Geographic information is one of the fundamental core data sources for various applications. Freely available geographic information knowledge bases are emerging and the spatial dimension has become part of the Linked Open Data initiative. However, geographic information is stored as abstract geographic objects and exploring, extracting, and understanding the information must be facilitated for di erent user perspectives and use cases. We propose to use a semantic model and an extraction methodology which is aimed at allowing the consumption of geographic information in an intuitive way. We illustrate our approach based on previous work of a highway navigation conceptualization and present a functional approach to exploit granularity extractions targeted at enabling the user to change the point of view in navigation tasks.
Geospatial information is becoming more and more important for a variety of
applications in our everyday life. From supply chain management to e-Commerce
support and from navigation tools through to complex recommendation systems,
Geographic Information Systems are important interfaces to build suitable
applications with a local or global perspective. E orts in the elds of the Semantic
Web and Linked Data [
However, one challenge in Geographic Information Science is to develop suitable conceptual models that facilitate the understanding of geographic data, relating this data with information sources of other domains, and using this combined data to solve a speci c task. Therefore, we have to consider the user's
perspective and the goal he wants to achieve with the information provided in distributed and heterogeneous data sources. OpenStreetMap5, and the semantic counterpart LinkedGeoData, are based on the following abstract elements: nodes, ways, relations, and tags. LinkedGeoData is aimed at linking the spatial dimension with knowledge bases from other domains. Although semantics have become an integral part of the geographic perspective, the required semantics to understand and explore geographic information in an intuitive way and from di erent user perspectives are not yet integrated in the geographic linked data e orts.
The goal of our research is to e ciently handle geographic data on di
erent granularity levels by enabling the user to change the point of view. The
contribution of our paper is summarized as follows:
{ We implemented a descriptive model for topological navigation tasks based
on di erent levels of detail by modeling di erent graph representations in
the semantic model.
{ We formalized the granularity transformations by using canonical projection
functions.
{ We highlight future challenges and compare our approach with the approach
from Timpf and Kuhn [
The remainder of this paper is structured as follows: In Section 2, we describe
shortly the fundamental conceptual model for way nding as well as related work.
In Section 3, we present our ontology and the extractions to retrieve the data for
the navigation tasks at the previously proposed granularity levels. We continue
with the comparison of our approach with the approach from Timpf and Kuhn
[
The geographic conceptual model we have chosen to investigate was rstly
introduced by Timpf et al. [
Since the emergence of the Web of Data, there have been major e orts to populate the Linked Data cloud with useful information. The advantages of the Linked Data cloud are evident in many areas such as research, collaboration, and creation of value. Also in the eld of geospatial data, initiatives are pushing ahead and populate the Web with interlinked geospatial data [3{5]. Together with the
\Semantic Geospatial Web" [
The idea of multiple levels in the presentation of geospatial data, referring to
the concept of di erent levels of detail, is already explored in various application
elds of GI science. Weibel and Dutton [
In this section, we introduce our idea of how to develop a descriptional
semantic model, which allows di erent users to explore and understand geographic
information of road networks based on a topological network data model and
extract the corresponding information for the use cases introduced by Timpf and
Kuhn [
The Topological Network Model
Road networks are typically represented as graphs. Cities, interstates, and exits
or entrances are modeled as nodes and roads between the nodes are described
as links. For instance, the RDF Graph Modeling Language (RGML) illustrates
the opportunities of RDF to describe graph structures [
The following section illustrates how the ontology can be used to extract the
information required to solve previously introduced [
In our model, instead of granularity mappings, we use coarsening functions,
mapping from one conceptual level to another by removing information, given in
form of triples. This means that we explicitly suppress some information which
results in a more abstract level of detail. This process can be iterated (i.e.,
changing from one level to another) and based on di erent criteria. Starting with
the following notation for RDF graphs derived from the W3C RDF Semantics
speci cation [
E is the set of entities, which describe anything in the universe of discourse
and can be seen as a vocabulary [
P is the set of properties (i.e., the predicate of sentence), which describe the relations between entities and values.
V is the set of values, which are either entities or atomic values (i.e., literals).
The value of a triple is denoted as object of a sentence.
We start with a set E of entities, a set P of properties and a set V of values, that allow us to express sentences. We represent a world W, i.e. universe of discourse, in form of an RDF graph, which is formalized as a set of triples, where each triple t consists of an entity e 2 E , a property p 2 P, and a value v 2 V, i.e. t = (e; p; v), and therefore we obtain: W := fti : i = 1; 2; : : :g E P V .
By employing the concept of canonical projection functions on E P V (e.g., 1 retrieves the entity of triples), we can extract information and thus precisely describe certain entities which appear in triples (which can be subject to further conditions) in a world: e.g., to get all entities with property p0 and value v0 we can write:
A := 1 2 1(p0) \ 3 1(v0) \ W = fe 2 E : (e; p0; v0) 2 Wg
Therefore, properties and values allow us to search using di erent criteria. The canonical projection functions are de ned on the whole space E P V , which requires to intersect all appearing preimages with our universe of discourse W (as can be seen in the example above). In the following step, all triples including the identi ed entities as subject or object have to be removed. For instance, let us consider a country with several cities. We can use functions to nd these cities, but if we consider a more granular world, e.g. only cities with a high population, we have to remove all information about these small cities, i.e. all triples concerning these smaller cities. Hence we are interested in all triples with the entity e and values v not in A (with p = \is"; v = \small"), (Ac denotes A complement) so we consider the set:
fw 2 W : w = (e; p; v) ^ e 2= A ^ v 2= Ag = W \ 1 1(Ac) \ 3 1(Ac)
Based on the requirements of Timpf and Kuhn [
E
P
V According to our construction, an operation that is executed on a given level of detail can also be executed on a more detailed level, e.g. the function route planning has to be executable at the planning level as well as the driver level. However, it is not possible to execute the function driving at the planning level.
In order to map from the driving level to the instructional level, we remove triples from Wdriver describing the lanes and road segments, resulting in a predicate containing a disjunction (denoted as _):
C1 := fe 2 E : (e; \isA"; \Lane") _ (e; \isA"; \Segment") 2 Wdriverg =)
Winstructional = fz 2 Wdriver : z = (e; p; v) ^ e 2= C1 ^ v 2= C1g
Similarly, the coarsening function which maps from the instructional to the planning level includes removing the directions of the highways. We assume that in this case this comprises all \Ramps", \Junctions", and \Directions":
C2 := fe 2 E : (e; \isA"; \Ramp") _ (e; \isA"; \J unction")_
_(e; \isA"; \Direction") 2 Winstructionalg =)
Wplanning = fw 2 Winstructional : w = (e; p; v) ^ e 2= C2 ^ v 2= C2g 4
In this section, we compare our implementation for granularity transformations
based on Linked Data with the Graph Transformations of Timpf and Kuhn [
The rst advantage of Linked Data is that every relation and information can be mapped in a exible and iterative way. Hence, instead of using di erent graphs, where we have to use graph amalgamations to map from model to model, we can describe the connectivities of the entities in one descriptional model. Hence, Timpf and Kuhn's approach does not support the semantics between the described entities. For instance, when exploring a lane, we want to know the name of the highway the lane belongs to. Extractions facilitate in addition the retrieval of smaller subsets of information, which can be compared to humans' natural abstraction abilities. The second advantage we could identify is that the Linked Data model can be easily enriched with further information that can be used for various applications. For instance, nodes could be enriched with sights or landmarks to improve navigation tools or the links could be enriched with risk data that can be used to decide about the best route in supply chain management. 5
The proposed approach leaves some open challenges. In this section, we discuss challenges arising when using RDF graphs for way nding.
Incompleteness Modeling the real world in simple triples is a time-consuming
and challenging task, since the annotation of all conceivable conditions requires
signi cant e orts in creating descriptive content. The knowledge base we
create is, therefore, mostly incomplete and reasoning based on this knowledge base
will not always reveal the best solution. Arguable, the Semantic Web, Linked
Open Data, and an open data community represent the the rst step to
mitigate the problem of incomplete knowledge, since everyone can contribute and
extend the fundamental knowledge for reasoning. For instance, with the
mentioned concepts, linking a concrete route by using an array of location points
(a snapshot of the route) is a standard task. Linking nodes of OpenStreetMap
enables OpenStreetMap contributors to edit the information when exploring the
network graphs.
Imperfect Models While modeling the ontology introduced in Section 3.1, it
became apparent that modeling the concepts and relations is highly dependable
on the application area and the use cases to be considered. Creating the perfect
model for all kinds of application tasks is therefore not possible. In Section 3.2,
we have introduced our idea of granularity transformations. Extracting,
transforming, and mapping information out of a comprehensive knowledge base into
a suitable model is, due to RDF's exible nature, a justi ably tting approach.
In this paper, we have redesigned the formalization rstly introduced in [
Oversized Search Space In [
The Web of Data is gaining importance, o ering users the possibility to utilize it as an open knowledge base serving information for various applications. Publishing and consuming data for geographical reasoning is still in an early stage, since the application elds are versatile and the development of fundamental ontologies and query languages has not been nished. The data of proprietary navigation tools is kept on inaccessible company servers. This major drawback inspired us to analyze a previously introduced formal model for way nding on di erent levels of detail. We used the derived results to develop an ontology and functional transformation operations, which should facilitate the consumption of topological network data.
We aim to extend our rst approach to a framework that should enable to explore geospatial data and useful links in an intuitive way, which should facilitate data retrieval, linking, visualization, and hence the consumption of open data with a geospatial perspective.
Acknowledgements This research was partially funded by the Vienna University of Technology through the Doctoral College Environmental Informatics.