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Knowledge-based Management and Reasoning
on Cultural and Natural Touristic Routes
Evangelos A. Stathopoulos, Alexandros Kokkalas, Eirini E. Mitsopoulou,
Athanasios T. Patenidis, Georgios Meditskos, Sotiris Diplaris, Ioannis Paliokas,
Stefanos Vrochidis, Konstantinos Votis, Dimitrios Tzovaras, and Ioannis
Kompatsiaris
Information Technologies Institute,
Centre for Research & Technology - Hellas, Thessaloniki, Greece
{estathop, akokkalas, emitsopou, apatenidis, gmeditsk, diplaris,
ipaliokas, stefanos, kvotis, dimitrios.tzovaras, ikom}@iti.gr
Abstract. There is great potential in interdisciplinary traveling plat-
forms mingling knowledge about cultural heritage aspects, such as places
with schedules providing visits or even containing augmented reality fea-
tures also, along with environmental concerns to enhance personalized
tourist experience and tripping avocation. For an ontological framework
to support and nominate trip detours of targeted interests according to
end-users, it should incorporate and unify as much heterogeneous infor-
mation, deriving either from web sources or wherever there are ubiqui-
tously available such as sensors or open databases. A plethora of qual-
itatively diverse data along with adequate quantities of them escalate
the contingent results in terms of conferring a plurality of relevant op-
tions which can be utterly manifested through involving axioms with
rule-based reasoning functionalities upon properties considered to be ir-
relevant to each other at first glance. Thus, managing to import prede-
fined concepts from other ontologies, such as temporality or spatiality,
and combine them with new defined concepts to tourist assets, such as
points of interest, results in novel meaningful relationships never estab-
lished before. Apart from the utilization of pre-existent resources and
logic towards automatic detouring suggestions, a wide-spectrum model-
ing enables a suitable problem statement relevant to the e-Tracer frame-
work and comprehension of the issues, providing the opportunity of sta-
tistical analysis of knowledge when adequate amounts amassed.
Keywords: Ontologies ·Reasoning ·Semantically enriched geodata ·
Data homogenization ·Route recommendation subsystem
1 Introduction
Vast amounts of data are effusive throughout every ecosystem. Gradually, the
ability to effectively capture data for knowledge extraction has increased. Digi-
tal agents tend to pseudomimic mental processes, such as deductive reasoning,
intricate decision making, inferring general assumptions and so on.The chasm
2 E.A. Stathopoulos et al.
between data and knowledge is bridged by semantics, inserting and fusing con-
texts into otherwise meaningless data. Ultimately, the interest is not on the value
itself but on its representation and meaning inside a system and its exploitation.
Today, there are many methodologies followed towards knowledge design and
manipulation; in that aspect, one can amalgamate disparate conceptions into a
unified model. To achieve the wished level of homogeneity, the affinities of enti-
ties must become firmly established, the capitalization of which is the rapidity
and deftness of knowledge elaboration to infer with logic as a basis.
In this paper we describe an ontology-based framework for capturing and
interlinking assets of cultural and natural substance facilitating the formation
of routes via the utilization of spatio-temporal rule-based reasoning. The on-
tological model encompasses and depicts every unique data genre present in
the workflow of the platform: genres related to weather, topological formats ex-
pressing geometry and geospatiality (such as points in 3D space and routes),
time formats expressing temporality, user profiling, Augmented Reality (AR)
and hierarchical content categorization of places along with other information.
That way, places might be discarded or included in a final route recommendation
for the end-user. To complete this task, a systematic evaluation was performed
to assess the abundance of our approach.
Our methodology starts by identifying each relevant entity tagged as coher-
ent with the e-Tracer objectives, as described above. Thereinafter, an extensive
research on pre-existing ontologies encapsulating relative knowledge concepts
was conducted. Upon the dilemma emerged concerning either the entire imports
of concepts (where the majority of structures were needed) or manufacturing our
own, we concluding in building custom concepts targeting precisely our objec-
tives and linking afterwards where applicable. At the same time, we paid effort
to keep a minimalistic approach in the design of the overall system. Based on
the ontology already created, the aggregation of web content or content from of-
ficial databases was populated in the knowledge base. Finally, rules that should
diminish significantly the offered number of selections based on property con-
straints were implemented and incorporated into web Application Programming
Interfaces (APIs).
The rest of the paper is structured as follows: Section 2 presents work which
at some extent seems coherent to this paper. Sections 3 provides an overview of
the framework and bestows the overall vision and motivation. In Section 4 elab-
oration on the inference, validation and consistency capabilities are exhibited,
while in Section 5 fundamental reasoning functionalities relying on time and
geo-spatial properties are showcased. In Section 6 quantitative and qualitative
evaluation is displayed and, finally, Section 7 concludes our work.
2 Related Work
Data about Cultural and Natural places are bound with their location informa-
tion and time parameters. With the increasing amount of geospatial data being
published online and the geographic information taking a crucial part in several
Knowledge management and reasoning on touristic routes 3
central hubs on the Linked Data Web; geospatial semantics, geo-ontologies and
semantic interoperability can have a key role in supporting publishing, retrieving,
and reusing data while reducing the risks of misinterpretations [1], [2]. Moreover,
various semantic web technologies have been adapted for geospatial data, with
progress in the effectiveness of the methods used in [3] with space and time having
a key role for definition, organization and mutual interaction between concepts
for knowledge engineering [4]. In similar approach, RDF models regarding space-
time events have been designed, that integrate spatial, temporal and semantic
relations for capturing factors behind certain geographic changes [5]
Aggregating geospatial datasets into a single one is a challenging task. To
tackle this problem semantic technologies were deployed so as to automate the
geospatial data conflation process. By using ontology, RDF data conversion and
a set of SWRL rules, one can produce a Points of Interest (POIs ) dataset with
reduced duplicates and improved accuracy [6]. Furthermore, the semantic onto-
logical network graph (SONET) [7] is an ontological network to match categories
across multiple heterogeneous sources of POIs of Volunteered Geographic Infor-
mation (VGI) data. This ontological network advances the study of VGI data by
enabling cross-platform analysis while it supports the use of POI data in land
use mapping and population modeling applications. Deepening in the issue of
heterogeneity since particularly cultural heritage consists of multiple resources
which might include entities such as places, events, availability, and others that
have special characteristics and might be connected with each other. Cultural-
ON is a suite of ontology modules, to model the principal elements identified
in the cultural heritage data type classification. The result is a knowledge base
consisting of semantic interconnections with also other data available in the Web
to be exploited according to different tasks and users preferences [8].
Taking advantage of cultural assents and towards touristic recommendations,
there are several ontology-based systems available. They provide personalized
suggestions to users, based on user profiles and information concerning the sug-
gested locations. The results are ranked based on profile assignment, content
filtering and user feedback [9] or by ontology-based content analyzer, ontology-
based profile learner, and ontology-based filtering component [10] or in the case
of STAAR (Semantic Tourist information Access and Recommending) where
algorithms take into consideration itinerary length and user interests [11].
3 e-Tracer Framework
In a world with liberty and convenience of locomotion, data can be aggregated in
abundance, enabling the sector of personalized tourism to attribute with further
enhancements to provide a more pleasant experience to the end-user, an effort
also facilitated by e-Tracer [12], a national funded project.
3.1 Key Concepts and Vision
The conceptual architecture of the platform is depicted in Fig. 1. Briefly, the
Web Content involves data derived from official websites and open governmen-
4 E.A. Stathopoulos et al.
Fig. 1. The conceptual framework of e-Tracer
tal databases. For the former the easIE framework [13] was used to scrap content,
while for the later massive file exports sufficed. Furthermore, the Route Forma-
tion Pipeline expresses a complex algorithm to create routes, containing not
only reasoning functionalities but also personalization based on similarity mea-
sures and graph algorithms such as the traveling salesman approaches, Dijkstra’s
shortest path and so on. Moreover, the Augmented Reality Pipeline encompasses
search, identification, retrieval and display of objects on a Smart Device. Con-
cluding, for aggregating information and presenting it to the User the Frontend
Platform Services are responsible for.
Our work is focused on the semantics and aims to enrich e-Tracer with such
capabilities. It acts as a semantic middleware, capturing, interlinking and serv-
ing results. This is materialized in the ”Analysis & Unification with Ontologies”
component where dissimilar content is homogenized and stored locally in com-
pliance with Resource Description Framework (RDF) triplets inside a Knowl-
edge Base (KB). Additionally, it provides the semantic infrastructure to retrieve
stored assets in an asynchronous on-demand manner to fulfil dynamic querying
requirements. Furthermore, the reasoning occurs either automatically on each
update of the KB or by calling it as a service where data are reciprocated am-
phidromously. Concluding, it is obvious from Fig. 1 that the use of semantics
stands in the core of the platform adding extra value and coordinating a majority
of processes.
3.2 e-Tracer Ontological Core Model
Figure 2 illustrates the upper-level concepts of the e-Tracer hierarchical model
where each differentiated arrow line is depicting a distinct type of connection
and dotted lines implying customness. The conceptual model revolves around the
notions of point of interest (POI),event,augmented reality object,spatial object,
Knowledge management and reasoning on touristic routes 5
Fig. 2. The upper-level core concepts of the e-Tracer model and the semantic conju-
gation
temporal entity,route,user profile,user feedback and weather. Each distinct
connotation is potentially intertwined with others in manifold degrees, both at
root and lower modeled levels, in a way that there should not be perceived
in the overview graph such a phenomenon as an orphaned node when data
increment efficiently and satisfactorily. Intuitively, entities will be linked with
pre-existing online entities as soon as the core model reaches near its terminal
configuration after numerous successive iterations. In the succeeding subsections
we circumscribe extensively each key concept along with its semantic correlation
with the rest.
Re-used Concepts. The concept of the Event is described both accurately and
sufficiently by the ontology for linking open descriptions of events, LODE [14].
In its simplicity, it is explained more thoroughly in the next subsection along
with how supplementary content was formed on top of this work to extend it.
The GeoSPARQL ontology [15], abiding by strictly to the Open Geospatial
Consortium’s standards, was shaped to represent and provide functionalities
for objects possessing physical extent. In that aspect, as a ”Feature” can be
characterized anything from points in space to more intricate corporeal spa-
tioformations such as polylines and acanonic, convex or non-convex, polygons.
Noticeably, this supplementary property can be exploited to typify e-Tracer’s
concepts such as routes,POIs,events and furthermore the weather.
In conjunction with efforts towards the aforementioned localization, OWL-
Time [16] instils the nature of temporality into the e-Tracer ontology. Periodic
and sporadic chronic intervals can be analytically specified and represented,
spanning from time-limited events to recurring schedules of points of particular
interest, as well as unfolding iterative events such as weather phenomena. Con-
sequently, the fusion of space and time capacitates a thorough designation for
when an entity is shifting through those dimensions, facilitating perpetual and
unremitting knowledge monitoring of its evolutionary existence.
6 E.A. Stathopoulos et al.
Finally, we took into consideration previous work regarding the User Profile
concept and collected very few content from [17] as described further below and
extended it according to our needs with additional properties and relations with
other entities.
Points Of Interest, Events & Routes. The necessity for differentiation be-
tween the POI and the Event relies on the logical assumption which dictates
that an Event might occur at a POI, coinciding spatially at the exact same
longitude and latitude, thus being associated with it or at a place which is not
regarded as a POI at all, also containing coordinates unclaimed by any POI.
On the contrary, a POI might or might not ever hold an event, without being
self-defined by it in any case. In terms of OWL 2 semantics, this is defined as:
Route ≡F eature u ∀includes.(P OI u ∀isAssociatedW ith.Ev ent)
Regarding temporal discrepancies, we take into consideration that a POI is per-
manently established, mayhaps withholding a somewhat fixed schedule, whereas
an Event is strictly time-limited and even if periodicity is witnessed, each re-
sumption will be considered a distinct instance. Apart from the dimensional per-
spective, a qualitatively extensive context-based research, based on local govern-
mental resolutions 1,2,3,4,5, has been conducted so as to conclude to a class-based
hierarchical categorization of eventual supported types of POIs and Events of
interest.
Ultimately, the existence of an instance of the class Route solely depends on
it incorporating either an instance of a POI or that of an Event at least, in a
ranked manner which mandates the order of visit each Route suggests. Routes are
being generated dynamically in a more compound integrated algorithm, which
is out of the scope of this paper, due to the humongous population of potential
options, thus not pre-processed or stored locally where the later occurs only after
an end-user has completed successfully a proposed detour, on several occasions
accompanied by an evaluation of his overall experience in the form of User
Feedback.
User Profile, User Feedback & History Of Usage. The User Profile con-
struction encapsulates basic demographic information plus personal interests
complemented by the end-user himself or potential impairments. Each instance
is bound to possess a History of Routes and a History of POIs archiving each
Route, and within, each POI the user has indeed paid a visit to, which is cross
validated via the end-user’s smart device geolocalization. Furthermore, the sub-
classes of User Feedback are related at a lower level with the historical assets of
1http://odysseus.culture.gr
2http://listedmonuments.culture.gr
3http://estia.minenv.gr/
4http://www.minagric.gr
5http://www.opengov.gr/
Knowledge management and reasoning on touristic routes 7
the POI and the Route of the ontology, rendering infeasible to submit a per-
sonal standardized evaluation about a spatial entity without a priori ratified
attendance by the appropriate digital agent of the platform.
U serP rof ile ≡ ∀hasF eedback.U serF eedback u ∀hasRouteHistory
.(HistoryOf Routes u ∃hasRoute.Route)
Augmented Reality & Weather Features. The Augmented Reality Object
intends to enclose a profusion of essential properties and describe thoroughly in
a semantic way 2D, 2.5D and 3D objects. Unfortunately, there is a scarcity in
modelled digital assets to the applied interests of e-Tracer, nevertheless, due to
the purposeful adaptability of the system beyond fixed use cases, it was deemed
vital to patronize such features. Pragmatically, such an object might only be
related to spatial entities, potentially deriving implicit inferences from them
according to each scenario. Moreover, the Weather in its simplicity is considered
to hold, apart from self-explanatory data properties, both temporal and spatial
attributes, achieving to monitor evolutionary weather phenomena across regions
pertaining POIs or Events, eventually served to the end-user as plain information
or taken into consideration in rule-based reasoning upon nominating routes.
4 Inference and Validation
4.1 Implicit Relationships
Extra logical assumptions are the outcome of blending native OWL 2 RL reason-
ing and manually constructed custom rules, where the prior relies on the OWL 2
RL profile semantics [18]. Sadly, OWL 2 is limited as it serves modeling only for
instances related in a tree-like approach [19]. Our framework implements domain
rules on top of the standard graphs in order to enunciate richer relations by the
utilization of CONSTRUCT graph motifs, thus enabling the identification of
valid inferences. For example, an Augmented Reality Object instance never con-
tains information about its geolocalization but in our ontology is always attached
to a spatial entity, which in turns contains coordinates that can be bequeathed
to it via the suitable SPARQL CONSTRUCT query shown below:
CONSTRUCT {
?arobject geo:asWKT ?coordinates.
} WHERE {
?arobject etr:relatedTo ?a2.
?a2 geo:hasGeometry ?a3.
?a3 geo:asWKT ?coordinates.
}
4.2 Consistency & Validation Check
The validation procedure guarantees the consistency of the framework along with
the quality, both morphologic and syntactic. This scope is fulfilled through the
8 E.A. Stathopoulos et al.
usage of both custom SHACL [20] validation rules and native ontology consis-
tency checking, always adhering to the closed-world paradigm. The latter man-
ages validation by considering the semantics at TBOX, such as class disjointness,
whereas the first discerns constraint contraventions like imperfect information
or cardinality contradictions. For example, a SHACL shape representing a con-
straint which forces all POIs to contain exactly a single ID as a data property
of type string is shown below:
etr:POIIDShape
a sh:NodeShape;
sh:targetClass etr:POI;
sh:property [
sh:path etr:hasID;
sh:datatype xsd:string;
sh:minCount 1;
sh:maxCount 1;
].
5 Spatio-temporal Rule-based Reasoning
Entire concepts were imported from well-known ontologies and were combined
so as to administer especial properties to specific instances, thus conferring ad-
ditional capabilities on ruled-based reasoning in order to succeed in a significant
diminish in the pool of recommended selections which flow to posterior in-chain
services with ultimate objective to deliver delightful route suggestions to the
end-user.
The very essence of reasoning in e-Tracer relies on the meaningful restriction
of proffered choices. It has already been stated explicitly that each place of inter-
est withholds formal standardized coordinates nearby a major traffic network. On
top of those coordinates, functions and APIs based on [15] were developed and
utilized so as to estimate euclidean distances between interchanges of the initial
route and the places of interest, realistically serving as a lower distance bound
where at best case the euclidean distance equals the actual driving distance.In
addition, the fixed traffic network speed limits facilitated the development of an
algorithm about approximate calculation of the time needed to arrive from one
place to another, also serving as a lower estimation bound.
The algorithms described above were fused into dynamic hybrid rules ex-
pressed in complex SPARQL queries, combining time and space dimensions
and an additional boolean variable of accessibility impairment to showcase the
true potential of complex rule-based reasoning. Furthermore, it is not obliga-
tory to set all limitations at once for the API to function, e.g. sometimes we
only mind for distance and not at all for time or accessibility. Consequently,
by applying limitations when retrieving places of interest correlated to each in-
terchange within the main route of the end-user, the options stand fewer than
before based on logic and necessarily satisfy either default constraints or con-
straints set by the end-user himself. A sample SPARQL pseudocode applying
Knowledge management and reasoning on touristic routes 9
time and space constraints is provided in Algorithm 1 where if input variables
are set to zero the algorithm does not consider that variable for filtering at all:
Algorithm 1: Spatio-temporal Reasoning SPARQL Pseudocode
Data: Interchange, POI Coordinates, Interchange Coordinates
Input : DistanceOfTravel, TimeOfTravel ∈N
Output: A list of POIs
initialization;
foreach Interchange do
GET each POI coordinates and the Interchange coordinates;
foreach POI Coordinates do
if DistanceOf T rav el 6= 0 then
X=euclidean distance( POI Coordinates,
Interchange Coordinates);
end
FILTER (X≤DistanceOf T rav el) ;
if T imeOf T ravel 6= 0 and DistanceOf T ravel 6= 0 then
Y= (X×60) ÷90000;
end
FILTER (Y≤T imeOf T ravel) ;
if T imeOf T ravel 6= 0 and DistanceOf T ravel = 0 then
Z=euclidean distance(POI Coordinates,
Interchange Coordinates)×60 ÷90000;
end
FILTER (Z≤T imeOf T ravel);
end
end
6 Evaluation
Currently, a user-centered evaluation stands infeasible as the pilots are due to
commence in subsequent months, ipso facto we focalized in system-wise bench-
marking. In that aspect, we demonstrate the population of the stored entities,
shown in detail in Table 1. The triple store at our disposal is a GraphDB 9.1.1
Free Edition with currently stored 15286 triples which was deployed at a server
with Ubuntu 18.04.4 LTS (Bionic Beaver) 64-bit operating system, an Intel
Xeon(R) Silver 4108 CPU @ 1.80GHz x 32, 62.5GB of RAM and a Hard Disk
Drive of 3.6TB capacity.
Table 1. The number of POIs and Events with (average) sum of properties for each,
inside the knowledge base
#POIs &Events #Properties Avg. Properties per POI /Event
257 11627 ≈45
10 E.A. Stathopoulos et al.
Unfortunately, it is only anticipated to incline the evaluation towards the
engineering response times in a manner where the bias is eliminated. All but
one methods were manufactured as dynamic RESTful, thereby we ensured upon
summoning that the variables on call conform to a uniformly distributed pseu-
dogenerator with their range values spanning with equal probability of selection
to all meaningful and valid content. All of them gratify the competency ques-
tions which were documented formerly of the creation of the e-Tracer ontology,
a subset of which is showcased in Table 2, along with mean response times and
standard deviations, elicited from 1000 executions for each.
Table 2. Exemplary competency questions
# Question Mean (SD) in
msec
Q1 Retrieve all registered POI names with their respective IDs 27 ±8
Q2 Retrieve all related properties to a pseudorandom POI 19 ±10
Q3 Retrieve all POI names with their IDs registered to a pseudo-
random interchange bound to pseudorandom time & distance
constraints
22 ±11
Q4 Retrieve all related properties to POIs registerd to a pseudo-
random interchange bound to pseudorandom time & distance
constraints
114 ±139
Q5 Retrieve all related properties from multiple POIs registered to
pseudorandom multiple interchanges bound to pseudorandom
time & distance constraints
3277 ±4491
A simulation example of our approach is displayed in Fig 3, while moving from
point A to point B, where all POIs retrieved from the interchange without any
reasoning occurring stand 43. On the contrary, it is conceived that the number
of 5 POIs is noticeably less when the constraint of time is set to 30 minutes and
that of the distance to 20 kilometers.
7 Conclusion And Future Work
In this paper we presented an ontology-based framework for encapsulating and
interlinking resources of cultural and natural nature towards the construction
of suggestive enhanced routes. On top of the structured knowledge we practised
rule-based reasoning based on spatial and temporal properties of the assets.
At the moment, the work featured is part of a synthesis of services, where
dynamic routes are formed based only on the distinct unary level. Consequently,
looking to the future, the next step is to exploit knowledge at a more aggre-
gated level, such as applying reasoning at route level. Apart from reasoning, at
final stages the resources of e-Tracer ought to be openly linked to other efforts,
following the principles of Open Data & Linked Data.
Knowledge management and reasoning on touristic routes 11
Fig. 3. Geographic map of simulation
The evaluation plan at a cultural level will be orchestrated by Piraeus Bank
Group Cultural Foundation whereas at environmental level Axios - Loudias - Ali-
akmonas Delta, Koronia-Volvi and Pamvotis lakes protected area management
bodies are responsible for. The platform encapsulates the Egnatia Motorway
axis for pilots and content provided by the prior organizations. Finally, the as-
sessment of the prototypes will be conducted during the pilot tests applied to
2 collaborating museums (the silversmithing museum and the silk museum ) and
at least 3 areas of environmental interest.
Acknowledgements
This work is co-financed by the European Union and Greek national funds via the
Operational Program Competitiveness, Entrepreneurship and Innovation, under
the call RESEARCH-CREATE-INNOVATE (project code: T1E∆K-00410).
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