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A	Lightweight	Approach	to	Explore,	Enrich	
and	Use	Data	with	a	Geospa9al	Dimension	
using	Seman9c	Web	Technologies	
Christophe	Debruyne	(TCD),	Kris	McGlinn	(TCD),		
Lorraine	McNerney	(OSi),	and	Declan	O’Sullivan	(TCD)	
2017-05-14	@	GeoRICH	2017	
The	ADAPT	Centre	is	funded	under	the	SFI	Research	Centres	Programme	(Grant	13/RC/2106)	and	is	co-funded	under	the	European	Regional	Development	Fund.
www.adaptcentre.ieIntroduc9on	
•  Linked	Data	is	a	set	of	best	pracXces	and	guidelines	to	publish	and	
interlink	data	on	the	Web	by	cleverly	combining	several	standardized	
technologies.	
•  GeospaXal	informaXon	is	an	important	part	of	the	LD	Web.	In	fact,	most	
datasets	have	a	geospaXal	dimension	and	loca%on	provides	a	convenient	
way	to	explore,	analyze	and	align	datasets.	
•  Its	importance	is	evidenced	by	the	vast	amount	of	geographic	data	on	the	
Web	and	bespoke	tools	that	are	available	that	support	the	consumpXon	of	
such	data	(e.g.,	GeoSPARQL-enabled	triplestores).	
•  How	can	we	limit	relying	on	such	bespoke	tools,	and	leverage	engagement	
with	geospaXal	Linked	Data	to	analyze,	explore	and	enrich	non-RDF	data?
www.adaptcentre.ieContext	–	Open	Data	Engagement	Fund		
•  Organized	by	the	Department	of	Public	Expenditure	and	Reform	in	
conjuncXon	with	the	Open	Data	Governance	Board	
•  The	goal	of	this	iniXaXve	is	to	improve	the	availability	and	usage	of	data	
on	the	hap://data.gov.ie/	portal	
•  We	observed	that	not	many	datasets	are	available	as	RDF	and	thus	also	
not	available	as	Linked	Data.	They	are	available	as	CSV,	TSV,	etc.	But,	
many	datasets	have	a	geospaXal	dimension,	which	ideally	should	be	linked	
to	authoritaXve	geospaXal	Linked	Data	(as	provided	by	the	Ordnance	
Survey	Ireland).
www.adaptcentre.ieContext	–	data.geohive.ie	
data.geohive.ie	is	an	ongoing	
collaboraXon	between	ADAPT	and	
the	Ordnance	Survey	Ireland	to	
publish	OSi’s	authoritaXve	
geospaXal	informaXon	as	Linked	
Data.	
	
StarXng	from	publicly	available	
boundary	data,	supporXng	two	
use	cases:	provision	of	different	
geometries	for	features,	and	
provenance	and	evoluXon	of	
features	and	their	geometries
www.adaptcentre.ieApproach	
We	propose	a	method	and	a	set	of	tools	for:	
•  Uplif:	transforming	non-RDF	into	RDF	
•  Interlinking	
•  Downlif:	transforming	back	to	non-RDF,	and	
•  Engagement	
CSV
File
Uplift
Data
in RDF
Links
in RDF
Merge /
Combine
Enriched
descriptions
in RDF
Engage
Enriched CSV
File
Downlift
!
Create Links
Link
Discovery
Tools
SPARQL
CONSTRUCT
queries
...
www.adaptcentre.ieBackground	
Triple	PaJern	Fragments	(Verborgh	et	al.	2016)	
•  SPARQL	endpoints	require	a	lot	of	resources	on	one’s	server.	And	one	
ofen	provides	data	dumps	and	resolvable	URIs	as	a	“good	enough”	
pracXce	to	avoid	this	problem	(Verborgh	et	al.	2016)	
•  Distribute	load	between	a	TPF	client	and	server	
•  Less	load	on	the	server	at	the	cost	of	increased	bandwidth	
•  Easy	to	setup	locally	and	to	“simulate”	federated	queries	
	
GeoSPARQL	
•  An	OGC	standard	for	represenXng	and	querying	geospaXal	data	on	the	
SemanXc	Web.	Defines	both	a	vocabulary	for	represenXng	geospaXal	
informaXon	and	an	extension	to	the	SPARQL	query	language.	
R.	Verborgh,	M.	Vander	Sande,	O.	HarXg,	J.	Van	Herwegen,	L.	De	Vocht,	B.	De	Meester,	G.	Haesendonck,	and	P.	
Colpaert.	2016.	Triple	Paaern	Fragments:	A	low-cost	knowledge	graph	interface	for	the	Web.	J.	Web	Sem.	37-38	
(2016),	184–206
www.adaptcentre.ieR2RML	
R2RML:	RDB	to	RDF	Mapping	Language	
•  A	W3C	RecommendaXon	since	fall	2012	
•  CreaXng	an	R2RML	file	that	annotates	a	relaXonal	database	with	exisXng	
vocabularies	and/or	ontologies	(RDFS	or	OWL).	
•  That	R2RML	file	goes	through	an	R2RML	Mapping	Engine	to	produce	RDF.	
•  R2RML	specified	
•  An	ontology	to	specify	those	mappings;	
•  How	those	mappings	should	be	interpreted	to	produce	RDF.	
•  R2RML	files	are	thus	stored	as	RDF.
www.adaptcentre.ie
@prefix	rr:	<hap://www.w3.org/ns/r2rml#>	.	
@prefix	foaf:	<hap://xmlns.com/foaf/0.1/>	.	
@prefix	dbpedia:	<hap://dbpedia.org/ontology/>	.	
	
<#CityTriplesMap>	
	a	rr:TriplesMap	;	
	
	rr:logicalTable	[	rr:tableName	"City"	]	;	
	
	rr:subjectMap	[	
	 	rr:template	"hap://foo.example/City/{ID}"	;	
	 	rr:class	dbpedia:Place	;	
	]	;	
	
	rr:predicateObjectMap	[	
	 	rr:predicate	foaf:name	;	
	 	rr:objectMap	[	rr:column	"Name"	]	;	
	]	;	
	.	
What	is	being	
mapped?	A	logical	
table/view	or	an	SQL	
query.	
How	to	generate	and	
state	something	about	
the	subject	of	those	
triples.	
How	to	generate	
predicates	and	objects.	
City	
ID	 Name	
1	 Dublin	
2	 Ghent	
Background	–	R2RML	Example
www.adaptcentre.ieApproach	–	Running	Example	
•  Fingal	County	Council	Weather	StaXons	
•  Example	from	the	hap://data.gov.ie/	portal	
Name	
Weather	
Reading	 Agency	 LAT	 LONG	
M50	
Blanchardstown		 hap://…	
NaXonal	Roads	
Authority	
53.3704660
3	
-6.38085144
7	
M50	Dublin	Airport		hap://…	
NaXonal	Roads	
Authority	
53.4096411
1	
-6.22759742
8	
Dublin	Airport		 hap://…	 Met	Éireann	
53.4215060
8	-6.29784754	
Records	in	fccweatherstaXonsp20110829-2221.csv
www.adaptcentre.ieApproach – Uplift
@prefix odef: <http://adaptcentre.ie/ont/odef#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix csv: <file:///...#> .
<#TriplesMap>
rr:logicalTable [
rr:sqlQuery """SELECT rownum() AS ROW_NUM, *
FROM fccweatherstationsp201108292221;"""
] ;
rr:subjectMap [
rr:template "http://www.example.org/record/{ROW_NUM}" ;
rr:class odef:Record ;
] ;
rr:predicateObjectMap [
rr:predicate csv:LONG ;
odef:label "LONG" ;
odef:order "5"^^xsd:int ;
rr:objectMap [ rr:column "LONG" ] ;
] ;
# Other predicate-object maps omitted for brevity
rr:predicateObjectMap [
rr:predicate csv:ROW_NUM ;
rr:objectMap [ rr:column "ROW_NUM" ] ;
] .
Adopt	the	W3C	RDB	to	RDF	
Mapping	Language	
(R2RML)	to	generate	a	
mapping	from	CSV	to	RDF	
Insert	metadata	containing	
informaXon	about	the	non-
RDF	resource’s	structure	in	
the	mapping
www.adaptcentre.ieApproach	–	UpliX	
Generate	RDF	with	R2RML-F	–	an	implementaXon	of	R2RML	supporXng	
funcXons	in	JavaScript	(haps://opengogs.adaptcentre.ie/debruync/r2rml)		
Christophe	Debruyne,	Declan	O'Sullivan:	R2RML-F:	Towards	Sharing	and	
ExecuXng	Domain	Logic	in	R2RML	Mappings.	LDOW@WWW	2016	
<http://www.example.org/record/2>
a <http://adaptcentre.ie/ont/odef#Record> ;
<file:///...#AGENCY> "National Roads Authority" ;
<file:///...#LAT> "53.4096411069945" ;
<file:///...#LONG> "-6.22759742761812" ;
<file:///...#NAME> "M50 Dublin Airport" ;
<file:///...#ROW_NUM> "2"^^<http://www.w3.org/2001/XMLSchema#int> ;
<file:///...#WEATHER_READING> "http://www.nratraffic.ie/..." ;
geo:asWKT "POINT(-6.22759742761812 53.4096411069945)"^^geo:wktLiteral .
www.adaptcentre.ieApproach	–	Interlinking	
Either	with	SPARQL	CONSTRUCT	queries	or	use	the	RDF	using	link	discovery	
tools	such	as	Silk	(Jentzsch	et	al.	2010)	or	LIMES	(Ngonga	et	al.	2011)	
	
SPARQL	CONSTRUCT	queries	can	be	formulated	using	TPF.	For	support	for	
GeoSPARQL,	we	use	an	extension	of	the	TPF	client	(Debruyne	et	al.	2017)	
	
In	our	running	example,	we	can	adopt	GeoSPARQL	to	find	out	where	the	
weather	staXons	are	based	by	either:	
•  ConcatenaXng	the	LAT	and	LONG	to	create	a	WKT	point	
•  Extending	the	skeleton	mapping	and	generate	WKT	points	
Anja	Jentzsch,	Robert	Isele,	ChrisXan	Bizer:	Silk	-	GeneraXng	RDF	Links	while	Publishing	or	Consuming	
Linked	Data.	ISWC	Posters&Demos	2010	
Axel-Cyrille	Ngonga	Ngomo,	Sören	Auer:	LIMES	-	A	Time-Efficient	Approach	for	Large-Scale	Link	Discovery	
on	the	Web	of	Data.	IJCAI	2011:	2312-2317	
Christophe	Debruyne,	Eamonn	Clinton,	Declan	O'Sullivan:	Client-side	Processing	of	GeoSPARQL	FuncXons	
with	Triple	Paaern	Fragments.	LDOW@WWW	2017
www.adaptcentre.ieApproach	–	Interlinking	
	rr:predicateObjectMap	[	
				rr:predicate	geo:asWKT	;	
				rr:objectMap	[	
						rr:template	"POINT({LONG}	{LAT})"	;	
						rr:termType	rr:Literal	;	
						rr:datatype	geo:wktLiteral	;	
				]	;	
		]	;	
A	 B	
C	
D	
ExecuXng	a	CONSTRUCT	query	with		
A.  OSi's	TPF	server	
B.  the	local	TPF	server	
C.  the	query	(using	our	extension),	and	
D.  the	resulXng	RDF	graph.	
	
Runs	in	a	browser,	and	one	does	not	need	
to	install	and	populate	a	GeoSPARQL-
enabled	triplestore.	
	
Straigh~orward	federated	querying.	
Extending	the	R2RML	Mapping
www.adaptcentre.ieApproach	
Two	possible	approaches	to	extending	TPF	with	GeoSPARQL:	
	
A)	Extending	a	TPF	Client	
•  TPF	server	specificaXon	intact	(backwards	compaXble)	
•  Possibly	more	network	overhead	
	
B)	Extending	the	TPF	server	
•  Outside	server	specificaXon,	but	proven	to	be	viable	for	
substring	filtering	(Van	Herwegen	et	al.	2015)	
Addi9onal	requirement:	a	pure	JavaScript	implementa9on		
•  Allows	one	to	run	the	client	in	a	browser	and	hence	facilitate	
stakeholders	in	formulaXng	GeoSPARQL	queries	
J.	Van	Herwegen,	L.	De	Vocht,	R.	Verborgh,	E.	Mannens,	and	R.	Van	de	Walle.	2015.	Substring	Filtering	for	Low-Cost	
Linked	Data	Interfaces.	In	The	SemanXc	Web	-	ISWC	2015	-	14th	InternaXonal	SemanXc	Web	Conference,	
Bethlehem,	PA,	USA,	October	11-15,	2015,	Proceedings,	Part	I	(LNCS),	Vol.	9366.	Springer,	128–143.
www.adaptcentre.ieApproach	–	DownliX	
1.  Merge	generated	RDF	and	links	
2.  Provide	downlif	algorithm		
1.  The	merged	RDF	
2.  The	R2RML	mapping	–	to	reconsXtute	the	original	file	
3.  A	set	of	predicates	(URIs)	–	for	the	addiXonal	columns.		
Empty	values	will	be	generated	when	no	links	exist	
Name	
Weather	
Reading	 …	 hJp://www.opengis.net/ont/geosparql#within	
M50	
Blanchardstown		 hap://…	 …	hap://data.geohive.ie/resource/county/2AE19629144F13A3E055000000000001	
M50	Dublin	Airport		hap://…	 …	hap://data.geohive.ie/resource/county/2AE19629144F13A3E055000000000001	
Dublin	Airport		 hap://…	 …	hap://data.geohive.ie/resource/county/2AE19629144F13A3E055000000000001
www.adaptcentre.ieImplementa9on	
On	the	tools	extension	for	a	TPF	Client	
•  Uplif:	haps://opengogs.adaptcentre.ie/debruync/generate-mapping	
•  Downlif:	haps://opengogs.adaptcentre.ie/debruync/downlif	
•  Our	R2RML	engine:	haps://opengogs.adaptcentre.ie/debruync/r2rml		
•  All	available	with	accessible	licenses	(MIT)	
	
On	the	GeoSPARQL	extension	for	a	TPF	Client	
•  We	extended	V2.0.4	of	the	TPF	Node.js	Client	
(Available	at	haps://github.com/chrdebru/Client.js)	
•  Made	available	a	web-client	using	this	extension	
(At	hap://theme-e.adaptcentre.ie/geo-tpf/)
www.adaptcentre.ieDiscussion	
On	the	meaning	of	the	generated	RDF	and	enriched	CSV	
•  Goal	was	to	enrich	non-RDF	with	a	geospaXal	component,	that	also	
allowed	one	also	to	engage	with	that	data	
•  The	mappings	that	are	generated	are	not	necessarily	meaningful;	the	
generated	RDF	is	just	as	meaningful	as	the	original	file	and	users	are	not	
obliged	to	adopt	established	vocabularies,	but	they	can…	
•  AdopXon	of	established	vocabularies	would	allow	for	more	meaningful	
links,	which	are	then	stored	back	into	the	CSV,	and	the	mapping	would	
provide	a	basis	for	creaXng	meaningful	RDF,	and	even	Linked	Data.	
On	the	datasets	
•  We	used	a	simple	dataset	and	only	demonstrated	SPARQL	CONSTRUCT	
with	GeoSPARQL.	More	exercises	are	conducted	in	the	context	of	the	
Open	Data	Engagement	Fund;	to	be	reported.
www.adaptcentre.ieDiscussion	(not	in	paper	–	new)	
Apprecia9on	during	public	seminars	
•  About	40	registered	aaendees	from	academia,	government	agencies,	and	
industry	held	on	the	4th	and	10th	of	May.	
•  First	seminar	on	Linked	Data	(principles	and	case	studies)	
•  Second	seminar	on	Linked	Data	publishing	uplif	
•  A	recurring	concern	about	Linked	Data	publishing	and	interlinking	is	
relying	on	third	parXes	to	conduct	the	exercise,	and	how	one	can	acquire	
the	experXse.		
•  Simulated	direct	mapping,	and	its	generator,	was	appreciated	as	it	
allowed	to	incrementally	generate	"meaningful"	linked	data.	Downlif	was	
appreciated	as	it	facilitated	relying	on	their	exisXng	processes.
www.adaptcentre.ieWith	respect	to	related	work…	
The	GeoKnow	EU	FP7	Project	(hap://www.geoknow.eu/)	
•  Relies	on	a	parXcular	sofware	stack	
•  No	explicit	noXon	of	downlif	
•  Provides	support	uplif	of	unstructured	text	and	shapefiles	
Our	approach	was	aimed	to	be	lightweight,	but	
we	are	aware	that	approach	will	likely	not	scale	
as	well	as	bespoke	systems	built	for	this	
parXcular	purpose.	Our	goal	was	to	leverage	
the	enrichment	of	non-RDF	open	data	with	
a	geospaXal	dimension	with		
semanXc	technologies.
www.adaptcentre.ieSummary,	lessons	learned,	and	future	work	
Summary	
•  We	presented	a	lightweight	approach	to	uplif,	interlinking	and	enriching,	
and	downlif,	as	well	as	engage	with	that	informaXon.	
•  Developed	in	the	context	of	a	iniXaXve	to	improve	the	usage	and	
availability	of	open	data	on	the	Irish	open	data	portal	
•  Prototypes	have	been	made	available	with	an	accessible	license	
Lessons	learned	
•  We	deem	our	approach	viable.	Even	though	we	are	aware	our	approach	
will	not	outperform	certain	bespoke	systems	in	terms	of	GeoSPARQL	
processing	and	analysis,	our	goal	was	to	leverage	aforemenXoned	
processes.	
Future	work	
•  User	studies,	and	inclusion	of	governance	aspects	
•  AddiXonal	exercises	with	other	datasets	to	be	reported	in	a	whitepaper

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