=Paper=
{{Paper
|id=Vol-1488/paper-05
|storemode=property
|title=Designing SDI4Apps POI Base
|pdfUrl=https://ceur-ws.org/Vol-1488/paper-05.pdf
|volume=Vol-1488
|dblpUrl=https://dblp.org/rec/conf/semweb/CerbaMB15
}}
==Designing SDI4Apps POI Base==
https://ceur-ws.org/Vol-1488/paper-05.pdf
Designing SDI4Apps POI Base
Otakar Čerba1, Tomáš Mildorf1, Raitis Berzins2
1 University of West Bohemia, Univerzitní 8, 306 14 Plzeň, Czech Republic
{cerba, mildorf}@kma.zcu.cz
2 Baltic Open Solutions Center, Krišjāņa Barona iela 32-7,
Rīga, LV-1011, Latvija
raitisbe@gmail.com
Abstract. The SDI4Apps project has collected a large number of points of
interest (POIs). This data set represents a seamless and open resource of POIs in
Europe. Its principal target is to provide information for cycling as Linked data
together with other data set containing road network. The POIs, which will be
available for other users for download, search and reuse, will be helpful for
other applications in tourism as well. The article presents the data model for
POIs and harmonization of external data sources into this data model. The
current version of the SDI4Apps POI data set includes a harmonized
combination of selected OpenStreetMap data, experimental ontologies and local
data. A short comparison of the SDI4Apps POIs with the OpenPOIs data set is
presented.
Keywords: Point of interest, Linked Data, data model, SDI4Apps, data set,
spatial data modeling.1 Introduction
1 Introduction
SDI4Apps1 is an EU-funded project (European Union’s ICT Policy Support
Programme as part of the Competitiveness and Innovation Framework Programme)
coordinated by the University of West Bohemia 2 in Plzen, Czech Republic. SDI4Apps
seeks to build a cloud-based framework with open APIs (application programming
interfaces) for data integration focusing on the development of six pilot applications.
The project draws along the lines of INSPIRE (INfrastructure for SPatial InfoRmation
in Europe), Copernicus and GEOSS (Global Earth Observation System of Systems).
The SDI4Apps development process started with data integration and harmonization,
including semantic annotation and Linked Data interconnection. Data are collected
1 http://sdi4apps.eu/
2 http://www.zcu.cz/
based on requirements of the six pilot activities, which represent the first users, testers
and feedback providers of the whole SDI4Apps solution.
This article describes the SDI4Apps Point of Interest (SPOI) dataset as a specific
set of POIs which are useful for potential customers of applications developed in the
SDI4Apps project, above all in the Open Smart Tourist Data pilot. “POI provide an
essential data source for a wide range of location-based applications. Having emerged
from in-car navigation systems the classic POI are often linked to an address and
relate to businesses such as petrol stations, garages, shopping centers, or common
sense information, such as the church, police station or hospital in a city.” (Andrae et
al., 2011). Various POI datasets are implemented into popular applications for
traveling and tourism such as Trip Advisor or navigation tools such as Waze. There
are also two US Patents mentioning the role of POIs in on-line advertisement
(Jakobson & Rueben, 2013a, Jakobson & Rueben, 2013b).
There are a lot of resources providing POI data. First of all, it is necessary to
mention OpenPOIs by Open Geospatial Consortium. This database contains more
than 9 million POIs, which are available through an API. Other POI data are offered
by various web pages such as POIplaza3, POI download4, GPS Data Team 5, Pocket
GPS World6 or the POI service provided by Flemish government 7. Data are also
provided by several producers of navigation tools. These resources contain various
types of POIs and enable to download data in different formats (usually in a format
that can be processed by navigation tools).
The SPOI data set is created as a combination of global data (selected points from
OpenStreetMap) and local data provided by the SDI4Apps partners or data available
on the web. The final version will represent an open and seamless solution which will
be able to be “a data fuel” for location-based and navigation services and applications.
The added value of the SDI4Apps approach consists in implementation of linked data.
The current version contains several links to external resources (see the part “SPOI as
Linked Data”). Interconnections to other data will be added, including transformation
of all used code list into RDF (Resource Description Framework) vocabularies.
There are several disadvantages of contemporary POIs datasets (such as those
mentioned above), which prevent their integration and further re-use. These
disadvantages include:
using proprietary formats or specific formats for geographic information
systems,
download based on topics or geographical regions (for example countries),
3 http://poiplaza.com/
4 http://www.downloadpoi.com/
5 https://www.gps-data-team.com/
6 http://www.pocketgpsworld.com/
7 http://poi.api.geopunt.be/
common absence of standardized services or querying,
charge for data.
The main goal of this article is to introduce the SPOI base, above all the
development process and data model. The SPOI data are compared with the similar
solution OpenPOIs in order to check the compatibility between both POI datasets for
further reuse in various applications.
The article is structured as follows. Section 2 describes the used methodology
including fundamental pillars of the design and development of the SPOI base. This
includes theoretical backgrounds as well as inspiring data sets and models (as the
overview of state-of-the-art). The methodology also contains a short description of the
comparison of the SPOI base and OpenPOIs. Section 3 presents the SPOI data set, its
data model and relation to the Linked Data approach. Section 4 includes the
comparison of the SPOI and OpenPOIs datasets. This section shows the potential of
combining both data resources. The last section (except Conclusions) includes a
discussion on further steps of the SPOI development.
2 Methodology
The reason for the development of another dataset of POIs emerged from the needs of
users (tourists, tourist service providers as well as developers of applications focused
on tourism). There was a lack a complex set of POIs which would not be territory
specific (for example limited to particular countries, regions or national parks), would
be open and not limited to one data resource (usually OpenStreetMap). The
SDI4Apps team composed above all of experts from the Czech Republic and Latvia
developed a seamless open database of POIs which will be distributed as 5-star
Linked Open Data (Berners-Lee, 2009) to be accessible for all users.
Even though the data modeling of POIs (as features with simple point geometry,
identifier and several descriptive attributes) seems to be very trivial, authors did an
extensive research of existing data models and literature. The development of the
SPOI data model was based on seven fundamental pillars:
1. Classical studies and books focused on spatial (or geographical) data
modeling such as Goodchild (1992), Shekhar et al. (1997), Longley et al.
(2001) or Tomlinson (2007). These resources gave a basic framework of
SPOI data model.
2. Because RDF has to be the principal format to store data, also several
researches dealing with publication and modeling of spatial data as RDF
triples (for example Auer et al., 2009, Janowicz et al., 2012 or Kritikos et al.,
2013) were taken into consideration and implemented.
3. General principles of development Linked Data as they are published in
Bizer et al. (2008), Heath et al. (2008), Bizer et al. (2009) or Hausenblas
(2009). There is also a lot of publications focused on Linked Data on the
geographical domain such as Auer & Lehmann (2009), Atemezing & Troncy
(2012) or Kuhn et al. (2014).
4. The data model of POI as it is published in the W3C Editor's Draft Points of
Interest Core (Hill & Womer, 2012; this document was originally created
W3C Points of Interest Working Group that was transformed into OGC as
Points of Interest Standards Working Group) as well as in the presentation
Framing a Geo Strategy for the Web with Points-Of-Interest Data by R.
Singh (2012).
5. Data models of existing POI datasets (for example POIplaza or POI
download).
6. Existing standards, formats and vocabularies such as RDF, RDFS, SKOS,
OWL, FOAF, GeoSPARQL or WGS84 Geo Positioning.
7. Experiences of data modeling from existing solutions and projects such
LinkedGeoData, DBpedia, GeoNames.org or SmartOpenData.
The reasons for selection of particular classification systems, coordinate systems
and other parts of the data model are explained in the next section.
The POI data model is open and flexible. The essential core of the model (Id,
coordinates, label and categorization) was extended by several attributes which are
integral components of some original data and could be helpful for tourist purposes
(for example contact information, opening hours or accessibility for handicapped
visitors).
The contemporary version of the SPOI base is populated by XSLT templates
(several examples of using XSLT transformations in spatial data domain are published
in Čerba, 2010 or Čerba & Čepický, 2012). The XSLT template contains procedure of
harmonization, data models’ mapping, including transformation rules of classification
systems.
In order to prove the concept of the SPOI base, a comparison with the OpenPOIs
data set as the main sample of global POI database was realized. The first part of the
comparison contains basic properties of both data sets such as coverage, number of
POIs or output formats. The second part tests five small areas in Europe and its
coverage by POIs in both databases. The size of the each area is 0,02° x 0,02° to
satisfy the limitation of OpenPOIs. The OpenPOIs API is able to provide in maximum
100 POIs. Ten various types of landscape were chosen as areas of interest (for
example city important for tourism, mountains, coast, industrial area or countryside).
Also various European countries (for example Czech Republic, France, Italy, Latvia,
Poland) were selected to limit local differences. Numbers of POIs were gained by
XSLT template for filtering data (SPOI) and custom API (OpenPOIs).
3 SDI4apps POI Base
3.1 Basic description
Table 1. SPOI – basic information.
Property Description
Data amount 3 292 230 POIs
File size 3,1 GB
Coverage Europe (45 countries)
Data sources OpenStreetMap
Local data from the Posumavi region (Czech Republic)
Experimental ontologies developed at the University of West
Bohemia (Czech Republic) – European ski resorts and religious
monuments in Rome
POI classification SPOI contains nine fundamental classes adopted from the data
model used for data of the Waze navigation tool.
Storage XML file
Virtuoso
Publication Virtuoso SPARQL endpoint8
Map application Smart Tourist Data (Geoportal SDI4apps)
Links Several POIs are linked to DBpedia and GeoNames.org.
There are also DBpedia and GeoNames.org links to particular
countries containing POIs.
The main classification of POIs is accessible through URI.
Table 1 presents basic information about the set of the POIs developed in the Open
Smart Tourist Data pilot application of the SDI4Apps project.
8 http://ha.isaf2014.info:8890/sparql
3.2 SPOI as Linked Data
SPOI corresponds with 5-star rating system of Linked Open Data published by
Berners-Lee (2009) and described in Janowicz et al. (2014). Table 2 shows how
particular criteria are satisfied by the SPOI data.
Table 2. SPOI & 5-star rating system of Linked Open Data.
Stars Description SPOI
1 Data is available on the Web Data are provided to download on the Web
under an open license. through the SPARQL endpoint.
The data will be provided under the Open
Database License (ODbL).
2 Data are available as a SPARQL endpoint is able to provide data in many
structured data. structured formats, including JSON, XML, CSV
or various serialization of RDF.
3 Data uses a non-proprietary Majority of output format offering in Virtuoso
format. SPARQL endpoint are classed as non-proprietary
formats.
4 Particular objects have URI as Data uses unique identifier based on URI based
identifier. on http://www.sdi4apps.eu/poi.
5 Data is linked to another data. Several object are linked by properties
skos:exactMatch and owl:sameAs to equivalent
elements in DBpedia and GeoNames.org.
All objects are interconnected via topological
property sfWithin to relevant countries as they are
expressed in DBpedia and GeoNames.org.
The main classification of POIs is accessible
through URI.
3.3 Data model
The contemporary version of the SPOI data model (June 2015, Fig. 1) has seven basic
components:
1. Identification – each POI is identified by unique ID expressed as URI.
Original ID (URI of the product and a unique code generated by XSLT
script) was replaced by more readable form providing some information such
as country and category. The new identifier is composed of URI
(http://www.sdi4apps.eu/poi), ISO 3166-1 alpha-2 country code, category of
POI according Waze navigation data and code (generated randomly by the
XSLT script).
2. Description – each POI is described by a label (name). In several cases, there
are more labels differentiated by the xml:lang attribute. POIs can contain a
text description if it is available.
3. Geometry / Localization – each POI is localized by two coordinates (latitude
and longitude) of World Geodetic System (WGS) 84. WGS84 represents the
most used, respected and universal system, which is usually transformable to
local systems and cartographic projections. Coordinates are published
according to Basic Geo (WGS84 lat/long) Vocabulary (Brickley, 2006).
4. Classification – categorization is realized through three various parameters –
classification based on GPS-based geographical navigation Waze, which is
primary, mandatory and used for visualization on the SDI4apps geoportal.
The classification system used in Waze is quite short, clear and simple to
visualize as well as differentiate, because it contain 10 well-defined
categories. Since majority of data originate in OpenStreetMap, two types of
classification from Open StreetMap are used. The authors tested other
nomenclatures used in various products (data, services, applications) such as
Trip Advisor, Yelp!, USGS Geographic Names Information System or
Ordnance Survey POI classification scheme, but the Waze scheme is the
most appropriate to purposes of POI database developed in the SDI4Apps
project. Mapping rules between the Waze nomenclature, the OpenStreetMap
classification and categories used in other source data are kept in the
transformation XSLT file.
5. Contact information – several POIs contain contact information such as
address, e-mail, homepage, fax or phone number.
6. Common information – currently there are only two types of this type of
information (opening hours and access). This information is available only
for data from the Posumavi region.
7. Links – all POIs include one or more of three types of links to external data –
links to external non-linked data resources such as Wikipedia, Wolfram|
Alpha or raster maps, links to an equivalent object in DBpedia or
GeoNames.org, links to countries (in DBpedia and GeoNames.org)
containing the POI. The last type of links is mandatory for each object.
POI
+ id : anyURI
+ rdfs:label : xsd:string [1..*]
+ rdfs:comment : xsd:string [0..*]
+ geo:lat : xsd:float
+ geo:long : xsd:float «data type»
poi:WAZEClassification
+ poi:category : xsd:string [0..1]
+ Car Services : xsd:string
+ poi:categoryOSM : xsd:string [0..1]
+ Culture and entertainment : xsd:string
+ poi:categoryWAZE : poi:WAZEClassification
+ Food and drink : xsd:string
+ Lodging : xsd:string
+ poi:address : xsd:string [0..1] + Natural features : xsd:string
+ foaf:mbox : xsd:string [0..*] + Outdoors : xsd:string
+ poi:fax : xsd:string [0..*] + Professional and public : xsd:string
+ foaf:phone : xsd:string [0..*] + Shopping and services : xsd:string
+ foaf:homepage : anyURI [0..*] + Transportation : xsd:string
+ poi:openingHours : xsd:string [0..1]
+ poi:access : xsd:string [0..1]
+ rdfs:seeAlso : anyURI [0..*]
+ skos:exactMatch : anyURI [0..*]
+ owl:sameAs : anyURI [0..*]
+ geos:sfWithin : anyURI [1..*]
Fig. 1. SPOI – data model.
4 Comparison of SPOI and OpenPOIs
To evaluate the concept of the SPOI base, a short comparison with a similar data set
(OpenPOIs) was performed. This part is divided into two parts – comparison of
common characteristics and monitoring of POIs in selected areas.
4.1 Common characteristics
Table 3 shows basic properties of both data sets. Information on OpenPOIs was
extracted from the OpenPOIs’ homepage9 and presentation Framing a Geo Strategy
for the Web with Points-Of-Interest Data (Singh, 2012).
9 http://openpois.net/
Table 3. SPOI & OpenPOIs – basic characteristics.
Property SPOI OpenPOIs
Number of POIs > 3.2 millions > 9.5 millions
Coverage Europe World
Main sources of data OpenStreetMap GeoNames, DBpedia (these
resources are mentioned in
Singh, 2012, a short survey of
data demonstrated that many
objects originated from
OpenStreetMap)
Ways of data providing SPARQL endpoint Custom API, WFS
Output data formats Formats provided by Virtuoso XML, JSON, microdata, RDF
tool (RDF, JSON, CSV,
Javascript…)
Table 3 shows two main relations between both POI data sets. Both are based on
similar original data (OpenStreetMap). This fact will be evident from Table 4
comparing numbers of POIs in selected areas. The second similarity is connected with
standardized solutions to provide data. OpenPOIs prefers standards of OGC, because
this organization maintains OpenPOIs as well as Web Feature Service. SPOI uses
SPARQL endpoint to be compliant with Linked Data and RDF solutions. Output
formats of both sets are similar.
Also data models are comparable. Both data models contains basic components
such as identifiers, location (points; but both products deal with addresses and
OpenPOIs also with relationships to other POIs), labels, description, categorization
and links. OpenPOIs offers metadata items. SPOI contains more contact information
and data important for issues of tourism.
The most noticeable difference is evident from data. OpenPOIs data are just copied
from original resources, while SPOI data are harmonized to the uniform data model.
Therefore all SPOI features use the same classification in comparison with SPOI. This
fact can complicate potential combination of both data sets, but it can be treated with
similar transformation rules as they are applied to import external data. Moreover,
mapping between the OpenStreetMap classification of POI and the Waze
nomenclature, which is used as primary classifier in SPOI, is defined in the
contemporary version of XSLT styles.
4.2 POIs in selected areas
The comparison of quantity of POIs in both datasets (Table 4) was realized in ten
European localities. These areas (0,02° x 0,02°) were selected to cover various types
of landscape (for example rural area, industrial area, large city or mountains) as well
as different countries evenly distributed over the whole continent.
Table 4. Number of POIs in selected areas.
Area SPOI OpenPOIs
Seaside resort (Croatia) 7 4
Submontane area (Czech republic) 1 0
Mountains (France) 1 1
Rural area (Germany) 28 28
Historical site (Greece) 9 10
Large city (Italy) 57 60
Coast (Latvia) 0 0
Small towns and villages (Netherlands) 6 8
Sport center (Norway) 46 41
Industrial area (Poland) 54 57
Even though the total number of POIs in selected sample areas (Table 4) is equal
(209), there are evident several interesting knowledge (which can utilized to improve
and extend SPOI data, because complete integration of OpenStreetMap data is not
finished yet):
1. OpenPOIs dataset contains more POIs in urbanized regions.
2. Results in sparsely populated area is very similar.
3. In localities important for tourism SPOI data evinces better results.
4. SPOI and OpenPOIs show similar results in post-communist countries.
5. There are minor differences between North and South Europe.
The Table 1 shows that both POI data sets are quite similar. It is evident not only
from the number of POI, but also from the similar content. For example the sample
from France containing just one same POI in both resources. This fact just support a
challenge of joining of SPOI and OpenPOIs to get large POI database. In this case it is
necessary to solve redundant feature in both datasets.
This test is just initial. It will be repeated in other areas to find out potential random
errors and prove hypothesis mentioned in previous list. Also a graphical visualization
(a heat map) and comparison of content (not only number of POIs) will be realized to
compare both data sets.
5 Discussion & future steps
The contemporary version of the SPOI base is useful, but the base as well as its model
haven’t been completed yet. The developers together with the SDI4Apps project and
other users are discussing many proposed changes and improvements. They could be
divided into two groups – (1) modifications of the data model and (2) other further
steps related to populating, visualization or maintenance.
The possible changes of the SPOI data model include implementations of:
a secondary identifier based on name(s) of features to make URIs more
readable.
persistent identifiers being stable during data updating.
the form of coding coordinates as it is defined in the GeoSPARQL standard
(Perry & Herring, 2011) to support exploitation of the GeoSPARQL
querying.
a property for preferred label (for example skos:prefLabel), because the SPOI
base contains more than one labels (in one language) for several features.
transformations of classifications to RDF structure to be re-usable in other
data and applications.
changes of string values (for example addresses or opening hours) to several
semantically rich values, for example based on INSPIRE specifications (for
example INSPIRE Data Specification on Addresses) or ISA Core Location
Vocabulary for addresses.
new attributes important for tourism.
Other further steps are related to population of the SPOI base (searching of new
data resources and its processing, removing errors and shortcomings in data, massive
and automated adding links to other resources), refining data (eliminations of
duplicities), providing data (export to other formats that are supporting by navigations
tools, improvements of map portal, generalization), updating (questions of persistent
URIs or processing of changes in source data) and improvements of a presentation of
the product (social media). A detail description of these steps is not the subject of this
article and the final list will change according to user requirements.
6 Conclusions
There are many ways how to describe the SDI4Apps POI data set (for example quality
of data or maintenance and updating). With respect to the limits of the conference
proceedings it is not possible to mention all question or problems connected to SPOI.
This paper introduces the data set of Points of interest developed in the SDI4Apps
project. This data set is the seamless and open resource of POIs that will be available
for other users to download, search or use in applications and services. The data
model of SPOI comes from review of literature, existing data (for example
OpenPOIs), recommendations of W3C and OGC and user requirements. The current
version of the data set has been created as a harmonized combination of selected
OpenStreetMap data, experimental ontologies developed in the Section of Geomatics
of the University of West Bohemia and local data provided by the Uhlava region
(Czech Republic). The transformation was realized by XSLT templates. Data are
stored in the Virtuoso tool as RDF triples. SPOI is published via SPARQL endpoint
which enables comfortable, efficient and standardized querying of data.
The document also contains the short comparison of SPOI with other respected
data set of POIs (OpenPOIs). The results of the comparison show common data
resources (above all OpenStreetMap), similar approach to data modeling and
contrarily various approaches to data harmonization, storing and provision. The
acquired information could be useful in case of development of mix of both data set or
in case of mutual exchange of data.
The added value of the SDI4Apps approach in comparison to other similar
solutions consists in implementation of linked data, using of standardized and
respected datatype properties and development of the completely harmonized data set
with uniform data model and common classification (not only a copy of original
resources).
The authors believe that the selected approach to develop an open data base of
POIs is promising, because
implementation of many external data resource can provide a multi-level
view on POIs, including corrections of shortcoming and gaps,
Linked data enables more efficient way how to combine and re-use data,
open data can generate an interesting business effect such local advertising or
development applications.
The authors welcome other remarks and comments how to improve the SPOI data
set, its model, content as well as interconnection to other data.
References
1. Andrae, S., Erlacher, C., Paulus, G., Gruber, G., Gschliesser, H., Moser, P., Sabitzer,
K. & Kiechle, G. (2011). OpenPOI–Developing a web-based portal with high school
students to collaboratively collect and share points-of-interest data. Learning with
geoinformation, 66-69.
2. Atemezing, G. A., & Troncy, R. (2012). Comparing vocabularies for representing
geographical features and their geometry. In Terra Cognita 2012 Workshop (p. 3).
3. Auer, S., & Lehmann, J. (2009). LinkedGeoData–Collaboratively Created Geo-
Information for the Semantic Web. Semantic Web Challenge, ISWC.
4. Auer, S., Lehmann, J., & Hellmann, S. (2009). Linkedgeodata: Adding a spatial
dimension to the web of data (pp. 731-746). Springer Berlin Heidelberg.
5. Berners-Lee, T. (2009). Linked Data. Design Issues. W3C.
6. Bizer, C., Heath, T., & Berners-Lee, T. (2008). Linked data: Principles and state of
the art. In World Wide Web Conference.
7. Bizer, C., Heath, T., & Berners-Lee, T. (2009). Linked data-the story so far. Semantic
Services, Interoperability and Web Applications: Emerging Concepts, 205-227.
8. Brickley, D. (2006). Basic Geo (WGS84 lat/long) Vocabulary, World Wide Web
Consortium.
9. Čerba, O. (2010). XSLT Templates for Thematic Maps (pp. 181-192). Springer Berlin
Heidelberg.
10. Čerba, O., & Čepický, J. (2012). Web Services for Thematic Maps. In Online Maps
with APIs and WebServices (pp. 141-155). Springer Berlin Heidelberg.
11. Goodchild, M. F. (1992). Geographical data modeling. Computers &
Geosciences, 18(4), 401-408.
12. Hausenblas, M. (2009). Exploiting linked data to build web applications. IEEE
Internet Computing, (4), 68-73.
13. Heath, T., Hausenblas, M., Bizer, C., Cyganiak, R., & Hartig, O. (2008). How to
publish linked data on the web. In Tutorial in the 7th International Semantic Web
Conference, Karlsruhe, Germany.
14. Hill, A. & Womer, M. (2012). Point of Interest Core. W3C Editor's Draft.
15. Jakobson, G., & Rueben, S. (2013a). U.S. Patent Application 13/986,744.
16. Jakobson, G., & Rueben, S. (2013b). U.S. Patent Application 13/987,075.
17. Janowicz, K., Hitzler, P., Adams, B., Kolas, D., & Vardeman, C. (2014). Five stars of
Linked Data vocabulary use. Semantic Web, 5(3), 173-176.
18. Janowicz, K., Scheider, S., Pehle, T., & Hart, G. (2012). Geospatial semantics and
linked spatiotemporal data-Past, present, and future. Semantic Web, 3(4), 321-332.
19. Kritikos, K., Rousakis, Y., & Kotzinos, D. (2013). Linked open GeoData management
in the cloud. In Proceedings of the 2nd International Workshop on Open Data (p. 3).
ACM.
20. Kuhn, W., Kauppinen, T., & Janowicz, K. (2014). Linked data-A paradigm shift for
geographic information science. In Geographic Information Science (pp. 173-186).
Springer International Publishing.
21. Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2001). Geographic
information system and Science. England: John Wiley & Sons, Ltd.
22. Perry, M., & Herring, J. (2011). OGC GeoSPARQL-A geographic query language for
RDF data. OGC Implementation Standard, ref: OGC.
23. Singh, R. (2012). Framing a Geo Strategy for the Web with Points-Of-Interest Data.
Invited presentation in the 5th International Terra Cognita Workshop 2012 In
Conjunction with the 11th International Semantic Web Conference, Boston, USA.
24. Shekhar, S., Coyle, M., Goyal, B., Liu, D. R., & Sarkar, S. (1997). Data models in
geographic information systems. Communications of the ACM, 40(4), 103-111.
25. Tomlinson, R. F. (2007). Thinking about GIS: geographic information system
planning for managers. ESRI, Inc..