ND
NDC.ND
CEN.Midwest
NDC.Midwest
CEN.MA
NDC.MA
CEN.IN
NDC.IN
CEN.CT
NDC.CT
CEN.NH
NDC.NH
CEN.PA
NDC.PA
CEN.MI
NDC.MI
CEN.WI
NDC.WI
CEN.NE
NDC.NE
CEN.SD
NDC.SD
CEN.MN
NDC.MN
CEN.NY
NDC.NY
CEN.NJ
NDC.NJ
CEN.ME
NDC.ME
CEN.IL
NDC.IL
CEN.VT
NDC.VT
CEN.MO
NDC.MO
CEN.OH
NDC.OH
CEN.IA
NDC.IA
CEN.KS
NDC.KS
CEN.RI
NDC.RI
Nod
CEN
NDC
comb
Edg
input
inferre
overlapsinf
USA, Midwest and State-level
alignments are all congruent
69

CEN.West
NDC.Southwest
CEN.USA
NDC.USA
CEN.Northeast
NDC.Northeast
CEN.South
NDC.Southeast
NDC.West
CEN.DC
NDC.DC
CEN.NM
NDC.NM
CEN.ND
NDC.ND
CEN.Midwest
NDC.Midwest
CEN.AZ
NDC.AZ
CEN.CA
NDC.CA
CEN.MT
NDC.MT
CEN.MA
NDC.MA
CEN.IN
NDC.IN
CEN.NV
NDC.NV
CEN.MD
NDC.MD
CEN.CT
NDC.CT
CEN.NH
NDC.NH
CEN.KY
NDC.KY
CEN.PA
NDC.PA
CEN.CO
NDC.CO
CEN.WA
NDC.WA
CEN.MI
NDC.MI
CEN.VA
NDC.VA
CEN.WI
NDC.WI
CEN.NE
NDC.NE
CEN.SD
NDC.SD
CEN.MN
NDC.MN
CEN.MS
NDC.MS
CEN.ID
NDC.ID
CEN.WV
NDC.WV
CEN.NY
NDC.NY
CEN.NJ
NDC.NJ
CEN.UT
NDC.UT
CEN.ME
NDC.ME
CEN.IL
NDC.IL
CEN.TN
NDC.TN
CEN.VT
NDC.VT
CEN.GA
NDC.GA
CEN.DE
NDC.DE
CEN.NC
NDC.NC
CEN.OK
NDC.OK
CEN.MO
NDC.MO
CEN.SC
NDC.SC
CEN.AR
NDC.AR
CEN.TX
NDC.TX
CEN.LA
NDC.LA
CEN.OH
NDC.OH
CEN.IA
NDC.IA
CEN.KS
NDC.KS
CEN.RI
NDC.RI
CEN.WY
NDC.WY
CEN.FL
NDC.FL
CEN.OR
NDC.OR
CEN.AL
NDC.AL
Nodes
CEN 3
NDC 4
comb 51
Edges
input 61
inferred 3
overlapsinferred 3
CEN.West
NDC.Southwest
CEN.USA
NDC.USA
NDC.Northeast
CEN.South
NDC.Southeast
CEN.DC
NDC.DC
CEN.NM
NDC.NM
CEN.AZ
NDC.AZ
CEN.MA
NDC.MA
CEN.MD
NDC.MD
CEN.CT
CEN.KY
NDC.KY
CEN.VA
NDC.VA
CEN.MS
NDC.MS
CEN.WV
NDC.WV
CEN.TN
NDC.TN
CEN.GA
NDC.GA
CEN.DE
NDC.DE
CEN.NC
NDC.NC
CEN.OK
NDC.OK
CEN.SC
NDC.SC
CEN.AR
NDC.AR
CEN.TX
NDC.TX
CEN.LA
NDC.LA
CEN.FL
NDC.FL
CEN.AL
NDC.AL
The overlapping relations are
automatically derived from data
70

CEN.West
NDC.Southwest
CEN.USA
NDC.USA
CEN.Northeast
NDC.Northeast
CEN.South
NDC.Southeast
NDC.West
CEN.DC
NDC.DC
CEN.NM
NDC.NM
CEN.ND
NDC.ND
CEN.Midwest
NDC.Midwest
CEN.AZ
NDC.AZ
CEN.CA
NDC.CA
CEN.MT
NDC.MT
CEN.MA
NDC.MA
CEN.IN
NDC.IN
CEN.NV
NDC.NV
CEN.MD
NDC.MD
CEN.CT
NDC.CT
CEN.NH
NDC.NH
CEN.KY
NDC.KY
CEN.PA
NDC.PA
CEN.CO
NDC.CO
CEN.WA
NDC.WA
CEN.MI
NDC.MI
CEN.VA
NDC.VA
CEN.WI
NDC.WI
CEN.NE
NDC.NE
CEN.SD
NDC.SD
CEN.MN
NDC.MN
CEN.MS
NDC.MS
CEN.ID
NDC.ID
CEN.WV
NDC.WV
CEN.NY
NDC.NY
CEN.NJ
NDC.NJ
CEN.UT
NDC.UT
CEN.ME
NDC.ME
CEN.IL
NDC.IL
CEN.TN
NDC.TN
CEN.VT
NDC.VT
CEN.GA
NDC.GA
CEN.DE
NDC.DE
CEN.NC
NDC.NC
CEN.OK
NDC.OK
CEN.MO
NDC.MO
CEN.SC
NDC.SC
CEN.AR
NDC.AR
CEN.TX
NDC.TX
CEN.LA
NDC.LA
CEN.OH
NDC.OH
CEN.IA
NDC.IA
CEN.KS
NDC.KS
CEN.RI
NDC.RI
CEN.WY
NDC.WY
CEN.FL
NDC.FL
CEN.OR
NDC.OR
CEN.AL
NDC.AL
Nodes
CEN 3
NDC 4
comb 51
Edges
input 61
inferred 3
overlapsinferred 3
CEN.West
NDC.Southwest
CEN.USA
NDC.USA
NDC.Northeast
CEN.South
NDC.Southeast
CEN.DC
NDC.DC
CEN.NM
NDC.NM
CEN.AZ
NDC.AZ
CEN.MA
NDC.MA
CEN.MD
NDC.MD
CEN.CT
CEN.KY
NDC.KY
CEN.VA
NDC.VA
CEN.MS
NDC.MS
CEN.WV
NDC.WV
CEN.TN
NDC.TN
CEN.GA
NDC.GA
CEN.DE
NDC.DE
CEN.NC
NDC.NC
CEN.OK
NDC.OK
CEN.SC
NDC.SC
CEN.AR
NDC.AR
CEN.TX
NDC.TX
CEN.LA
NDC.LA
CEN.FL
NDC.FL
CEN.AL
NDC.AL
DC is in both the South and the Northeast
71

Case 2: Census Region vs Time Zone
Pacific
Mountain
Central
Eastern
West
South
Midwest
North-
east
CEN TZ
come from??
– expert input
72

CEN.Northeast
TZ.Eastern
<
CEN.Midwest
><
TZ.Mountain
><
TZ.Paciﬁc
!
CEN.South
><
><
!
TZ.Central
><
CEN.USA
CEN.West
TZ.USA
==
!
><
!
CEN.Midwest
CEN.USA
TZ.USA
TZ.Eastern
TZ.Central
TZ.Mountain
CEN.South
CEN.Northeast
CEN.West TZ.Paciﬁc
Input
Output:
Possible World
Top-down regional alignment
73

How do we know if our ‘expert
articulations’ are correct?
R1
R2
R3
R4
R5
R6
R7
R8
R9
GIS solution as the Ground Truth..
74

R1
R2
R3
R4
R5
R6
R7
R8
R9
CEN.Midwest
CEN.USA
TZ.USA
CEN.West
CEN.Northeast
TZ.EasternCEN.Midwest
TZ.EasternCEN.South
CEN.South
CEN.South*TZ.Central
TZ.CentralCEN.Midwest
CEN.SouthTZ.Eastern
CEN.SouthTZ.Mountain
TZ.Central
CEN.MidwestTZ.Eastern
TZ.MountainCEN.South
TZ.Mountain
CEN.MidwestTZ.Mountain
TZ.MountainCEN.Midwest
CEN.Midwest*TZ.Mountain
CEN.MidwestTZ.Central
TZ.MountainCEN.West
CEN.Midwest*TZ.Eastern
CEN.West*TZ.Mountain
CEN.South*TZ.Mountain
CEN.SouthTZ.Central
TZ.Eastern
CEN.South*TZ.Eastern
CEN.Midwest*TZ.Central
TZ.CentralCEN.South
TZ.Paciﬁc
CEN.WestTZ.Mountain
N
CE
newC
co
T
E
Combined concepts solution
for regional-level alignments
75

Do the taxonomies have to be
spatial in order to use RCC-5?
• No! The more typical cases for taxonomy
alignment are usually between non-spatial
taxonomies
– for which no “GIS route” or direct visual cues
about regional extensions are available
– the use of RCC-5 as an alignment vocabulary is a
suitable approach to perform a wide range of
multi-hierarchy reconciliations

Conclusion & Discussion
• Underscores the benefits of designing different
alignment workflows (Bottom-up vs. Top-Down)
– Bottom-up: non-overlapping relationships at the lowest-level
articulations, not sure how to align the higher-level concepts
– Top-Down: when there is often overlapping leaf-level relations..
Expert input will frequently be needed to establish such
expectations under the top-down approach
yiyunyc2@illinois.edu
77

Implications
• Logic-based taxonomy alignment approach
– Disambiguate name-based taxonomy alignment over time
• 40% of the concepts in biology taxonomies undergoes
name change over time (Franz et al., 2016)
– May mitigate problems in equivalent crosswalking
• Membership condition problem that was often criticized in
crosswalking
– Preserves the original taxonomies while providing an
alignment view
• Solve data integration problems that happen in the more
coarse-grained relative crosswalking
yiyunyc2@illinois.edu
78

• … Aristotle …
• … Euler …
• …
• … Greg Whitbread …
• [BPB93] J. H. Beach, S. Pramanik, and J. H. Beaman. Hierarchic
taxonomic databases.,Advances in Computer Methods for Systematic
Biology: Artificial Intelligence, Databases, Computer Vision, 1993
• [Ber95] Walter G. Berendsohn. The concept of “potential taxa” in
databases. Taxon, 44:207–212, 1995.
• [Ber03] Walter G. Berendsohn. MoReTax – Handling Factual Information
Linked to Taxonomic Concepts in Biology. No. 39 in Schriftenreihe für
Vegetationskunde. Bundesamt für Naturschutz, 2003.
• [GG03] M. Geoffroy and A. Güntsch. Assembling and navigating the
potential taxon graph. In [Ber03], pages 71–82, 2003.
• [TL07] Thau, D., & Ludäscher, B. (2007). Reasoning about taxonomies in
first-order logic. Ecological Informatics, 2(3), 195-209.
• [FP09] Franz, N. M., & Peet, R. K. (2009). Perspectives: towards a
language for mapping relationships among taxonomic concepts.
Systematics and Biodiversity, 7(1), 5-20.
• … 79
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History

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