A geometry may occur as an object of an RDF quad. The SQL MM functions can then be used for querying for geometries.
For geometry functions, see the SQL Geometry support section.
A geometry may occur as an object value in an RDF quad. In such a case, the bare geometry object is not used but instead a special RDF typed literal is made with the type virtrdf:Geometry. Such a literal is automatically indexed in an R tree index containing all distinct geometries occurring in any quad of any graph under any predicate. Normally, WGS84, SRID 4326 is the SRID of any such geometry.
In this section, the geo namespace prefix is used to mean <http://www.w3.org/2003/01/geo/wgs84_pos#>.
The preferred way of adding geometries to RDF graphs is with the ttlp and related functions which parse a text string in the Turtle syntax and insert the result in a graph.
For example:
ttlp ('@prefix virtrdf: <http://www.openlinksw.com/schemas/virtrdf#>
<point> geo:geometry "point(1.2 22.4"^^virtrdf:Geometry .',
'xxx', 'graph');
A typed literal whose text is a WKT representation of a geometry and whose type is virtrdf:geometry creates a geometry object and adds it to the R tree index of all RDF geometries.
Geometries can be queried with geometry predicates, st_intersects, st_contains and st_within, as follows. As usual, the bif: namespace is used since these are SQL built-in functions.
SQL>
SPARQL
SELECT ?c COUNT (*)
WHERE
{
?m geo:geometry ?geo .
?m a ?c .
FILTER (bif:st_intersects (?geo, bif:st_point (0, 52), 100))
}
GROUP BY ?c
ORDER BY DESC 2;
c callret-1
VARCHAR VARCHAR
____________________________________________________________
http://linkedgeodata.org/vocabulary#node 2317684
http://linkedgeodata.org/vocabulary#way 85315
http://linkedgeodata.org/vocabulary#building 14257
http://dbpedia.org/class/yago/Landmark108624891 9093
http://linkedgeodata.org/vocabulary#wood 7155
http://linkedgeodata.org/vocabulary#gate 7079
http://www.w3.org/2002/07/owl#Thing 6788
http://linkedgeodata.org/vocabulary#post_box 6144
http://linkedgeodata.org/vocabulary#pub 5697
http://dbpedia.org/ontology/Place 5670
http://linkedgeodata.org/vocabulary#hedge 5391
...
This would return the classes of things within 100 km of 0, 52, which is near London.
SQL>
SPARQL
SELECT ?m (bif:st_distance (?geo, bif:st_point (0, 52)))
WHERE
{
?m geo:geometry ?geo .
?m a <http://dbpedia.org/ontology/City> .
FILTER (bif:st_intersects (?geo, bif:st_point (0, 52), 30))
}
ORDER BY DESC 2
LIMIT 20;
m callret-1
VARCHAR VARCHAR
_______________________________________________________________________________
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 39.13180985471543
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 39.13180985471543
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 39.13180985471543
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 39.13180985471543
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 37.36907252285992
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 34.49432513061792
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 33.7676326404143
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 33.24238654570499
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 32.60139660515003
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 32.60139660515003
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 32.17414911350438
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 31.45681319171456
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 31.17750625349044
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 31.115377038
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 31.115377038
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 30.56388658524301
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 29.89662974046085
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 29.85090625132639
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 29.82605254366244
http://dbpedia.org/resource/Kingston%2C_Cambridgeshire 29.60102064794003
20 Rows. -- 13600 msec.
This would be the cities within 20 km of 0, 52, ordered by increasing distance from this point.
When SPARQL is called from SQL, the geometries can be bound to SQL variables as anything else returned from SPARQL. The st_ functions can then be used for retrieving properties of these objects.
The ttlp function is the preferred way of inserting geometries. The more are inserted at one time, the more efficient the operation is. This loader function will also deal with cluster message optimization.
For deleting quads with geometries, normal SPARUL operations apply.
A geometry occurring in an RDF quad object is a member of the RDF box data type. This data type stands for a typed RDF literal. The type of all geometries is 256. This is mapped to a URI in the RDF_DATATYPE system table.
A geometry does not occur directly in the object position of a quad. It is referenced by an id that is stored in the RDF typed literal box and references RO_ID of the RDF_OBJ system table. To translate a geometry into a RDF box that can be stored, do as in the example below:
INSERT INTO RDF_QUAD (g, s, p, o)
VALUES (
"g",
"s",
iri_to_id ('http://www.w3.org/2003/01/geo/wgs84_pos#geometry'),
DB.DBA.rdf_geo_add (rdf_box (st_point (lng, lat), 256, 257, 0, 1)));
The DB.DBA.RDF_GEO_ADD function looks if an identical geometry already exists and if so assigns the existing id to it. If the geometry is new, it gets a new ID and is stored in the RDF literals table RDF_OBJ. At this time it is also automatically inserted into the RDF geometry index.
In a cluster situation one should use the dpipe mechanism for inserting into RDF quad so as to get large numbers of inserts into a single message. This is essential for performance.
Many data sets use the geo:lat and geo:long properties for describing a position. Virtuoso comes with a function for converting these properties into geometries. This operation reads through all graphs and for each subject with at least one geo:lat and geo:long, a point geometry is made for each distinct lat/long pair where lat and long are in the same graph. It should not happen in practice that a single subject has multiple lats or long within one graph. If this still happens, a geometry is made for each combination. The geometry is added to the subject with the lat and long as the value of the geo:geometry property. This is added to the same graph where the lat and long were.
The SQL procedure DB.DBA.RDF_GEO_FILL () performs this operation. This is performed in parallel on multiple threads and is optimized for cluster execution. This is done without transaction logging and is not transactional. To make the result persistent, the operator should do an explicit checkpoint. This is done by executing:
SQL>cl_exec ('checkpoint');
on any process of a cluster or single server. Otherwise the result may be lost if the server terminates abnormally before an automatic checkpoint is made.
The DB.DBA.RDF_GEO_FILL procedure may in principle be called several times but it will read every lat and long in the database. This is inefficient if there are large numbers of geometries.
Application logic must generally be used for constructing geometries and adding these to RDF subjects. It is easiest for the application to construct a text representation of the geometries in TTL and to use the ttlp function for loading this.
The geometry feature is compatible with any Virtuoso 6 databases. Once geometries are used, the database should not be opened with a server older than the one used for first inserting geometries, older servers will consider the storage format a physical corruption.
## Get All Stuff For Given Coordinates
SQL>SPARQL
SELECT ?c COUNT (*)
WHERE
{
?m geo:geometry ?geo .
?m a ?c .
FILTER (bif:st_intersects (?geo, bif:st_point (0, 52), 100))
}
GROUP BY ?c
ORDER BY desc 2;
c callret-1
VARCHAR VARCHAR
_______________________________________________________________________________
http://linkedgeodata.org/vocabulary#node 2317684
http://linkedgeodata.org/vocabulary#way 85315
http://linkedgeodata.org/vocabulary#building 14257
http://dbpedia.org/class/yago/Landmark108624891 9093
http://linkedgeodata.org/vocabulary#wood 7155
....
## Get City Stuff Around Catholic Churches In Paris
SQL>
SPARQL
SELECT ?m (bif:st_distance (?geo, bif:st_point (0, 52)))
WHERE
{
?m geo:geometry ?geo .
?m a <http://dbpedia.org/ontology/City> .
FILTER (bif:st_intersects (?geo, bif:st_point (0, 52), 30))
}
ORDER BY DESC 2
LIMIT 20;
m callret-1
VARCHAR VARCHAR
_______________________________________________________________________________
http://dbpedia.org/resource/Stansted_Mountfitchet 39.13180985471543
http://dbpedia.org/resource/Stansted_Mountfitchet 39.13180985471543
http://dbpedia.org/resource/Stansted_Mountfitchet 39.13180985471543
http://dbpedia.org/resource/Stansted_Mountfitchet 39.13180985471543
http://dbpedia.org/resource/Stansted_Mountfitchet 37.36907252285992
http://dbpedia.org/resource/Stansted_Mountfitchet 34.49432513061792
http://dbpedia.org/resource/Stansted_Mountfitchet 33.7676326404143
http://dbpedia.org/resource/Stansted_Mountfitchet 33.24238654570499
http://dbpedia.org/resource/Stansted_Mountfitchet 32.60139660515003
http://dbpedia.org/resource/Stansted_Mountfitchet 32.60139660515003
http://dbpedia.org/resource/Stansted_Mountfitchet 31.45681319171456
http://dbpedia.org/resource/Stansted_Mountfitchet 31.115377038
http://dbpedia.org/resource/Stansted_Mountfitchet 31.115377038
http://dbpedia.org/resource/Stansted_Mountfitchet 30.56388658524301
http://dbpedia.org/resource/Stansted_Mountfitchet 29.89662974046085
http://dbpedia.org/resource/Stansted_Mountfitchet 29.85090625132639
http://dbpedia.org/resource/Stansted_Mountfitchet 29.82605254366244
http://dbpedia.org/resource/Stansted_Mountfitchet 29.60102064794003
http://dbpedia.org/resource/Stansted_Mountfitchet 29.44147385851453
http://dbpedia.org/resource/Stansted_Mountfitchet 29.421242437379
## Get City Stuff Around Catholic Churches In Paris Extended
SQL>
SPARQL
SELECT ?m (bif:st_distance (?geo, bif:st_point (0, 52)))
WHERE
{
?m geo:geometry ?geo .
?m a <http://dbpedia.org/ontology/City> .
FILTER (bif:st_intersects (?geo, bif:st_point (0, 52), 100))
}
ORDER BY DESC 2
LIMIT 20;
m callret-1
VARCHAR VARCHAR
_______________________________________________________________________________
http://dbpedia.org/resource/Weston-on-Trent 138.7082197019335
http://dbpedia.org/resource/Weston-on-Trent 137.7213767969613
http://dbpedia.org/resource/Weston-on-Trent 136.4597167847218
http://dbpedia.org/resource/Weston-on-Trent 134.1807668663677
http://dbpedia.org/resource/Weston-on-Trent 133.104337839536
http://dbpedia.org/resource/Weston-on-Trent 133.104337839536
http://dbpedia.org/resource/Nonington 132.7368236183588
http://dbpedia.org/resource/Nonington 132.1339163200362
http://dbpedia.org/resource/Nonington 132.1339163200362
http://dbpedia.org/resource/Nonington 130.5478483560461
http://dbpedia.org/resource/Nonington 130.1620410981843
http://dbpedia.org/resource/Nonington 129.8549842943355
http://dbpedia.org/resource/Nonington 129.6459280567849
http://dbpedia.org/resource/Nonington 129.4504858595742
http://dbpedia.org/resource/Nonington 129.2790713235814
http://dbpedia.org/resource/Nonington 128.9081040147881
http://dbpedia.org/resource/Nonington 128.8845164618929
http://dbpedia.org/resource/Nonington 128.6676189617872
http://dbpedia.org/resource/Nonington 128.2565253458452
http://dbpedia.org/resource/Nonington 128.2551696344652
20 Rows. -- 120 msec.
## Text Or Geo
SQL>
SPARQL
SELECT ?c COUNT (*)
WHERE
{
?m geo:geometry ?geo .
?m a ?c .
FILTER (bif:st_intersects (?geo, bif:st_point (0, 52), 100) && REGEX (str (?c), "London") )
}
GROUP BY ?c
ORDER BY DESC 2
LIMIT 10;
c callret-1
____________________________________________________________________________
http://dbpedia.org/class/yago/DistrictsOfLondon 861
http://dbpedia.org/class/yago/GradeIListedBuildingsInLondon 199
http://dbpedia.org/class/yago/MuseumsInLondon 107
http://dbpedia.org/class/yago/ArtMuseumsAndGalleriesInLondon 92
http://dbpedia.org/class/yago/GradeIIListedBuildingsInLondon 89
http://dbpedia.org/class/yago/SportsVenuesInLondon 80
http://dbpedia.org/class/yago/RoyalBuildingsInLondon 72
http://dbpedia.org/class/yago/LondonOvergroundStations 69
http://dbpedia.org/class/yago/NationalGovernmentBuildingsInLondon 69
http://dbpedia.org/class/yago/SkyscrapersInLondon 60
## Example "Places Of Worship, Within 5 km Of Paris":
## Describes places of worship, within 5 km of Paris,
## that have cafes in close proximity(0.2 km).
## The query requires V6 or higher.
SQL>
PREFIX lgv: <http://linkedgeodata.org/vocabulary#>
DESCRIBE ?cafe ?church
WHERE
{
?church a lgv:place_of_worship .
?church geo:geometry ?churchgeo .
?church lgv:name ?churchname .
?cafe a lgv:cafe .
?cafe lgv:name ?cafename .
?cafe geo:geometry ?cafegeo .
?cafe geo:lat ?lat .
?cafe geo:long ?long .
FILTER ( bif:st_intersects ( ?churchgeo, bif:st_point ( 2.3498, 48.853 ), 5 ) &&
bif:st_intersects ( ?cafegeo, ?churchgeo, 0.2 ) )
}
LIMIT 10;
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix ns1: <http://linkedgeodata.org/triplify/node/243360870#> .
@prefix ns2: <http://linkedgeodata.org/vocabulary#> .
ns1:id rdf:type ns2:place_of_worship ,
ns2:node .
@prefix geo: <http://www.w3.org/2003/01/geo/wgs84_pos#> .
ns1:id geo:lat 48.8794 ;
geo:long 2.3748 ;
ns2:created_by "Potlatch 0.6c" ;
ns2:name "Saint-Georges de la Villette" ;
ns2:religion "christian" ,
ns2:christian .
@prefix virtrdf: <http://www.openlinksw.com/schemas/virtrdf#> .
ns1:id geo:geometry "POINT(2.3748 48.8794)"^^virtrdf:Geometry .
@prefix ns5: <http://linkedgeodata.org/triplify/node/266632049#> .
ns5:id rdf:type ns2:node ,
ns2:cafe ;
geo:lat 48.8518 ;
geo:long 2.325 ;
ns2:created_by "Potlatch 0.9a" ;
ns2:name "Le Babylone" ;
geo:geometry "POINT(2.325 48.8518)"^^virtrdf:Geometry .
....
## Count Geo
SQL>
SPARQL
SELECT ?c COUNT (*)
WHERE
{
?s geo:geometry ?geo .
FILTER (bif:st_intersects (?geo, bif:st_point (2.3498, 48.853), 5)) .
?s a ?c
}
GROUP BY ?c
ORDER BY desc 2
LIMIT 10;
c callret-1
VARCHAR VARCHAR
_______________________________________________________________________________
http://linkedgeodata.org/vocabulary#node 37792
http://dbpedia.org/class/yago/Landmark108624891 4003
http://linkedgeodata.org/vocabulary#way 1688
http://linkedgeodata.org/vocabulary#building 719
http://linkedgeodata.org/vocabulary#station 257
http://linkedgeodata.org/vocabulary#post_box 247
http://www.w3.org/2002/07/owl#Thing 227
http://linkedgeodata.org/vocabulary#park 208
http://linkedgeodata.org/vocabulary#restaurant 198
http://dbpedia.org/ontology/Place 192
10 Rows. -- 932 msec.
## Get Stuff Around Notre Dame De Paris
SQL>
SPARQL
SELECT ?c COUNT (*)
WHERE
{
?s a ?c .
?s geo:geometry ?geo .
FILTER (bif:st_intersects (?geo, bif:st_point (2.3498, 48.853), 0.3))
}
GROUP BY ?c
ORDER BY desc 2
LIMIT 10;
c callret-1
VARCHAR VARCHAR
_______________________________________________________________________________
http://linkedgeodata.org/vocabulary#node 408
http://dbpedia.org/class/yago/Landmark108624891 134
http://linkedgeodata.org/vocabulary#way 17
http://dbpedia.org/class/yago/RomanCatholicChurchesInParis 17
http://dbpedia.org/class/yago/TallBuildingsAndStructuresInParis 13
http://dbpedia.org/class/yago/CathedralsInFrance 13
http://sw.opencyc.org/2008/06/10/concept/Mx4rvVigPpwpEbGdrcN5Y29ycA 13
http://sw.opencyc.org/2008/06/10/concept/Mx4rjm5QanS6EdaAAACgyZzFrg 13
http://sw.opencyc.org/2008/06/10/concept/Mx4rwQwtGpwpEbGdrcN5Y29ycA 13
http://www.w3.org/2002/07/owl#Thing 10
10 Rows. -- 241 msec.
## Things within 10 km proximity of place of worship
SQL>
SPARQL
PREFIX lgv: <http://linkedgeodata.org/vocabulary#>
SELECT ?c COUNT (*)
WHERE
{
?s a ?c .
?s a lgv:place_of_worship .
?s geo:geometry ?geo .
FILTER (bif:st_intersects (?geo, bif:st_point (2.3498, 48.853), 10))
}
GROUP BY ?c
ORDER BY desc 2
LIMIT 10;
c callret-1
VARCHAR VARCHAR
_______________________________________________________________________________
http://linkedgeodata.org/vocabulary#place_of_worship 147
http://linkedgeodata.org/vocabulary#node 146
http://linkedgeodata.org/vocabulary#way 46
http://linkedgeodata.org/vocabulary#building 36
http://linkedgeodata.org/vocabulary#attraction 3
http://linkedgeodata.org/vocabulary#church 1
6 Rows. -- 120 msec.
## Get Stuff Around Notre Dame De Paris with Names
SQL>
SPARQL
PREFIX lgv: <http://linkedgeodata.org/vocabulary#>
SELECT ?cn
WHERE
{
?s lgv:name ?cn .
?s geo:geometry ?geo .
FILTER (bif:st_intersects (?geo, bif:st_point (2.3498, 48.853), 0.3))
}
LIMIT 20;
cn
VARCHAR
_______________________________________________________________________________
Parking Lagrange
Maitre Albert B&B
Le Grenier de Notre Dame
Eglise Saint-Julien-le-Pauvre
Eglise Saint Julien le Pauvre
Polly Magoo
Point 0 des Routes de France
Square Jean XXIII
....
20 Rows. -- 140 msec.
## Get Churches With The Most Bars
SQL>
SPARQL
PREFIX lgv: <http://linkedgeodata.org/vocabulary#>
SELECT ?churchname ?cafename (bif:st_distance (?churchgeo, ?cafegeo))
WHERE
{
?church a lgv:place_of_worship .
?church geo:geometry ?churchgeo .
?church lgv:name ?churchname .
?cafe a lgv:cafe .
?cafe lgv:name ?cafename .
?cafe geo:geometry ?cafegeo .
FILTER (bif:st_intersects (?churchgeo, bif:st_point (2.3498, 48.853), 5)
&& bif:st_intersects (?cafegeo, ?churchgeo, 0.2))
}
LIMIT 10;
churchname cafename callret-2
VARCHAR VARCHAR VARCHAR
_______________________________________________________________________________
Eglise Saint-Julien-le-Pauvre Le Saint R+?-?gis 0.09759308692691648
Eglise Saint-Germain des Pr+?-?s Caf+?-? de Flore 0.08774468391412803
Eglise Saint-Germain des Pr+?-?s Les Deux Magots 0.05235923473923059
Eglise Saint-Germain des Pr+?-?s Caf+?-? Mabillon 0.1712042770289815
Eglise Saint-Germain-des-Pr+?-?s Caf+?-? de Flore 0.1466502865197912
Eglise Saint-Germain-des-Pr+?-?s Les Deux Magots 0.1096767137079839
Eglise Saint-Germain-des-Pr+?-?s Bar du march+?-? 0.1831441251868126
Eglise Saint-Germain-des-Pr+?-?s Caf+?-? Mabillon 0.1174051745495528
Synagogue La Chaise au Plafond 0.1038387283609551
Synagogue Le Loir dans la Th+?-?i+?-?re 0.1632848322062273
10 Rows. -- 511225 msec.
## Things around highly populated places
SQL>
SPARQL
SELECT ?s ( sql:num_or_null (?o) ) COUNT (*)
WHERE
{
?s <http://dbpedia.org/ontology/populationTotal> ?o .
FILTER ( sql:num_or_null (?o) > 6000000 ) .
?s geo:geometry ?geo .
FILTER ( bif:st_intersects (?pt, ?geo,2) ) .
?xx geo:geometry ?pt
}
GROUP BY ?s ( sql:num_or_null (?o) )
ORDER BY desc 3
LIMIT 20;
s callret-1 callret-2
VARCHAR VARCHAR VARCHAR
_______________________________________________________________________________
http://dbpedia.org/resource/London 7556900 312307
http://dbpedia.org/resource/Toronto 8102163 115859
http://dbpedia.org/resource/New_York_City 8363710 95629
http://dbpedia.org/resource/The_Hague 6659300 84410
http://dbpedia.org/resource/Tokyo 12790000 78618
http://dbpedia.org/resource/Philadelphia 6385461 67115
http://dbpedia.org/resource/Los_Angeles 17755322 64394
http://dbpedia.org/resource/Bangkok 8160522 62519
http://dbpedia.org/resource/Barcelona 2147483648 57635
http://dbpedia.org/resource/Cairo 6758581 52738
http://dbpedia.org/resource/Istanbul 12697164 50745
http://dbpedia.org/resource/Seoul 10421782 43962
http://dbpedia.org/resource/Beijing 17430000 35979
http://dbpedia.org/resource/Purmerend 6659300 33508
http://dbpedia.org/resource/Baghdad 6554126 33426
http://dbpedia.org/resource/Bogot%C3%A1 6776009 30429
http://dbpedia.org/resource/Mexico_City 8836045 30127
http://dbpedia.org/resource/Jakarta 8500000 28944
http://dbpedia.org/resource/Boston 7514759 27705
http://dbpedia.org/resource/Baden-W%C3%BCrttemberg 10755000 25112
20 Rows. -- 4296 msec.
## Example "Places Of Worship, Within 5 km Of Paris":
## Constructs a custom Linked Data Mesh (graph) about
## places of worship, within 5 km of Paris, that have
## cafes in close proximity(0.2 km).
## Note: we have distinct pin colors that identify
## for places of worship distinct from cafes.
## The query requires V6 or higher.
SQL>
SPARQL
PREFIX lgv: <http://linkedgeodata.org/vocabulary#>
PREFIX rtb: <http://www.openlinksw.com/schemas/oat/rdftabs#>
CONSTRUCT
{
?cafe geo:geometry ?cafegeo ;
rtb:useMarker '01' ;
lgv:name ?cafename .
?church geo:geometry ?churchgeo ;
rtb:useMarker '02' ;
lgv:name ?churchname .
}
WHERE
{
?church a lgv:place_of_worship .
?church geo:geometry ?churchgeo .
?church lgv:name ?churchname .
?cafe a lgv:cafe .
?cafe lgv:name ?cafename .
?cafe geo:geometry ?cafegeo .
?cafe geo:lat ?lat .
?cafe geo:long ?long .
FILTER ( bif:st_intersects ( ?churchgeo, bif:st_point ( 2.3498, 48.853 ), 5 ) &&
bif:st_intersects ( ?cafegeo, ?churchgeo, 0.2 ) )
}
LIMIT 10;
@prefix ns0: <http://linkedgeodata.org/vocabulary#> .
@prefix ns1: <http://linkedgeodata.org/triplify/node/237435716#> .
ns1:id ns0:name "Chapelle du Val de Gr\u00C3\u00A2ce" .
@prefix ns2: <http://www.openlinksw.com/schemas/oat/rdftabs#> .
ns1:id ns2:useMarker "02" .
@prefix virtrdf: <http://www.openlinksw.com/schemas/virtrdf#> .
@prefix geo: <http://www.w3.org/2003/01/geo/wgs84_pos#> .
ns1:id geo:geometry "POINT(2.3418 48.8406)"^^virtrdf:Geometry .
@prefix ns5: <http://linkedgeodata.org/triplify/node/218147750#> .
ns5:id ns0:name "Synagogue" ;
ns2:useMarker "02" ;
geo:geometry "POINT(2.3593 48.857)"^^virtrdf:Geometry .
@prefix ns6: <http://linkedgeodata.org/triplify/node/218145208#> .
ns6:id ns0:name "Synagogue" ;
ns2:useMarker "02" ;
geo:geometry "POINT(2.3589 48.8567)"^^virtrdf:Geometry .
...
## Example "Places Of Worship, Within 5 km Of Paris":
## Asks for places of worship, within 5 km of Paris,
## that have cafes in close proximity(0.2 km).
## The query requires V6 or higher.
SQL>
SPARQL
PREFIX lgv: <http://linkedgeodata.org/vocabulary#>
ASK
WHERE
{
?church a lgv:place_of_worship .
?church geo:geometry ?churchgeo .
?church lgv:name ?churchname .
?cafe a lgv:cafe .
?cafe lgv:name ?cafename .
?cafe geo:geometry ?cafegeo .
?cafe geo:lat ?lat .
?cafe geo:long ?long .
FILTER ( bif:st_intersects ( ?churchgeo, bif:st_point ( 2.3498, 48.853 ), 5 ) &&
bif:st_intersects ( ?cafegeo, ?churchgeo, 0.2 ) )
};
Done.
true
## Places of worship, within 5 km of Paris,
## that have cafes in close proximity(0.2 km)
SQL>
SPARQL
PREFIX lgv: <http://linkedgeodata.org/vocabulary#>
SELECT DISTINCT ?cafe ?lat ?long ?cafename ?churchname
(bif:round(bif:st_distance (?churchgeo, ?cafegeo)))
WHERE
{
?church a lgv:place_of_worship .
?church geo:geometry ?churchgeo .
?church lgv:name ?churchname .
?cafe a lgv:cafe .
?cafe lgv:name ?cafename .
?cafe geo:geometry ?cafegeo .
?cafe geo:lat ?lat.
?cafe geo:long ?long.
FILTER ( bif:st_intersects (?churchgeo, bif:st_point (2.3498, 48.853), 5) &&
bif:st_intersects (?cafegeo, ?churchgeo, 0.2) )
}
LIMIT 10;
cafe lat long cafename churchname callret-5
VARCHAR VARCHAR VARCHAR VARCHAR VARCHAR VARCHAR
_______________________________________________________________________________________________________________________________________________________________
http://linkedgeodata.org/triplify/node/321932192#id 48.8522 2.3484 Le Saint R+?-?gis Eglise Saint-Julien-le-Pauvre 0
http://linkedgeodata.org/triplify/node/251699776#id 48.8541 2.3326 Caf+?-? de Flore Eglise Saint-Germain des Pr+?-?s 0
http://linkedgeodata.org/triplify/node/251699775#id 48.854 2.3331 Les Deux Magots Eglise Saint-Germain des Pr+?-?s 0
http://linkedgeodata.org/triplify/node/315769036#id 48.8533 2.3358 Caf+?-? Mabillon Eglise Saint-Germain des Pr+?-?s 0
http://linkedgeodata.org/triplify/node/251699776#id 48.8541 2.3326 Caf+?-? de Flore Eglise Saint-Germain-des-Pr+?-?s 0
http://linkedgeodata.org/triplify/node/251699775#id 48.854 2.3331 Les Deux Magots Eglise Saint-Germain-des-Pr+?-?s 0
http://linkedgeodata.org/triplify/node/315769035#id 48.8539 2.3371 Bar du march+?-? Eglise Saint-Germain-des-Pr+?-?s 0
http://linkedgeodata.org/triplify/node/315769036#id 48.8533 2.3358 Caf+?-? Mabillon Eglise Saint-Germain-des-Pr+?-?s 0
http://linkedgeodata.org/triplify/node/251126326#id 48.8572 2.3577 La Chaise au Plafond Synagogue 0
http://linkedgeodata.org/triplify/node/251043135#id 48.8562 2.361 Le Loir dans la Th+?-?i+?-?re Synagogue 0
10 Rows. -- 120 msec.
## Stuff around Notre Dame de Paris
SQL>
SPARQL
PREFIX lgv: <http://linkedgeodata.org/vocabulary#>
SELECT ?s ?cn ?lat ?long
WHERE
{
?s lgv:name ?cn .
?s geo:geometry ?geo .
?s geo:lat ?lat.
?s geo:long ?long.
FILTER ( bif:st_intersects (?geo, bif:st_point (2.3498, 48.853), 0.3) )
}
LIMIT 20;
s cn lat long
VARCHAR VARCHAR VARCHAR VARCHAR
______________________________________________________________________________________________________________
http://linkedgeodata.org/triplify/node/237004656#id Parking Lagrange 48.8506 2.3487
http://linkedgeodata.org/triplify/node/237003117#id Mus+?-?e de l'Assistance Publique H+?-¦pitaux de Paris 48.8507 2.3519
http://linkedgeodata.org/triplify/way/23071565#id Jardin de la Rue de Bi+?-?vre 48.8504 2.3502
http://linkedgeodata.org/triplify/node/251652818#id Maitre Albert B&B 48.8507 2.3496
http://linkedgeodata.org/triplify/node/251373384#id Le Grenier de Notre Dame 48.8513 2.35
http://linkedgeodata.org/triplify/node/205266764#id Eglise Saint-Julien-le-Pauvre 48.852 2.3471
http://linkedgeodata.org/triplify/way/19741083#id Eglise Saint Julien le Pauvre 48.8521 2.3469
http://linkedgeodata.org/triplify/node/251474112#id Polly Magoo 48.8526 2.3467
http://linkedgeodata.org/triplify/node/251531803#id H+?-¦tel Esmerelda 48.8523 2.3468
http://linkedgeodata.org/triplify/node/191031796#id Point 0 des Routes de France 48.8533 2.3489
http://linkedgeodata.org/triplify/way/20444455#id Square Jean XXIII 48.8529 2.3511
http://linkedgeodata.org/triplify/way/19740745#id Square Ren+?-? Viviani 48.8525 2.3476
http://linkedgeodata.org/triplify/node/321932192#id Le Saint R+?-?gis 48.8522 2.3484
http://linkedgeodata.org/triplify/node/27440965#id Notre-Dame de Paris 48.853 2.3499
http://linkedgeodata.org/triplify/node/243461762#id Parking Notre-Dame 48.8537 2.3475
http://linkedgeodata.org/triplify/way/21816758#id Notre-Dame de Paris 48.8531 2.349
http://linkedgeodata.org/triplify/way/22972062#id La Seine 48.8538 2.3531
http://linkedgeodata.org/triplify/way/25463927#id La Seine 48.8548 2.3518
http://linkedgeodata.org/triplify/node/251128395#id H+?-¦tel Hospitel 48.854 2.3484
http://linkedgeodata.org/triplify/way/14155323#id H+?-¦tel Dieu 48.8555 2.3485
20 Rows. -- 167 msec.
## Stuff around Notre Dame de Paris
SQL>
SPARQL
PREFIX lgv: <http://linkedgeodata.org/vocabulary#>
DESCRIBE ?s
WHERE
{
?s lgv:name ?cn .
?s geo:geometry ?geo .
?s geo:lat ?lat.
?s geo:long ?long.
FILTER (bif:st_intersects (?geo, bif:st_point (2.3498, 48.853), 0.3))
}
LIMIT 20;
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix ns1: <http://linkedgeodata.org/triplify/node/27440966#> .
@prefix ns2: <http://linkedgeodata.org/vocabulary#> .
ns1:id rdf:type ns2:node ,
ns2:police .
@prefix geo: <http://www.w3.org/2003/01/geo/wgs84_pos#> .
ns1:id geo:lat 48.8542 ;
geo:long 2.3473 ;
ns2:created_by "Potlatch 0.6a" ;
ns2:name "Pr\u00C3\u00A9fecture de Police de Paris" ,
"Pr\u00E9fecture de Police de Paris" .
@prefix virtrdf: <http://www.openlinksw.com/schemas/virtrdf#> .
ns1:id geo:geometry "POINT(2.3473 48.8542)"^^virtrdf:Geometry .
@prefix ns5: <http://linkedgeodata.org/triplify/node/27440965#> .
ns5:id rdf:type ns2:node ,
ns2:place_of_worship ;
geo:lat 48.853 ;
geo:long 2.3499 ;
ns2:denomination "catholic" ;
ns2:name "Notre-Dame de Paris" ;
ns2:religion "christian" ,
ns2:christian ;
geo:geometry "POINT(2.3499 48.853)"^^virtrdf:Geometry .
......
## Cities within 30 km proximity of London
SQL>
SPARQL
SELECT ?m (bif:round(bif:st_distance (?geo, ?gm)))
WHERE
{
<http://dbpedia.org/resource/London> geo:geometry ?gm .
?m geo:geometry ?geo .
?m a <http://dbpedia.org/ontology/City> .
FILTER (bif:st_intersects (?geo, ?gm, 30))
}
ORDER BY DESC 2
LIMIT 20;
m callret-1
VARCHAR VARCHAR
____________________________________________________________
http://dbpedia.org/resource/Bletchingley 30
http://dbpedia.org/resource/Bletchingley 30
http://dbpedia.org/resource/Bletchingley 30
http://dbpedia.org/resource/Bletchingley 30
http://dbpedia.org/resource/Bletchingley 30
http://dbpedia.org/resource/Bletchingley 30
http://dbpedia.org/resource/Bletchingley 30
http://dbpedia.org/resource/Bletchingley 30
http://dbpedia.org/resource/Bletchingley 30
http://dbpedia.org/resource/Bletchingley 30
http://dbpedia.org/resource/Bletchingley 30
http://dbpedia.org/resource/Bletchingley 30
http://dbpedia.org/resource/Bletchingley 30
http://dbpedia.org/resource/Bletchingley 30
http://dbpedia.org/resource/Bletchingley 30
http://dbpedia.org/resource/Ebbsfleet_Valley 30
http://dbpedia.org/resource/Bletchingley 30
http://dbpedia.org/resource/Bletchingley 30
http://dbpedia.org/resource/Bletchingley 30
http://dbpedia.org/resource/Bletchingley 30
20 Rows. -- 727666 msec.
## Motorways across England & Scotland from DBpedia
SQL>
SPARQL
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX dbpprop: <http://dbpedia.org/property/>
PREFIX yago: <http://dbpedia.org/class/yago/>
SELECT ?road ?services ?lat ?long
WHERE
{
{
?services dbpprop:road ?road .
?road a yago:MotorwaysInEngland .
?services dbpprop:lat ?lat .
?services dbpprop:long ?long .
}
UNION
{
?services dbpprop:road ?road .
?road a yago:MotorwaysInScotland .
?services dbpprop:lat ?lat .
?services dbpprop:long ?long .
}
}
LIMIT 20;
road services lat long
VARCHAR VARCHAR VARCHAR VARCHAR
______________________________________________________________________________________________________________________________________
http://dbpedia.org/resource/M90_motorway http://dbpedia.org/resource/Kinross_services 56.209628 -3.439257
http://dbpedia.org/resource/M1_motorway http://dbpedia.org/resource/Leicester_Forest_East_services 52.6192 -1.206
http://dbpedia.org/resource/M1_motorway http://dbpedia.org/resource/Woodall_services 53.3152 -1.2813
http://dbpedia.org/resource/M1_motorway http://dbpedia.org/resource/Tibshelf_services 53.13708 -1.33179
http://dbpedia.org/resource/M1_motorway http://dbpedia.org/resource/London_Gateway_services 51.631 -0.264
http://dbpedia.org/resource/M1_motorway http://dbpedia.org/resource/Donington_Park_services 52.823651 -1.305887
http://dbpedia.org/resource/M1_motorway http://dbpedia.org/resource/Watford_Gap_services 52.3069 -1.1226
http://dbpedia.org/resource/M1_motorway http://dbpedia.org/resource/Newport_Pagnell_services 52.083066 -0.748508
http://dbpedia.org/resource/M1_motorway http://dbpedia.org/resource/Trowell_services 52.963198 -1.265988
http://dbpedia.org/resource/M1_motorway http://dbpedia.org/resource/Woolley_Edge_services 53.62259 -1.549422
http://dbpedia.org/resource/M1_motorway http://dbpedia.org/resource/Toddington_services 51.9478 -0.502075
http://dbpedia.org/resource/M1_motorway http://dbpedia.org/resource/Northampton_services 52.209201 -0.944799
http://dbpedia.org/resource/M4_motorway http://dbpedia.org/resource/Chieveley_services 51.449 -1.3112
http://dbpedia.org/resource/M4_motorway http://dbpedia.org/resource/Magor_services 51.58786 -2.83713
http://dbpedia.org/resource/M4_motorway http://dbpedia.org/resource/Pont_Abraham_services 51.74712 -4.0655
http://dbpedia.org/resource/M4_motorway http://dbpedia.org/resource/Swansea_services 51.678197 -3.994646
http://dbpedia.org/resource/M4_motorway http://dbpedia.org/resource/Leigh_Delamere_services 51.511528 -2.159468
http://dbpedia.org/resource/M4_motorway http://dbpedia.org/resource/Reading_services 51.424527 -1.035633
http://dbpedia.org/resource/M4_motorway http://dbpedia.org/resource/Cardiff_West_services 51.50626 -3.30535
http://dbpedia.org/resource/M4_motorway http://dbpedia.org/resource/Heston_services 51.48807 -0.39106
20 Rows. -- 531 msec.
SELECT DISTINCT ?s (bif:round(?lat)) as ?lat (bif:round(?long)) as ?long
WHERE
{
{
SELECT ?g ?s WHERE
{
graph ?g {
?s geo:geometry ?geo }
}
LIMIT 100
}
graph ?g {
?s geo:lat ?lat .
?s geo:long ?long . }
FILTER (datatype (?lat) in (xsd:integer, xsd:float, xsd:double)) .
FILTER (datatype (?long) in (xsd:integer, xsd:float, xsd:double))
}
s lat long
ANY ANY ANY
________________________________________________________________________________________________
http://dbpedia.org/resource/QUaD -90 -139
http://dbpedia.org/resource/Amundsen-Scott_South_Pole_Station -90 -139
http://dbpedia.org/resource/Amundsen-Scott_South_Pole_Station -90 0
http://dbpedia.org/resource/Degree_Angular_Scale_Interferometer -90 -139
http://dbpedia.org/resource/South_Pole_Telescope -90 -139
http://dbpedia.org/resource/Arcminute_Cosmology_Bolometer_Array_Receiver -90 -139
http://dbpedia.org/resource/Viper_telescope -90 -139
http://dbpedia.org/resource/Mount_Weaver -87 -154
http://dbpedia.org/resource/Axel_Heiberg_Glacier -85 -163
http://dbpedia.org/resource/Mount_Ray -85 -171
http://linkedgeodata.org/triplify/node/275487234#id -85 -142
http://linkedgeodata.org/triplify/node/303732928#id -85 -142
http://linkedgeodata.org/triplify/node/332036611#id -85 -85
http://linkedgeodata.org/triplify/node/303732935#id -85 -143
http://linkedgeodata.org/triplify/node/303732951#id -85 -144
http://linkedgeodata.org/triplify/node/303732953#id -85 -144
http://linkedgeodata.org/triplify/node/276208684#id -85 -166
## "Find things within 20km of New York City":
SELECT DISTINCT ?resource ?label ?location
WHERE
{
<http://dbpedia.org/resource/New_York_City>
geo:geometry ?sourcegeo .
?resource geo:geometry ?location ;
rdfs:label ?label .
FILTER( bif:st_intersects( ?location, ?sourcegeo, 20 ) ) .
FILTER( lang(?label) = "en" )
}
## "Find Distance between New York City
## and London, England":
SELECT ( bif:st_distance( ?nyl,?ln ) )
AS ?distanceBetweenNewYorkCityAndLondon
WHERE
{
<http://dbpedia.org/resource/New_York_City>
geo:geometry ?nyl .
<http://dbpedia.org/resource/London>
geo:geometry ?ln .
}
## "Find "All Educational Institutions
## within 10km of Oxford, UK; ordered by
## date of establishment":
SELECT DISTINCT ?thing AS ?uri
?thingLabel AS ?name
?date AS ?established
?matchgeo AS ?location
WHERE
{
<http://dbpedia.org/resource/Oxford>
geo:geometry ?sourcegeo .
?resource geo:geometry ?matchgeo .
FILTER( bif:st_intersects( ?matchgeo, ?sourcegeo, 5 ) ) .
?thing ?somelink ?resource ;
<http://dbpedia.org/ontology/established> ?date ;
rdfs:label ?thingLabel .
FILTER( lang(?thingLabel) = "en" )
}
ORDER BY ASC( ?date )
## "Find Historical cross section of events related
## to Edinburgh and the surrounding area (within 30km)
## during the 19th century":
SELECT DISTINCT ?thing ?thingLabel
?dateMeaningLabel ?date ?matchgeo
WHERE
{
{
SELECT DISTINCT ?thing ?matchgeo
WHERE
{
<http://dbpedia.org/resource/Edinburgh>
geo:geometry ?sourcegeo .
?resource geo:geometry ?matchgeo .
FILTER( bif:st_intersects (
?matchgeo, ?sourcegeo, 30 ) ) .
?thing ?somelink ?resource
}
}
{
?property rdf:type owl:DatatypeProperty ;
rdfs:range xsd:date
} .
?thing ?dateMeaning ?date .
FILTER( ?dateMeaning IN ( ?property ) ) .
FILTER( ?date >= xsd:gYear("1800")
&& ?date <= xsd:gYear("1900") )
?dateMeaning rdfs:label ?dateMeaningLabel .
FILTER( lang(?dateMeaningLabel) = "en" ) .
?thing rdfs:label ?thingLabel .
FILTER( lang(?thingLabel) = "en" )
}
ORDER BY ASC ( ?date )
|
Previous
Inference Rules & Reasoning |
Chapter Contents |
Next
Performance Tuning |
