Geospatial data have been pervasive and indispensable for various real-world application of e.g. urban planning, traffic analysis and emergency response. To this end, the data integration and knowledge transfer are two prominent 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 portrayal 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 outreaching such knowledge to other domains as future study.
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
standards which shape the current solutions for spatial data infrastructure (SDI). Such
standards are mainly from Open Geospatial Consortium (OGC), and most of them only
guarantee interoperability on a syntactic level, whereas the semantics and knowledge are
represented insufficiently. Therefore, we need a way for addressing the semantic
challenges concerning geospatial data and knowledge [
Visualization is one of the most important and pervasive application areas of geospatial 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 sensemaking 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
The application of Semantic Web technologies has developed considerably in
geospatial 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 https://www.stardog.com/ 3 https://virtuoso.openlinksw.com/
There is an ongoing trend of publishing geospatial data as Linked Data; initially
Semantic Web researchers showcased the potential of Linked Data by transforming
popular, third-party datasets to RDF, and then more Linked Data initiatives have been run
by governmental agencies and large-scale data infrastructures [
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
presenting the links between entities to facilitate the intuitive discovery of underlying
information and knowledge [
The capacity of knowledge representation of Semantic Web through leveraging
ontologies 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. [
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
identified by OGC, and thereof they initiated several testbeds to investigate a semantic
portrayal solution. They developed ontologies for semantically modelling the information
of style, symbol, symbolizer and graphic [
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 modelling 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 inference5; (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 inconsistency6. 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.
6 https://www.w3.org/Submission/spin-modeling/
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 limitations for geospatial applications; for example, SWRL adopts the open world assumption and thereof only supports monotonic semantics, and in some geospatial applications, 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 readily 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 SHACL7 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
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 designed 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
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 visualize 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 spreadsheets or sketch maps. However, the challenges arise in two respects: data integration between the cycling data and geospatial features, and the transfer of visualization/cartography knowledge to the cycling researchers to generate competent geospatial visualizations. Simply put, the challenges lie in data integration and knowledge formalization, 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
knowledgebased 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
preliminarily plan to use the relative positioning approach proposed by [
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 visualization 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 different 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 advantage 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 benefits 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
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 includes spatial data is also an interesting topic to study.