=Paper=
{{Paper
|id=Vol-2204/paper4
|storemode=property
|title=Towards Knowledge-Based Integration and Visualization of Geospatial Data using Semantic Web Technologies
|pdfUrl=https://ceur-ws.org/Vol-2204/paper4.pdf
|volume=Vol-2204
|authors=Weiming Huang
|dblpUrl=https://dblp.org/rec/conf/ruleml/Huang18
}}
==Towards Knowledge-Based Integration and Visualization of Geospatial Data using Semantic Web Technologies==
https://ceur-ws.org/Vol-2204/paper4.pdf
Towards knowledge-based integration and visualization
of geospatial data using Semantic Web technologies *
Weiming Huang
GIS Centre, Lund University, Lund, Sweden
weiming.huang@nateko.lu.se
Abstract. Geospatial data have been pervasive and indispensable for various
real-world application of e.g. urban planning, traffic analysis and emergency re-
sponse. To this end, the data integration and knowledge transfer are two promi-
nent issues for augmenting the use of geospatial data and knowledge. In order to
address these issue, Semantic Web technologies have been considerably adopted
in geospatial domain, and there are currently still some activates investigating the
benefits brought up from the adoption of Semantic Web technologies. In this
context, this paper showcases and discusses the knowledge-based geospatial data
integration and visualization leveraging ontologies and rules. Specifically, we use
the Linked Data paradigm for modelling geospatial data, and then create
knowledge base of the visualization of such data in terms of scaling, data por-
trayal and geometry source. This approach would benefit the transfer, interpret
and reuse the visualization knowledge for geospatial data. At the meantime, we
also identified some challenges of modelling geospatial knowledge and outreach-
ing such knowledge to other domains as future study.
Keywords: geospatial data, data integration, data visualization, Semantic Web,
ontologies, rule-based inference.
1 Introduction
Geospatial information has received increasing attention from the mainstream IT world
and become indispensable for various real-world applications of e.g. urban planning,
traffic analysis and emergency response. In the geospatial community, the transfer,
sharing and visualization of geospatial data mainly rely on a number of syntactic stand-
ards which shape the current solutions for spatial data infrastructure (SDI). Such stand-
ards are mainly from Open Geospatial Consortium (OGC), and most of them only guar-
antee interoperability on a syntactic level, whereas the semantics and knowledge are
represented insufficiently. Therefore, we need a way for addressing the semantic chal-
lenges concerning geospatial data and knowledge [1]. Besides, the SDI - whose aim is
* The PhD project is supervised by Prof. Lars Harrie and Dr. Ali Mansourian at GIS Centre,
Lund University, and it is funded by China Scholarship Council and Lund University.
2
mainly for dissolving environmental and geospatial data held in silos – are still per-
ceived as islands in mainstream IT [2], and this impedes the augmentation of the use of
geospatial data to other domains. In this context, Semantic Web technologies, including
the part concerning Linked Data, unveil a promising way for resolving these issues by
embracing knowledge-based approaches which could foster better transfer, interpreta-
tion, expansion and reuse of geospatial data, information and knowledge.
Visualization is one of the most important and pervasive application areas of geo-
spatial data and in geographic information systems (GIS); it allows users to explore,
synthesize, present and analyze the underlying geospatial data in an interactive manner.
However, the visualization of geospatial data unveils some long-standing challenges to
both the providers and users. One such challenge is the data integration issue, which
can be the integration between geospatial data and also between geospatial data and
data from other domains. At the meantime, the visualization of geospatial data is also
knowledge-intensive from a cartographic perspective for both the providers and users.
For the providers, a wide range of cartographic theories is required to derive sensemak-
ing and cartographically satisfactory applications; and for the users, the knowledge is
required to interpret the presented data in a meaningful way. And sometimes the users
need to reach a high level of cognitive consensus with the providers in order to better
perceive the delivered information from the visualization applications.
Therefore, this PhD thesis mainly investigates how the Semantic Web technologies
can foster better integration and visualization of geospatial data, and thereof aid the
outreaching of geospatial data, information and knowledge to other domains. The scope
of the PhD thesis is broad, and benefits brought up by Semantic Web technologies will
be demonstrated in a few particular cases where some traditional geospatial problems
are better solved with the Semantic Web. And in this framework, ontologies and rules
are intensively used as two main paradigms for knowledge representation.
2 State-of-the-Art
The application of Semantic Web technologies has developed considerably in geospa-
tial domain in the last decade as they address several long-standing challenges of e.g.
data integration, semantic interoperability and knowledge formalization and provide a
promising way to connect spatial data infrastructures (SDIs) with the mainstream to
augment the application of geospatial data [2]. As a result, a vast number of geospatial
datasets have been released as Linked Data, and some of them are serving as central
hubs in the Linked Open Data (LOD) cloud 1. At the meantime, a number of vocabular-
ies for representing geospatial data (see [3] for a comparison), the geospatial Linked
Data query language GeoSPARQL [4] and several geospatial enabled RDF stores (e.g.
Stardog 2 and Virtuoso 3) have been developed. These theoretical and technical advance-
ments have facilitated the publishing of geospatial Linked Data and the use of Semantic
Web for geospatial knowledge representation and sharing.
1 https://lod-cloud.net/
2 https://www.stardog.com/
3 https://virtuoso.openlinksw.com/
3
2.1 Geospatial Linked Data
There is an ongoing trend of publishing geospatial data as Linked Data; initially Se-
mantic Web researchers showcased the potential of Linked Data by transforming pop-
ular, third-party datasets to RDF, and then more Linked Data initiatives have been run
by governmental agencies and large-scale data infrastructures [5]. For instance, Ord-
nance Survey (OS), the national mapping agency (NMA) in the UK, has released sev-
eral geospatial datasets maintained by them as Linked Data [6]. In Europe, the e-Gov-
ernment and open data communities are increasingly adopting the Linked Data ap-
proaches, and this has motivated the Joint Research Centre (JRC) of the European Com-
mission to investigate the potentials of publishing the INSPIRE-compliant geospatial
data as Linked Data through the ARE3NA activity 4. Varanka and Usery [7] argued that
the map data (geospatial data) that are released in RDF according to corresponding
ontologies can be treated as the knowledge base entailed by the map content. In this
respect, we hold the same opinion, and argue that more geospatial knowledge can be
represented upon the map knowledge base.
The increasing geospatial Linked Data have stimulated the studies of linking data
from other sources to such data and exploiting such Linked Data in graphical interfaces.
The visualization of Linked Data, in general, refers to the techniques of visually pre-
senting the links between entities to facilitate the intuitive discovery of underlying in-
formation and knowledge [8]. For geospatial data, the spatial context is crucial for eas-
ing this perception and discovery process. Therefore, the visualization of geospatial
Linked Data is generally in the form of map mashups, in which the data are spatially
represented as thematic data on top various base maps. To this end, several tools for
exploiting such data through visual and graphic interfaces have been developed. For
instances, LOD4WFS [9] enables geospatial Linked Data to be queried through web
feature service (WFS) protocol and visualized in GIS programs. Map4RDF [10] pro-
vides the possibility of editing the underlying data, and connecting to statistical data.
Nonetheless, these tools generally use predefined and hard-coded visualization settings
in the programs. However, in the context of Semantic Web, we can use a knowledge-
based approach for the visualization by formalizing the knowledge concerning how the
geospatial data (in Linked Data in this case) are visualized using ontologies and rules.
In this way, the knowledge can be more readily be shared, interpreted and reused.
2.2 Geospatial knowledge representation using Semantic Web
technologies
The capacity of knowledge representation of Semantic Web through leveraging on-
tologies and rules has been recognized in geospatial domain for many years and used
in a number of studies. These studies span several research areas of e.g. visualization,
geoprocessing and information retrieval. For instances, Janowicz et al. [1] proposed a
framework for modelling the knowledge and semantics using ontologies and SWRL
rules, and used the framework as a semantic enabled profile of current OGC-complaint
4 https://inspire.ec.europa.eu/news/linking-inspire-data-draft-guidelines-and-pilots
4
SDI. Hofer et al. [11] developed a knowledge base to support the composition of geo-
processing workflow, in which the ontologies were used to formalize the geooperators,
and SWRL rules are used for formulating the rules associated with the geooperators
chaining. Keßler et al. [12] employed ontologies and SWRL rules for context-aware
geographic information retrieval, where they used ontologies for organizing the seman-
tically annotated data and rules for deriving inference for context detecting. Gould and
Mackaness [13] formalized the knowledge for on-demand map generalization using
ontologies to facilitate the knowledge to be shared, expanded and reused in mapping
systems. Huang et al. [14] formalized the knowledge for both visualization scales for
geospatial features and the relations between thematic data and base maps using ontol-
ogies to enable geometrically self-adapting web maps.
With regard to the visualization of geospatial data, we argue that the knowledge in
this respect needs to be more formally modelled to facilitate the sharing and reuse of
such knowledge and also outreaching such knowledge to other domains. Data portrayal
is an indispensable part of visualization for geospatial data, and it subjects the semantic
challenge as the current standards for modelling such information lack semantics, and
this hampers the exchange and reuse of such information. This issue has also been iden-
tified by OGC, and thereof they initiated several testbeds to investigate a semantic por-
trayal solution. They developed ontologies for semantically modelling the information
of style, symbol, symbolizer and graphic [15].
3 Knowledge-based visualization coupling ontologies and
rules
The investigations performed by OGC Testbeds provide solid ground for the geospatial
community towards knowledge-based visualization for geospatial data and the vision
of shaping a web of knowledge for geovisualization. However, we argue that the mod-
elling of conditional portrayal rules is deficient.
Conditional portrayal is prevalent for visualization, i.e. the symbol/symbolizer
used for visualizing a feature depends on the visualization scale and attribute/geometric
data associated with the feature. In the ontologies developed by OGC testbeds, the
SPARQL ASK queries are recommended to model such conditions. However, such rule
modelling approach has several limitations: (1) although SPARQL can be utilized for
expressing rules in Semantic Web, the queries on their own are not commonly accepted
as rule modelling for knowledge presentation and inference 5; (2) the semantics could
potentially be misinterpreted because the SPARQL ASK constraints are generally used
to check whether certain conditions currently hold in a (scope of) knowledge graph and
therefore facilitate verification and inconsistency 6. To address this issue, we argue that,
in the environment of Semantic Web, we can leverage the rule-based inference and
knowledge representation capacities and thus augment the use of geospatial rules to
other areas of the mainstream IT world.
5 https://www.w3.org/2003/12/swa/dawg-charter
6 https://www.w3.org/Submission/spin-modeling/
5
The most commonly used semantic rules in geospatial domain are of the type of
SWRL, and this is mainly because the SWRL is supported by Protégé ontology editor
and several rule engines and ontology reasoners. However, SWRL has several limita-
tions for geospatial applications; for example, SWRL adopts the open world assump-
tion and thereof only supports monotonic semantics, and in some geospatial applica-
tions, we need to tackle the no data or voidable situations that entail the handling of
non-monotonic semantics. In contrast to SWRL, the object-oriented SPIN (SPARQL
Inferencing Notation) rules, that combines concepts from object-oriented languages,
SPARQL query language, and rule-based systems to model rules in the Semantic Web,
has better expressiveness and several advantages for geospatial applications, e.g. SPIN
rules can address the non-monotonic semantics and allow spatial predicates to be read-
ily embedded in the conditions within spatially enabled RDF store. Therefore, we argue
that the geospatial domain could appreciate the SPIN rules more (before its successor
SHACL 7 is better supported by tools).
Therefore, we have developed a new knowledge-based visualization framework in
which the ontologies and rules (SPIN rules) are tightly coupled. Figure 1 shows the
ontologies (knowledge base) used for data portrayal, and the SPIN rules are coupled
with the style through a predicate hasRules.
Fig. 1. The knowledge base for portrayal information.
Listing 1 demonstrates how a specific portrayal rule is formalized using SPIN in the
syntax of Turtle. And this rule formulates that if a building has been built for over 300
years and the rendering scale is larger than 1:10,000, then use the symboliser_0 to
symbolize the building. The INSPIRE vocabularies for 2D buildings are used in this
7 https://www.w3.org/TR/shacl/
6
case, and he age information is derived from the construction date. Furthermore, since
the symbolizers used for portrayal can be different in different visualization scales.
With a couple of such rules, the visualization programs can expose simple SPARQL
queries to retrieve the symbolizers used for portrayal in different scales and for the
features with different attribute values. In addition to this, we also designed ontologies
and rules for the knowledge of geometry source, i.e. different geometries are used for
each feature under different conditions and in different visualization scales (we de-
signed the ontology for scale information as metadata for geospatial data). Such rules
are also modelled in SPIN rules.
@[prefix definitions]
bu-core2D:Building a owl:Class;
spin:rule[
a sp:Construct;
sp:text"""
CONSTRUCT {?this symbolizer:isSymbolizedBy
portrayal:symbolizer_0}
WHERE{
?this bu-base: AbstractConstruction.dateOfConstruction/
bu_base:DateOfEvent.beginning ?built_up_time.
BIND(year(now())-
year(xsd:dateTime(?built_up_time)) as ?age)
FILTER(?age>300)
?client_scale a scale:ClientVisualisationScale;
scale:hasScaleValue ?rendering_scale.
FILTER(?rendering_scale<=10000)
}"""
].
Listing 1. An example of using SPIN rule to represent a portrayal rule in the syntax of Turtle.
4 Roadmap towards further knowledge-based
visualization
At this stage, we have created the knowledge base for visualization of geospatial data
through tightly coupling ontologies and rules. However, the information modelled here
is still insufficient. In order to further accomplish the vision of web of knowledge for
visualization, we need to incorporate more visualization/cartography knowledge which
is often embedded in complex programs or mind of cartographers. And we believe that
an infrastructure of such knowledge base would be of substantial help for knowledge
transfer.
Recently, we have initiated a research cooperation with cycling researchers to visu-
alize the cycling level-of-service in a spatial context (maps) to help the decision-makers
to observe the cycling infrastructure situation in real maps rather than merely spread-
sheets or sketch maps. However, the challenges arise in two respects: data integration
7
between the cycling data and geospatial features, and the transfer of visualization/car-
tography knowledge to the cycling researchers to generate competent geospatial visu-
alizations. Simply put, the challenges lie in data integration and knowledge formaliza-
tion, and this is where the Semantic Web technologies stand up. To address this issue,
we are planning to employ a knowledge-based approach, in which on the one hand, the
derivation of cycling level-of-service indexes is formalized using ontologies and rules;
and on the other hand, the visualization/cartography knowledge concerning e.g. the
color scale used for rendering, setting the width of the cycle lanes to make a slight
separation from the vehicle lanes to avoid confusion for the users, and also to embed
more information and knowledge into the legend to facilitate the users to perceive the
content of the thematic map.
There are still some challenges that need to be resolved to realize the knowledge-
based approach for visualizing cycling level-of-service, including:
• How to design ontologies to enable interoperability between geospatial road data
and cycling data collected by the cycling professionals of e.g. the type of the cycle
lane, the interaction between cycle lane and the adjacent vehicle lane? We prelim-
inarily plan to use the relative positioning approach proposed by [14] to facilitate
the information propagation from vehicle lanes to cycle lanes.
• What rule language would be sufficient for modelling the knowledge concerning
both the derivation of cycling level-of-service indexes as well as corresponding
cartographic knowledge of e.g. colour scale and feature displacement (which is in
fact more complex than the cycling knowledge). To this end, we would investigate
the successor of SPIN: SHACL, which includes the rule-based inference capacity
as its advanced feature 8. Also, we could also use SHACL for data validation, which
is also indispensable in our cross-domain data and knowledge sharing.
• How should the approach be evaluated e.g. in comparison to traditional methods?
5 Conclusion
In this paper, we have presented a framework for shaping a knowledge-based approach
for geospatial data integration and visualization, which can also be used for outreaching
the geospatial data and knowledge to other domains. We have designed a knowledge
representation approach tightly coupling ontologies and rules for the geospatial visual-
ization knowledge on the aspects of scaling, data portrayal and geometry source. In the
next steps, we will incorporate more visualization knowledge that could be used in dif-
ferent domains, and on such case that has been formulated is the visualization of cycling
level-of-service in spatial context. We expect this case study would showcase the ad-
vantage of Semantic Web technologies in terms of data and knowledge sharing between
different domains.
As stated earlier, the scope of this PhD project is broad and it investigates the bene-
fits of Semantic Web technologies could bring up for geospatial applications. Hence, it
is also interesting to investigate e.g. the use of Semantic Web (including the use of
8 https://www.w3.org/TR/shacl-af/
8
semantic rule) for real time integration between dynamic data (social media data) and
static geographic data in the context of disaster management. Also, how the semantic
gazetteers could foster better automatic construction of knowledge graph which in-
cludes spatial data is also an interesting topic to study.
References
1. Janowicz, K., Schade, S., Bröring, A., Keßler, C., Maué, P., Stasch, C.: Semantic enablement
for spatial data infrastructures. Transactions in GIS 14, 111-129 (2010)
2. Schade, S., Smits, P.: Why linked data should not lead to next generation SDI. In: Geosci-
ence and Remote Sensing Symposium (IGARSS) 2012 IEEE International, pp. 2894-2897.
IEEE (2012)
3. Atemezing, G.A., Troncy, R.: Comparing vocabularies for representing geographical fea-
tures and their geometry. In: Terra Cognita 2012 Workshop (2012)
4. Perry, M., and John Herring.: OGC GeoSPARQL-A geographic query language for RDF
data. Open Geospatial Consortium technical report (2012)
5. Regalia, B., Janowicz, K., Mai, G., Varanka, D., Usery, E.L.: GNIS-LD: Serving and Visu-
alizing the Geographic Names Information System Gazetteer As Linked Data. In: European
Semantic Web Conference, pp. 528-540. Springer (2018)
6. Goodwin, J., Dolbear, C., Hart, G.: Geographical linked data: The administrative geography
of great britain on the semantic web. Transactions in GIS 12, 19-30 (2008)
7. Varanka, D.E., Usery, E.L.: The map as knowledge base. International Journal of Cartog-
raphy (in press)
8. Dadzie, A.-S., Rowe, M.: Approaches to visualising linked data: A survey. Semantic Web
2, 89-124 (2011)
9. Jones, J., Kuhn, W., Keßler, C., Scheider, S.: Making the web of data available via web
feature services. Connecting a Digital Europe Through Location and Place, pp. 341-361.
Springer (2014)
10. Leon, A.d., Wisniewki, F., Villazón-Terrazas, B., Corcho, O.: Map4rdf-faceted browser for
geospatial datasets. In: Proceedings of the First Workshop on Using Open Ddata, W3C, 19–
20 June 2012, Brussels, Belgium (2012)
11. Hofer, B., Mäs, S., Brauner, J., Bernard, L.: Towards a knowledge base to support geopro-
cessing workflow development. International Journal of Geographical Information Science
31, 694-716 (2016)
12. Keßler, C., Raubal, M., Wosniok, C.: Semantic rules for context-aware geographical infor-
mation retrieval. In: European Conference on Smart Sensing and Context, pp. 77-92.
Springer (2009)
13. Gould, N., Mackaness, W.: From taxonomies to ontologies: formalizing generalization
knowledge for on-demand mapping. Cartography and Geographic Information Science 1-
15 (2015)
14. Huang, W., Mansourian, A., Abdolmajidi, E., Xu, H., Harrie, L.: Synchronising geometric
representations for map mashups using relative positioning and Linked Data. International
Journal of Geographical Information Science 32, 1117-1137 (2018)
15. Fellah, S.: Testbed-12 Semantic Portrayal, Registry and Mediation Engineering Report.
Open Geospatial Consortium technical report (2017)