=Paper=
{{Paper
|id=Vol-1417/paper14
|storemode=property
|title=PSOA RuleML Integration of Relational and Object-Centered Geospatial Data
|pdfUrl=https://ceur-ws.org/Vol-1417/paper14.pdf
|volume=Vol-1417
|dblpUrl=https://dblp.org/rec/conf/ruleml/Zou15
}}
==PSOA RuleML Integration of Relational and Object-Centered Geospatial Data==
https://ceur-ws.org/Vol-1417/paper14.pdf
PSOA RuleML Integration of Relational and
Object-Centered Geospatial Data
Gen Zou
Faculty of Computer Science,
University of New Brunswick, Fredericton, Canada
gen.zou@unb.ca
Abstract. In recent years, many geospatial data sets have become avail-
able on the Web. These data can be incorporated into real-world appli-
cations to answer advanced geospatial queries. In this paper, we present
a use case to integrate a local data set with external geospatial data
sets on the Web. The data sets are modeled in different paradigms –
relational and object-centered. The integration uses Positional-Slotted
Object-Applicative (PSOA) RuleML, which combines the relational and
object-centered modeling paradigms for databases as well as knowledge
bases (KBs).
1 Introduction
In recent years, many geospatial data sets, e.g. LinkedGeoData1 [1] and GeoN-
ames2 , become available on the Web. Since a large number of real-world appli-
cations are built on top of data sets that contain geospatial information, en-
riching these data with external geospatial knowledge on the Web become an
interesting topic. The integration becomes more complex when the data sets
are modeled in different paradigms, e.g. some use the relational paradigm built
on top of predicate applications while others use the object-centered paradigm
built on top of RDF triples. In this paper, we present a use case that integrates
two relational data sets and one object-centered data set to answer interesting
geospatial queries. The integration is done using the PSOA RuleML language,
which integrates the relational and the object-centered paradigms for knowledge
representation.
The paper is organized as follows: Section 2 introduces the basics of PSOA
RuleML. Section 3 explains the data sets used for integration and give sample
facts. Section 4 explains integration rules. Section 5 gives sample geospatial
queries that can be answered after integration. Section 6 concludes the paper.
1
http://linkedgeodata.org
2
http://www.geonames.org
2 Gen Zou
2 PSOA RuleML
PSOA RuleML [2] is an object-relational Web rule language that integrates
relations and graphs via positional-slotted object-applicative (psoa)3 terms, which
have the general form
o # f([t1,1 ... t1,n1 ] ... [tm,1 ... tm,nm ] p1 ->v1 ... pk ->vk )
Here, an object is identified by an Object IDentifier (OID) o and described by
(1) a class membership o # f, (2) a set of tupled arguments [ti,1 ... ti,ni ],
i = 1, . . . , m, each being a sequence of terms, and (3) a set of slotted arguments
pj ->vj , j = 1, . . . , k representing attribute-value pairs. Each slot can have one
or more fillers (values). The OID as well as tuples and, orthogonally, slots in a
psoa term are all optional. A psoa term can express untyped objects by treating
them as being typed by the root class f=Top.
A more detailed introduction, with many examples leading to the semantics,
can be found in [3].
3 Data Sets
The data sets employed in the use case consist of three parts, all of which are
converted into PSOA RuleML presentation syntax.
1. A relational data set that contains house rental information, where the ar-
guments denote the unique reference number of the house, the street name
and number, city name, province/state name, country name, number of bed-
rooms, price per month, and whether it is furnished. Some facts are shown
in the following.
ex:HouseRentalInfo(1 "35 Routliffe Lane" "Toronto" "ON" "CA"
3 2500 "False"^^xs:boolean)
ex:HouseRentalInfo(2 "42 Frey Crescent" "Toronto" "ON" "CA"
2 900 "True"^^xs:boolean)
ex:HouseRentalInfo(3 "200 Hilda Avenue" "Toronto" "ON" "CA"
4 2000 "True"^^xs:boolean)
ex:HouseRentalInfo(4 "56 Wellington Street West" "Toronto" "ON" "CA"
4 1695 "False"^^xs:boolean)
ex:HouseRentalInfo(5 "1130 McAllister Avenue" "Ottawa" "ON" "CA"
2 1300 "False"^^xs:boolean)
2. A relational data set containing coordinates of addresses in WGS 84 geodetic
longitude-latitude spatial reference system used by the Global Positioning
System. Such data can be obtained using online geocoding services. The
relational arguments represent the latitude, the longitude, the street address,
the city name, the province name, and the country name. Some facts are
shown in the following.
3
We use the upper-cased “PSOA” as a qualifier for the language and the lower-cased
“psoa” for its terms.
PSOA RuleML Integration of Geospatial Data 3
gc:Geocode(43.778267 -79.426723 "35 Routliffe Lane" "Toronto" "ON" "CA")
gc:Geocode(43.74242 -79.291529 "42 Frey Crescent" "Toronto" "ON" "CA")
gc:Geocode(43.7955 -79.429234 "200 Hilda Avenue" "Toronto" "ON" "CA")
gc:Geocode(43.645289 -79.389063 "56 Wellington Street West" "Toronto" "ON" "CA")
gc:Geocode(39.78373 -100.445882 "1130 McAllister Avenue" "Ottawa" "ON" "CA")
3. An object-centered data set, extracted from Geonames, that contains in-
formation about geospatial entities. Each object is of the class gn:Feature
(geospatial feature) and described by slots gn:name, geo:lat, geo:long,
and gn:featureCode for its name, WGS84 latitude, WGS84 longitude, and
feature code. The feature code describes the type of geospatial feature, e.g.
a store, a hotel, a city, etc. Following is a example fact.
#gn:Feature(gn:name->"Canadian Tire"
gn:featureCode->gn:S.RET
geo:lat->43.7
geo:long->-79.1)
4 Integration Rules
In order to extract geospatial knowledge from multiple data sets and inte-
grate them, we first create a vocabulary to describe the geospatial entities in
our KB. The constant names are modeled using IRIs starting with the pre-
fix gr:, which is a shortcut for http://psoa.ruleml.org/GeospatialRules#.
The class gr:GeoEntity denotes all geospatial entities that can be located. Ev-
ery gr:GeoEntity-typed object has a slot gr:coord for the precise coordinates
of its centroid, where the slot value is expressed as a gr:Point application to the
latitude and longitude floating-point values. It also has an gr:addr slot for its
address of the class gr:Address. Subclasses of gr:GeoEntity in the KB include
gr:SubwayStation, gr:Store and gr:House. gr:HouseForRent is a subclass of
gr:House. The set of subclasses can be expanded based on application require-
ments.
gr:SubwayStation##gr:GeoEntity
gr:Restaurant##gr:GeoEntity
gr:Store##gr:GeoEntity
gr:House##gr:GeoEntity
gr:HouseForRent##gr:House
The class gr:Address, which is used to type addresses objects, is described
by slots gr:street, gr:city, gr:prov, and gr:country.
The following rule extracts address information from the house rental rela-
tion. The OID of the gr:HouseForRent object is an application of the function
gr:HouseRentID to the reference number ?RefNo, which uniquely determines
the house. While the OID of the gr:Address object is an existentially quanti-
fied variable ?Addr denoting a system-generated OID.
4 Gen Zou
Forall ?Key ?Name ?Phone ?Street ?City ?Prov ?Country ?PostCode ?Addr
(
Exists ?Addr
(
And(gr:HouseRentID(?RefNo)#gr:HouseForRent(?Bedrooms ?Price ?Furnished
gr:addr->?Addr)
?Addr#gr:Address(gr:street->?Street
gr:city->?City
gr:prov->?Prov
gr:country->?Country))
)
:- ex:HouseRentalInfo(?RefNo ?Street ?City ?Prov ?Country
?Bedrooms ?Price ?Furnished)
)
The coordinate of a geospatial entity with an address can be retrieved from
the gc:Geocode relation, using the following rule.
Forall ?O ?Ad ?Lat ?Long ?Street ?City ?Prov ?Country
(
?O#gr:GeoEntity(gr:coord->gr:Point(?Lat ?Long))
:- And(?O#gr:GeoEntity(gr:addr->?Ad)
?Ad#gr:Address(gr:street->?Street
gr:city->?City
gr:prov->?Prov
gr:country->?Country)
gc:Geocode(?Lat ?Long ?Street ?City ?Prov ?Country))
)
The following rule derives that an object ?O is gr:in an ?Area if ?O has
an address ?Ad, ?Ad has coordinates ?Pt, and ?Pt is a proper part of ?Area.
The conclusion adds to ?O a slot name gr:in whose filler, ?Area, is the re-
sult of the condition query evaluation. The condition performs a composition
of the slot named gr:coord, with filler ?Pt, followed by the binary relation
gr:RCCProperPartOf, leading to ?Area.
Forall ?O ?Ad ?Pt ?Area
(
?O#gr:GeoEntity(gr:in->?Area)
:- And(
?O#gr:GeoEntity(gr:coord->?Pt)
gr:RCCProperPartOf(?Pt ?Area)
)
)
The following rule derives a “proper part of” relation between a point and an
rectangular area expressed as an gr:Box application to the minimum latitude,
the minimum longitude, the maximum latitude, and the maximum longitude of
the area, by comparing the coordinates of the point with the coordinates of the
boundaries of the area.
PSOA RuleML Integration of Geospatial Data 5
Forall ?Lat ?Long ?LatMin ?LongMin ?LatMax ?LongMax (
gr:RCCProperPartOf(gr:Point(?Lat ?Long)
gr:Box(?LatMin ?LongMin ?LatMax ?LongMax))
:- And (
External(pred:numeric-greater-than-or-equal(?Lat ?LatMin))
External(pred:numeric-greater-than-or-equal(?Long ?LongMin))
External(pred:numeric-less-than-or-equal(?Lat ?LatMax))
External(pred:numeric-less-than-or-equal(?Long ?LongMax))
)
)
Next we discuss the integration of the graph data set for other geospatial
queries. The following rule maps the slot values of gn:name, geo:lat, and
geo:long of gn:Feature into slot values of gr:name and gr:coord for gr:GeoEntity.
Forall ?O ?Name ?Lat ?Long
(
?O#gr:GeoEntity(gr:name->?Name
gr:coord->gr:Point(?Lat ?Long))
:- ?O#gn:Feature(gn:name->?Name
geo:lat->?Lat
geo:long->?Long)
)
The classes of these objects can be refined using their feature codes, as shown
in the following rules.
Forall ?O
(
?O#gr:SubwayStation
:- ?O#gn:Feature(gn:featureCode->gn:S.MTRO)
)
Forall ?O
(
?O#gr:Restaurant
:- ?O#gn:Feature(gn:featureCode->gn:S.REST)
)
Forall ?O
(
?O#gr:Store
:- ?O#gn:Feature(gn:featureCode->gn:S.RET)
)
With both data sets expressed as GeoEntity objects, we can introduce the fol-
lowing rule, which derives the distance (measured in km) of ?O1 and ?O2 to be less
or equal than ?Distance, using the external function gr:distanceLessEqual,
which computes whether the distance of two GPS points (?Lat1, ?Long1) and
(?Lat2, ?Long2) is less or equal than ?Distance.
6 Gen Zou
Forall ?Lat1 ?Long1 ?Lat2 ?Long2 ?Distance ?Name ?G ?F
(
gr:inDistance(?O1 ?O2 ?Distance)
:-
And(
?O1#gr:GeoEntity(gr:coord->gr:Point(?Lat1 ?Long1))
?O2#gr:GeoEntity(gr:coord->gr:Point(?Lat2 ?Long2))
External(gr:distanceLessEqual(?Lat1 ?Long1 ?Lat2 ?Long2 ?Distance)))
)
5 Geospatial Queries
The first kind of query that can be asked is one type of geospatial entities in a
region, such as all houses available for rent in a region:
And(?H#gr:HouseForRent(gr:in->gr:Box(43 -80 44 -79) gr:addr->?Addr)
?Addr#gr:Address(gr:street->?Street))
The result binds ?H to gr:HouseRentID(1) ... gr:HouseRentID(4), repre-
senting houses 1 to 4 from the original KB.
Another kind of query is to look for all geospatial entities near a specific
entity.
For example, we can ask queries such as all stores within 5km of the house
with reference number 2:
And(?S#gr:Store(gr:name->?Name) gr:inDistance(gr:HouseRentID(2) ?S 5))
We can also look for all houses for rent within a certain distance of a place,
e.g. 2km within the subway station named "Spadina".
And(?S#gr:SubwayStation(gn:name->"Spadina")
?H#gr:HouseForRent gr:inDistance(?H ?S 2))
6 Conclusion
In this paper, we show a PSOA RuleML-based integration of a local relational
house rental data set, with external geocoding data set and Geonames. The
integration enables the use of reasoning engines to answer advanced geospatial
queries. The approach can be applied to similar local data sets that contain
address information. In the use case, we demonstrate the usefulness of object-
relational PSOA rules, e.g. for expressing transformation among the relational,
the object-centered, or the combined modeling paradigms. Future work includes:
(1) exploring the use of Region Connection Calculus rules in GeospatialRules
KB [4] to answer more geospatial queries; (2) expand the KB with more facts to
test the scalability of the PSOATransRun engine [5].
PSOA RuleML Integration of Geospatial Data 7
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