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Knowledge	Representation	&	Reasoning
(&	workflows,	provenance,	…)	
Hierarchy	of	Hypotheses	(HoH)	Workshop
Bertram	Ludäscher
ludaesch@illinois.edu
HoH Workshop
2018-07-03..06	
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
All-in-One	(Teaser)
• Background:
– Computer	Science (Databases,	KR&R),	Scientific	Data	Management
– Scientific	Workflows &	Data	Provenance,	Reproducibility	
• Current	Projects
– DataONE,	Kurator,	SKOPE,	Whole-Tale	
• Tools	&	Technologies
– EulerX:	
• Taxonomy	Alignment,	KR&R
– Provenance &	Reproducibility
• YesWorkflow
• PRIMAD
• Why(-Not)	provenance,	game	provenance,	..		
• Opportunities
– Invitation to	collaborate!
Ludäscher:	KR&R	...	HoH 2
Whole	Tale	
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
>
<
Nodes
CEN 5
NDC 6
Edges
is_a (CEN) 4
is_a (NDC) 5
articulations 9
EulerX
Kurator
• Data	Observation	Network	for	Earth	(DataONE)
– Network	of	earth	science	data	repositories	(member	nodes)
– Large	NSF	DataNet project	to	Discover,	Share,	Use …	
– …	earth	science	data:	ecology,	biodiversity,	…	
• My	R&D	focus:	provenance tools	&	technologies,	ProvONE:
– W3C	PROV	model	extended	to	combine	retrospective &	prospective provenance	
Ludäscher:	KR&R	...	HoH 3
: Provenance in DataONE
A	DataONE search	(here:	“grass”)	yields	different	packages	with	Data	Provenance
(not	covered:	Semantic	Search)		
Ludäscher:	KR&R	...	HoH
4
Exploring	Provenance	in	DataONE
• Let’s	go	there è Mark	Carls.	2017.	Analysis	of	hydrocarbons	following	
the	Exxon	Valdez	oil	spill,	Gulf	of	Alaska,	1989	- 2014.	Gulf	of	Alaska	
Data	Portal.	urn:uuid:3249ada0-afe3-4dd6-875e-0f7928a4c171.	
5Ludäscher:	KR&R	...	HoH
DataONE:	Search	and	Provenance	Display
6
Ludäscher:	KR&R	...	HoH
DataONE:	Search	and	Provenance	Display
7
Ludäscher:	KR&R	...	HoH
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
Ludäscher:	KR&R	...	HoH
8
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:	KR&R	...	HoH 9
...	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:	KR&R	...	HoH
10
Computational Provenance …
• Origin,	processing	history	of	artifacts
– data	products,	figures,	...
– also:	underlying	workflow
è understand	methods,	dataflow,	and	dependencies
è think	about	the	role	of	provenance in	HoH!
Ludäscher:	KR&R	...	HoH 11
Climate Change Impacts
in the United States
U.S. National Climate Assessment
U.S. Global Change Research Program
Related:	Reproducibility Crisis
Watch	out	for	links	to	HoH!
• Successful reproducibility	study:
• increases trust in	prior	study	J
• …	but	no	surprises	L
• Failed reproducibility	study	:
• decreases	trust (or	falsifies)	prior	study	L
• …	but	surprising failure	yields	new	info/knowledge	J
• Learning	from	failures!
– Not	really	a	new,	revolutionary	idea..	
– What	is	a	positive	vs	negative	result	anyways?
– ...	fail	early,	fail	often	...	
Ludäscher:	KR&R	...	HoH 12
PRIMAD	(what	have	you	“primed”?)
Ludäscher:	KR&R	...	HoH 13
Dagstuhl Seminar	#16041	Report	 Outputs	=	Exec(M,I,P,D)	|	RO,	A
- M	=	parsimony/bootstrap/..
- I	=	package	XYZ
- P	=	MacOS ..	
- D	=	(Params,	Files)
PRIMAD	(what	have	you	“primed”?)
Ludäscher:	KR&R	...	HoH 14
Dagstuhl Seminar	#16041	Report	
A	new	dimension	for	HoH !!??
Runtime	Provenance	
(a.k.a.	traces,	logs,		
retrospective
provenance,
“Trace-land”)
Workflow	Modeling	&	Design
(a.k.a.	prospective provenance
“Workflow-land”)
Ludäscher:	KR&R	...	HoH
15
Workflows	ó Provenance	an	important	link!
Provenance	Support	for	Reproducible	Science	
Example:	Paleoclimate	Reconstruction
Science	paper	(OA)	uses:
• open	source	code:
– R,	PaleoCAR,	…
• Is	that	all	we	need?
• What	was	the	
“workflow”?
• Is	there	prospective
and/or	retrospective
provenance?
Ludäscher:	KR&R	...	HoH 16
SKOPE:	Synthesized	Knowledge	Of	Past	Environments
Bocinsky,	Kohler	et	al.	study	rain-fed	maize	of Anasazi
– Four	Corners;	AD	600–1500. Climate	change	influenced	Mesa	Verde	Migrations;	late	
13th	century	AD.	Uses	network	of	tree-ring	chronologies	to	reconstruct	a	spatio-
temporal	climate	field	at	a	fairly	high	resolution	(~800	m)	from	AD	1–2000.	Algorithm	
estimates	joint	information	in	tree-rings	and	a	climate	signal	to	identify	“best”	 tree-ring	
chronologies	for	climate	reconstructing.
K.	Bocinsky,	T.	Kohler,	A	2000-year	reconstruction	of	the	rain-fed	
maize	agricultural	niche	in	the	US	Southwest.	Nature
Communications.	doi:10.1038/ncomms6618
… implemented as an R Script …
Ludäscher:	KR&R	...	HoH 17
SKOPE “Data	Portal”	
• Different	user	groups	(“personas”)
– Researchers
– Tinkerers
– Modelers	
Ludäscher:	KR&R	...	HoH 18
YesWorkflow:	Adding	Prospective &	
Retrospective Provenance		
• 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!
Ludäscher:	KR&R	...	HoH
19
@BEGIN	..	@END	..
@IN	..	@OUT	..
@URI	..	@LOG	..
YW	Demo	Use	Cases	(IDCC’17)
Domain Use	case Programming	language Provenance	methods
Climate	science C3C4 MATLAB YW	+	MATLAB
RunManager
Astrophysics LIGO Python YW	+	NW	(code-level)
Protein crystal	samples Simulate	data	
collection
Python	 YW	+	NW	(code-level)
Biodiversity	data	
curation
kurator-SPNHC Python YW-recon	+	YW-logging
Social	network analysis Twitter Python	 YW +	NW	(file-level)
Oceanography	 OHIBC Howe Sound
(multi-run multi-script)
R	 YW +	R	RunManager
Ludäscher:	KR&R	...	HoH 20
Kurator: Data Curation Workflows
(Filtered-Push … Kepler … Kurator projects)
Ludäscher:	KR&R	...	HoH
21
Ludäscher:	KR&R	...	HoH
22
Ludäscher:	KR&R	...	HoH
23
http://kurator.acis.ufl.edu/kurator-web/
Ludäscher:	KR&R	...	HoH
24
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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
Ludäscher:	KR&R	...	HoH 25
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Taxon	Lookup	Workflow:	
Data	View	and	Process	View
Ludäscher:	KR&R	...	HoH
26
The	story	of	
two	individual	
records
Ludäscher:	KR&R	...	HoH
27
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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	..
Ludäscher:	KR&R	...	HoH
28
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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
Ludäscher:	KR&R	...	HoH 29
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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:	KR&R	...	HoH 30
Whole	Tale	
Whole	Tale	Dashboard
Whole	Tale	Vision
Tale
Data
{ Code
D1PROV
31
WT	Architecture
32
Ludäscher:	KR&R	...	HoH
https://dashboard.
wholetale.org
Example	Tale:	
LIGO	gravitational	wave	detection	
(tutorial	Jupyter notebook)
Ludäscher:	KR&R	...	HoH
34
https://dashboard.wholetale.org
Ludäscher:	KR&R	...	HoH
35
https://dashboard.wholetale.org
Ludäscher:	KR&R	...	HoH
36
https://dashboard.wholetale.org
YesWorkflow:	How	does	the	LIGO	script
produce	its	results?	
Ludäscher:	KR&R	...	HoH
37
Back	to	biodiversity	informatics
Whole	Tale	Summer	Internship:
A	reproducible scientific	workflow
Multiple	Taxonomic	Perspective	
++		Niche	Modeling
Ludäscher:	KR&R	...	HoH
39
Combine	EulerX,	
multiple taxonomic	
perspectives	
(hypotheses)	with	
ecological	niche	
modeling
è
transparency,	
reproducibility
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
Tracing	taxonomic	names	(concepts!)	over	time	…
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
41Ludäscher:	KR&R	...	HoH
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
"Controllingthetaxonomicvariable"
"Just bad"
Source: Franz et al. 2016. Controlling the taxonomic variable: […]. RIO Journal. doi:10.3897/rio.2.e10610
42Ludäscher:	KR&R	...	HoH
The 'consensus' The
'bible'
The (formerly)
federal
'standard'
The 'best', latest
regional flora
"Controllingthetaxonomicvariable"
"Just
bad"
Expert views
are
reconciled
Solution:
Instead of aggregating
an artificial 'consensus',
build translation services
Source: Franz et al. 2016. Controlling the taxonomic variable: […]. RIO Journal. doi:10.3897/rio.2.e10610
43Ludäscher:	KR&R	...	HoH
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.pdf
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
• Alignment visualization: "grey means taxonomically congruent"
Use	case	1.a.		Aligning	Microcebus +	Mirza sec.	MSW3 (2005)
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"
Use	case	1.a.		Aligning	Microcebus +	Mirza sec.	MSW3 (2005)
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
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”	?
50Ludäscher:	KR&R	...	HoH
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)
Ludäscher:	KR&R	...	HoH 51
The	taxonomies
Ludäscher:	KR&R	...	HoH
• 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
52
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?
Ludäscher:	KR&R	...	HoH 53
Sorting	things	out	…	
Ludäscher:	KR&R	...	HoH
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
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
• 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
54
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
>
<
5
6
4
5
9
Ludäscher:	KR&R	...	HoH 55
Merged	taxonomy	T3	(=	T1	“+”	T2)	
CEN.South
NDC.Northeast
NDC.Southwest
CEN.USA
NDC.USA
CEN.West
CEN.Northeast
NDC.Southeast
NDC.West
CEN.Midwest
NDC.Midwest
N
CE
ND
cong
Ed
is_a (
overlap
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.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
><
NDC.Southwest
><
NDC.Southeast>
CEN.Midwest
NDC.Midwest==
CEN.USA
CEN.West
CEN.Northeast
NDC.USA
==
!
><
NDC.West
>
<
des
N 5
C 6
ges
EN) 4
DC) 5
tions 9
T1 T2
T1	~	T2 T3	
Ludäscher:	KR&R	...	HoH 56
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
Ludäscher:	KR&R	...	HoH 57
TAP:	Possible	Outcomes
1.a
1.b
isa
1.c
isa
2.d
=
2.e<
<
2.f<
isa
isa
Input	Alignment
{A1, A2, A3, A4}
{A1, A2, A3} {A1, A2, A4} {A1, A3, A4} {A2, A3, A4}
{A1, A2} {A1, A3} {A2, A3} {A1, A4} {A2, A4} {A3, A4}
{A1} {A2} {A3} {A4}
{ }
Inconsistent!		
è Diagnosis	(Reiter)
=	Black-Box
Provenance
1.b2.e
1.c
1.a
2.d
2.f
Ambiguous!	
è Multiple	
Possible	
Worlds
1.c2.f
1.b
1.a
2.d
2.e
1.b
1.a
2.e
2.d
1.c
2.f
Ludäscher:	KR&R	...	HoH 58
Case	1:	Census	Region	vs.	National	
Diversity	Council
Ludäscher:	KR&R	...	HoH
West
South
Midwest
North-
east
NDC	(with	states)
West
Southwest Southeast
Midwest North-
east
CEN NDC
• … but where do the articulations
come from??
– automatically derived from data
– expert input
59
Ludäscher:	KR&R	...	HoH
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.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.AR
CEN.AL
CEN.OK
CEN.SC
CEN.LA
CEN.GA
CEN.NC
NDC.TN==
NDC.MS==
CEN.MT NDC.MT==
CEN.USA
CEN.Midwest
CEN.MI NDC.MI==
CEN.MN NDC.MN==
CEN.OH NDC.OH==
NDC.AR==
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
60
Ludäscher:	KR&R	...	HoH
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
NDC.GA
CEN.MO
NDC.MO
CEN.OH
NDC.OH
CEN.IA
NDC.IA
CEN.KS
NDC.KS
CEN.RI
NDC.RI
Nodes
CEN
NDC
comb
Edges
input
inferred
overlapsinfer
USA,	Midwest	and	State-level	
alignments	are	all	congruent
61
Ludäscher:	KR&R	...	HoH
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
NDC.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
NDC.OR
CEN.AL
NDC.AL
The	overlapping	relations	are	
automatically	derived	from	data
62
Ludäscher:	KR&R	...	HoH
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
NDC.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
NDC.OR
CEN.AL
NDC.AL
DC	is	in	both	the	South	and	the	Northeast
63
Case	2:	Census	Region	vs	Time	Zone
Ludäscher:	KR&R	...	HoH
Pacific
Mountain
Central
Eastern
West
South
Midwest
North-
east
CEN TZ
• … but where do the articulations
come from??
– automatically derived from data
– expert input
64
Ludäscher:	KR&R	...	HoH
CEN.Northeast
TZ.Eastern
<
CEN.Midwest
><
TZ.Mountain
><
TZ.Pacific
!
CEN.South
><
><
!
TZ.Central
><
CEN.USA
CEN.West
TZ.USA
==
!
><
!
2
CEN.Midwest
CEN.USA
TZ.USA
TZ.Eastern
TZ.Central
TZ.Mountain
CEN.South
CEN.Northeast
CEN.West TZ.Pacific
Input
Output:
Possible	World
Expert-based regional	alignment
65
How	do	we	know	if	our	‘expert	
articulations’	are	correct?	
Ludäscher:	KR&R	...	HoH
R1 R2
R3
R4
R5
R6 R7
R8
R9
GIS solution as the Ground Truth..
66
Ludäscher:	KR&R	...	HoH
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
No
CEN
newCo
com
TZ
Edg
inpu
Combined	concepts	solution	
for	regional-level	alignments
67
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	
Ludäscher:	KR&R	...	HoH 68
EulerX:	Some	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
Ludäscher:	KR&R	...	HoH
https://github.com/EulerProject/ASIST17
69
Black-Box Inconsistency Analysis
(Diagnostic Lattice)
• Then:
– Repair: find & revise minimal inconsistent subsets (Min-Incons)
– Expand: find maximal consistent subsets (Max-Cons) & revise outs
What happens if you can’t
satisfy all (here: 4) articulations
{a, b, c, d} simultaneously?
70Ludäscher:	KR&R	...	HoH
• Black-box	Analysis	(Hitting	Set	algo.)	yields	a	Diagnosis	(lattice)
– for	n=4	articulations,	there	are	168	possible	diagnoses
– depending	on	expected	“red/green	areas”	è explore	space	differently
• What	are	the	“knowledge	propagation”	rules	in	HoH?	
Inconsistency Analysis (Diagnostic Lattice)
• The Min-Incons (MIS) and
Max-Cons (MCS) sets
determine all others
è Repair MIS and/or Expand
MCS
71Ludäscher:	KR&R	...	HoH
• Inconsistency
propagates upward
• Consistency
propagates downward
Inconsistent	Alignment	
Example
• Here:	N	=	10 taxa	in	T1,	T2
• Euler/X	finds:	
inconsistent!
• è diagnostic lattice of	210
=	1024 nodes
è Find	minimal	inconsistent	
subset	(MIS)
è maximal	consistent	subset	
(MCS) ..	
è show to	user!
Ludäscher:	KR&R	...	HoH 72
Visualizing	Inconsistency	Diagnoses
Ludäscher:	KR&R	...	HoH
73
N	=	10	articulations	è 210 =	1024 node	diagnostic	lattice
Better:	Just	show	MIS,	MCS
è Relation	to	HoH ??!!
Ludäscher:	KR&R	...	HoH
74
N	=	4	articulations	è 24 =	16	node	diagnostic	lattice,
but	3	MCS and	2	MIS are	enough!
Visualizing	Diagnoses
..	but	4	MCS	and	
1	MIC	tell	it	all!
Ludäscher:	KR&R	...	HoH
75
1024 node	lattice
All-in-One	(Summary)
• R&D	“teaser”	on	Workflows,	Provenance,	Reproducibility,	KR&R
• EulerX Tools
– Reconciling multiple	taxonomic	perspectives	(hypotheses)
– Logic-based	Diagnosis
– Reasoning	with	Incomplete Knowledge,	Possible	Worlds
• One	size	doesn’t	fit	all:
– …	formalize	&	specialize HoH approach
– …	may	employ	some	of	(or	combination	of)	…
• Workflow	&	Provenance	Modeling	
• PRIMAD	model
• Answer	Set	Programming
• Argumentations	Frameworks	/	Games
• ….	
• Invitation	to	collaborate!	
– DataONE,	SKOPE,	Kurator
– New	Whole	Tale	Biodiversity	Informatics	Working	Group
• Transparency,	Provenance,	Reproducibility
– Reasoning	with	multiple	taxonomies
Ludäscher:	KR&R	...	HoH 76
• …	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.
• …		 77
Some	EulerX
(Pre-)History
Ludäscher:	KR&R	...	HoH
• SKOPE: system	and	tools	to	discover,	access,	
analyze,	visualize	paleoenvironmental data
– unprecedented	ability	to	explore	provenance	
(detailed,	comprehensible	record	of	computational	
derivation	of	results)
– for	researchers,	tinkerers,	and	modelers
• Whole	Tale:	
– leverage	&	contribute	to	existing	CI	to	support	the	
whole	tale	(“living	paper”),	from	workflow	run	to	
scholarly	publication
– integrate	tools	&	CI	(DataONE,	Globus,	iRODS,		
NDS,	...)	to	simplify	use	and	promote	best	
practices.
– driven	by	science	WGs	(Archaeology/SKOPE,	
materials	science,	astro,	bio	..)	
Project	Vignettes	
Ludäscher:	KR&R	...	HoH 78
HoH candidates:	Argumentation	
Frameworks	&	Game	Provenance
a
b
1
c
3
d e
f
1
g
3
m
h
1
k
l
oo
n
oo
oo
oo
2 2
2
Ludäscher:	KR&R	...	HoH
79
• Query	evaluation	and	logic-
based	argumentation	can	be	
understood	as	a	game!		
• One	logic	rule	to	rule	them	all	…	
win(X)	:- move(X,Y),	not win(Y)
• node	color	=>	edge	color	
– good vs bad moves
• good	moves	=	natural,	new	
notion	of	provenance!
• Implement,	e.g.	using	Answer	
Set	Programming	(~	EulerX)
Aside:	Games	~	Argumentation	Frameworks
win(X)	:- move(X,Y),	not win(Y)
def(X)	:- attacks(Y,X),	not def(Y)
Game	Provenance
W
bad Dbad
L
winning
bad
drawing
n/a
delaying
n/a
n/a
a
b
1
c
3
d e
f
1
g
3
m
h
1
k
l
oo
n
oo
oo
oo
2 2
2
Ludäscher:	KR&R	...	HoH
80
Extracting	Provenance:
ü Why/how win(x)?									
• [x]	–G.(R.G)*–> [y]
ü Why-not win(x)?	
• [x]	–(R.G)*–>	[y]
• [x]		–(Y+)–>			[y]
Move	types
Game	Provenance
a
b
1
c
3
d e
f
1
g
3
m
h
1
k
l
oo
n
oo
oo
oo
2 2
2
Ludäscher:	KR&R	...	HoH
81
Extracting	Provenance:
ü Why/how win(x)?									
• [x]	–G.(R.G)*–> [y]
ü Why-not win(x)?	
• [x]	–(R.G)*–>	[y]
• [x]		–(Y+)–>			[y]
• Next:	play	a	query	
evaluation	game
• =>	new	why-(not)	
provenance via	games!

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