Conference PaperPDF Available

Knowledge-Based Management and Reasoning on Cultural and Natural Touristic Routes


Abstract and Figures

There is great potential in interdisciplinary traveling platforms mingling knowledge about cultural heritage aspects, such as places with schedules providing visits or even containing augmented reality features 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 information, deriving either from web sources or wherever there are ubiquitously available such as sensors or open databases. A plethora of qualitatively diverse data along with adequate quantities of them escalate the contingent results in terms of conferring a plurality of relevant options which can be utterly manifested through involving axioms with rule-based reasoning functionalities upon properties considered to be irrelevant to each other at first glance. Thus, managing to import predefined 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 established before. Apart from the utilization of pre-existent resources and logic towards automatic detouring suggestions, a wide-spectrum modeling enables a suitable problem statement relevant to the e-Tracer framework and comprehension of the issues, providing the opportunity of statistical analysis of knowledge when adequate amounts amassed.
Content may be subject to copyright.
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
Information Technologies Institute,
Centre for Research & Technology - Hellas, Thessaloniki, Greece
{estathop, akokkalas, emitsopou, apatenidis, gmeditsk, diplaris,
ipaliokas, stefanos, kvotis, dimitrios.tzovaras, ikom}
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-
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
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
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
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:
?arobject geo:asWKT ?coordinates.
?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:
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
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
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);
FILTER (XDistanceOf T rav el) ;
if T imeOf T ravel 6= 0 and DistanceOf T ravel 6= 0 then
Y= (X×60) ÷90000;
FILTER (YT 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;
FILTER (ZT imeOf T ravel);
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
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
22 ±11
Q4 Retrieve all related properties to POIs registerd to a pseudo-
random interchange bound to pseudorandom time & distance
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.
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: T1EK-00410).
1. 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.
2. Homburg, T., Prudhomme, C., W¨urriehausen, F., Karmacharya, A., Boochs, F.,
Roxin, A., & Cruz, C. (2016, July). Interpreting heterogeneous geospatial data using
semantic web technologies. In International Conference on Computational Science
and Its Applications (pp. 240-255). Springer, Cham.
3. Zhao, T., Zhang, C., Wei, M., & Peng, Z. R. (2008, September). Ontology-based
geospatial data query and integration. In International Conference on Geographic
Information Science (pp. 370-392). Springer, Berlin, Heidelberg.
4. Janowicz, K. (2010). The role of space and time for knowledge organization on the
semantic web. Semantic Web, 1(1, 2), 25-32.
12 E.A. Stathopoulos et al.
5. Fan, J., & Stewart, K. (2016). Modeling and Reasoning about Geospatial Event
Dynamics Using Semantic Web Technologies. In SDW@ GIScience (pp. 17-25).
6. Yu, F., McMeekin, D. A., Arnold, L., & West, G. (2018, January). Semantic web
technologies automate geospatial data conflation: conflating points of interest data
for emergency response services. In LBS 2018: 14th International Conference on
Location Based Services (pp. 111-131). Springer, Cham.
7. Palumbo, R., Thompson, L., & Thakur, G. (2019, November). SONET: a semantic
ontological network graph for managing points of interest data heterogeneity. In
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Geospatial
Humanities (pp. 1-6).
8. Lodi, G., Asprino, L., Nuzzolese, A. G., Presutti, V., Gangemi, A., Recupero, D.
R., ... & Orsini, A. (2017). Semantic web for cultural heritage valorisation. In Data
Analytics in Digital Humanities (pp. 3-37). Springer, Cham.
9. Alonso, K., Zorrilla, M., I˜nan, H., Palau, M., Confalonieri, R., V´azquez-Salceda,
J., ... & Castro, E. (2012, June). Ontology-based tourism for all recommender
and information retrieval system for interactive community displays. In 2012 8th
International Conference on Information Science and Digital Content Technology
(ICIDT2012) (Vol. 3, pp. 650-655). IEEE.
10. Bahramian, Z., & Abbaspour, R. A. (2015). AN ONTOLOGY-BASED TOURISM
International Archives of the Photogrammetry, Remote Sensing & Spatial Informa-
tion Sciences, 40.
11. Cao, T. D., Phan, T. H., & Nguyen, A. D. (2011, October). An ontology based ap-
proach to data representation and information search in smart tourist guide system.
In 2011 Third International Conference on Knowledge and Systems Engineering (pp.
171-175). IEEE.
12. Stathopoulos, E. A., Paliokas, I., Meditskos, G., Diplaris, S., Tsafaras, S., Valk-
ouma, E., ... & Tzovaras, D. (2019, October). Smart Discovery of Cultural and
Natural Tourist Routes. In IEEE/WIC/ACM International Conference on Web
Intelligence-Companion Volume (pp. 208-214).
13. Gkatziaki, V., Papadopoulos, S., Mills, R., Diplaris, S., Tsampoulatidis, I., & Kom-
patsiaris, I. (2018). easIE: Easy-to-use information extraction for constructing CSR
databases from the web. ACM Transactions on Internet Technology (TOIT), 18(4),
14. Shaw, R., Troncy, R., & Hardman, L. (2009, December). Lode: Linking open de-
scriptions of events. In Asian semantic web conference (pp. 153-167). Springer,
Berlin, Heidelberg.
15. Battle, R., & Kolas, D. (2011). Geosparql: enabling a geospatial semantic web.
Semantic Web Journal, 3(4), 355-370.
16. Hobbs, J. R., & Pan, F. (2006). Time ontology in OWL. W3C working draft, 27,
17. Maria, G., Akrivi, K., Costas, V., George, L., & Constantin, H. (2007). Creating
an ontology for the user profile: Method and applications. In Proceedings AI* AI
Workshop RCIS.
18. Motik, B., Grau, B. C., Horrocks, I., Wu, Z., Fokoue, A., & Lutz, C. (2009). OWL
2 web ontology language profiles. W3C recommendation, 27, 61.
19. Motik, B., Cuenca Grau, B., & Sattler, U. (2008, April). Structured ob jects in
OWL: Representation and reasoning. In Proceedings of the 17th international con-
ference on World Wide Web (pp. 555-564).
20. Knublauch, H., & Kontokostas, D. (2017). Shapes Constraint Language (SHACL),
W3C Recommendation. World Wide Web Consortium.
... Another ontology-based system is Do-Care, a health-care monitoring system for patients with chronic diseases that uses a SWRL rule base to support the supervision and follow-up of outdoor and indoor patients suffering from chronic diseases (Elhadj et al., 2021). In the tourism domain, Stathopoulos et al. (2020) present an ontology-based framework for capturing and interlinking assets of cultural and natural substances facilitating the formation of routes through spatial-temporal rule-based reasoning. ...
Purpose The increasingly competitive hotel industry and emerging customer trends where guests are more discerning and want a personalized experience has led to the need of innovative applications. Personalization is much more important for hotels, especially now in the post-COVID lockdown era, as it challenges their business model. However, personalization is difficult to design and realize due to the variety of factors and requirements to be considered. Differences are both in the offer (hotels and their rooms) and demand (customers’ profiles and needs) in the accommodation domain. As for the implementation, critical issues are in hardware-dependent and vendor-specific Internet of Things devices which are difficult to program. Additionally, there is complexity in realizing applications that consider varying customer needs and context via existing personalization options. This paper aims to propose an ontological framework to enhance the capabilities of hotels in offering their accommodation and personalization options based on a guest’s characteristics, activities and needs. Design/methodology/approach A research approach combining both quantitative and qualitative methods was used to develop a hotel room personalization framework. The core of the framework is a hotel room ontology (HoROnt) that supports well-defined machine-readable descriptions of hotel rooms and guest profiles. Hotel guest profiles are modeled via logical rules into an inference engine exploiting reasoning functionalities used to recommend hotel room services and features. Findings Both the ontology and the inference engine module have been validated with promising results which demonstrate high accuracy. The framework leverages user characteristics, and dynamic contextual data to satisfy guests’ needs for personalized service provision. The semantic rules provide recommendations to both new and returning guests, thereby also addressing the cold start issue. Originality/value This paper extends HoROnt in two ways, to be able to add: instances of the concepts (room characteristics and services; guest profiles), i.e. to create a knowledge base, and logical rules into an inference engine, to model guests’ profiles and to be used to offer personalized hotel rooms. Thanks to the standards adopted to implement personalization, this framework can be integrated into existing reservation systems. It can also be adapted for any type of accommodation since it is broad-based and personalizes varying features and amenities in the rooms.
... Its use constitutes a path towards prosperity and development of countries and rural communities, which, until now, have only been visible through the spatial location of tourist attractions on the web [12,29]. Specifically, the use of computer software in the phase of planning and development of a tourist route requires the integration of features such as reliable information [16,30], the means of transportation and transportation infrastructure (public, private, and using bicycles or hiking), transportation time [31], optimal distance [32], available scenic attributes [33], and cultural heritage aspects [34]. In tourism, the use of "info-structure" computer tools has added many benefits to the value chain [35]. ...
Full-text available
The design of new routes is a specific strategy to improve tourism management and to increase the attractiveness of landscape features, promoting activities as a part of sustainable development. This study proposes the design of alternative multi-parameter tourist routes in the Chimborazo Wildlife Reserve based on spatial network analysis implemented in ArcGIS 10.5 ® software. Tourist interest points were identified and mapped using spatial analysis software, then two routes for bicycles and hiking were defined as being the most efficient, based on the most frequented tourist attractions. The main contribution of this study is the identification of optimal routes for vehicular, bicycling, and hiking traffic through tourist attractions, considering variables such as the time, distance, average circulation speed, road state, and tourist facilities. As a result, two routes were identified. Route one includes 17 tourist attractions, five lodging establishments, four food centers, and one health center. On the other hand, route two includes 11 tourist attractions, two lodging and food establishments, and one health center. The final contribution of this research is to maximize tour satisfaction by presenting new routes of visiting tourist attractions due to the growing demand in the Chimborazo Reserve.
Conference Paper
Full-text available
Scalability, standardization, and management are important issues when working with very large Volunteered Geographic Information (VGI). VGI is a rich and valuable source of Points of Interest (POI) information, but its inherent heterogeneity in content, structure, and scale across sources present major challenges for interlinking data sources for analysis. To be useful at scale, the raw information needs to be transformed into a standardized schema that can be easily and reliably used by data analysts. In this work, we tackle the problem of unifying POI categories (e.g. restaurants, temple, and hotel) across multiple data sources to aid in improving land use maps and population distribution estimation as well as support data analysts wishing to fuse multiple data sources with the OpenStreetMap (OSM) mapping platform or working with projects that are already configured in the OSM schema and wish to add additional sources of information. Graph theory and its implementation through the SONET graph database, provides a programmatic way to organize, store, and retrieve standardized POI categories at multiple levels of abstraction. Additionally, it addresses category heterogeneity across data sources by standardizing and managing categories in a way that makes cross-domain analysis possible.
Conference Paper
Full-text available
This paper presents a system designed to utilize innovative spatial interconnection technologies for sites and events of environmental, cultural and tourist interests. The system will discover and consolidate semantic information from multiple sources, providing the end-user the ability to organize and implement integrated and enhanced tours. The system, called e-xnilatis, will extend existing innovative techniques, including: semi-automated searching and extraction of real-time knowledge from online resources, automated discovery of points of interest as well as events, semantic integration, classification and hierarchy of information from various sources, spatial representation of content, personalized user experience and Augmented Reality (AR) for the interconnection of the digital with the natural environment. A complex online platform and applications for smart devices will be developed so that the user manages and receives the optimum route information along with AR experience when applicable. The platform will be an open architecture tool that, with the appropriate time-space constraints, will be able to create adaptive content. A typical example of this is the Egnatia motorway, where the e-xnilatis platform will be evaluated.
Conference Paper
Full-text available
A tourist has time and budget limitations; hence, he needs to select points of interest (POIs) optimally. Since the available information about POIs is overloading, it is difficult for a tourist to select the most appreciate ones considering preferences. In this paper, a new travel recommender system is proposed to overcome information overload problem. A recommender system (RS) evaluates the overwhelming number of POIs and provides personalized recommendations to users based on their preferences. A content-based recommendation system is proposed, which uses the information about the user’s preferences and POIs and calculates a degree of similarity between them. It selects POIs, which have highest similarity with the user’s preferences. The proposed content-based recommender system is enhanced using the ontological information about tourism domain to represent both the user profile and the recommendable POIs. The proposed ontology-based recommendation process is performed in three steps including: ontology-based content analyzer, ontology-based profile learner, and ontology-based filtering component. User’s feedback adapts the user’s preferences using Spreading Activation (SA) strategy. It shows the proposed recommender system is effective and improves the overall performance of the traditional content-based recommender systems.
Full-text available
Geospatial data sharing is an increasingly important subject as large amount of data is produced by variety of sources, stored in incompatible formats, and accessible through different GIS applications. Past efforts to enable sharing have produced standardized data format such as GML and data access protocols such as Web Feature Service (WFS). While these standards help enabling client applications to gain access to heterogeneous data stored in different formats from diverse sources, the usability of the access is limited due to the lack of data semantics encoded in the WFS feature types. Past research has used on-tology languages to describe the semantics of geospatial data but ontology-based queries cannot be applied directly to legacy data stored in databases or shape-files, or to feature data in WFS services. This paper presents a method to enable ontology query on spatial data available from WFS services and on data stored in databases. We do not create ontology instances explicitly and thus avoid the problems of data replication. Instead, user queries are rewritten to WFS getFea-ture requests and SQL queries to database. The method also has the benefits of being able to utilize existing tools of databases, WFS, and GML while enabling query based on ontology semantics.
Public awareness of and concerns about companies’ social and environmental impacts have seen a marked increase over recent decades. In parallel, the quantity of relevant information has increased, as states pass laws requiring certain forms of reporting, researchers investigate companies’ performance, and companies themselves seek to gain a competitive advantage by being seen to operate fairly and transparently. However, this information is typically dispersed and non-standardized, making it complicated to collect and analyze. To address this challenge, the WikiRate platform aims to collect this information and store it in a standardized format within a centralized public repository, making it much more amenable to analysis. In the context of WikiRate, this article introduces easIE, an easy-to-use information extraction (IE) framework that leverages general Web IE principles for building datasets with environmental, social, and governance information from the Web. To demonstrate the flexibility and value of easIE, we built a large-scale corporate social responsibility database comprising 654,491 metrics related to 49,009 companies spending less than 16 hours for data engineering, collection, and indexing. Finally, a data collection exercise involving 12 subjects was performed to showcase the ease of use of the developed framework.
Conflating multiple geospatial data sets into a single dataset is challenging. It requires resolving spatial and aspatial attribute conflicts between source data sets so the best value can be retained and duplicate features removed. Domain experts are able to conflate data using manual comparison techniques, but the task it is labour intensive when dealing with large data sets. This paper demonstrates how semantic technologies can be used to automate the geospatial data conflation process by showcasing how three Points of Interest (POI) data sets can be conflated into a single data set. First, an ontology is generated based on a multipurpose POI data model. Then the disparate source formats are transformed into the RDF format and linked to the designed POI Ontology during the conversion. When doing format transformations, SWRL rules take advantage of the relationships specified in the ontology to convert attribute data from different schemas to the same attribute granularity level. Finally, a chain of SWRL rules are used to replicate human logic and reasoning in the filtering process to find matched POIs and in the reasoning process to automatically make decisions where there is a conflict between attribute values. A conflated POI dataset reduces duplicates and improves the accuracy and confidence of POIs thus increasing the ability of emergency services agencies to respond quickly and correctly to emergency callouts where times are critical.
Cultural heritage consists of heterogeneous resources: archaeological artefacts, monuments, sites, landscapes, paintings, photos, books and expressions of human creativity, often enjoyed in different forms: tangible, intangible or digital. Each resource is usually documented, conserved and managed by cultural institutes like museums, libraries or holders of archives. These institutes make available a detailed description of the objects as catalog records. In this context, the chapter proposes both a classification of cultural heritage data types and a process for cultural heritage valorisation through the well-known Linked Open Data paradigm. The classification and process have been defined in the context of a collaboration between the Semantic Technology Laboratory of the National Research Council (STLab) and the Italian Ministry of Cultural Heritage and Activities and Tourism (MIBACT) that the chapter describes, although we claim they are sufficiently general to be adopted in every cultural heritage scenario. In particular, the chapter introduces both a suite of ontology modules named Cultural-ON to model the principal elements identified in the cultural heritage data type classification, and the process we employed for data valorisation purposes. To this end, semantic technologies are exploited; that is, technologies that allow us to conceptualise and describe the meaning of data forming the cultural heritage and including such entities as places, institutions, cultural heritage events, availability, etc. These entities have special characteristics and are connected with each other in a profound way. 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. By navigating the semantic relationships between the various objects of the knowledge base, new semantic paths can be revealed and utilised with the aim to develop innovative services and applications. The process is compliant with Linked Open Data and W3C Semantic Web best practices so that to enable a wider promotion of cultural heritage, and of sharing and reuse of cultural heritage data in the Web. The chapter concludes presenting a number of methodological principles and lessons learnt from the STLab/MIBACT collaboration that are applicable to any cultural heritage context and, in some cases, also to other domains.
Conference Paper
The paper presents work on implementation of semantic technologies within a geospatial environment to provide a common base for further semantic interpretation. The work adds on the current works in similar areas where priorities are more on spatial data integration. We assert that having a common unified semantic view on heterogeneous datasets provides a dimension that allows us to extend beyond conventional concepts of searchability, reusability, composability and interoperability of digital geospatial data. It provides contextual understanding on geodata that will enhance effective interpretations through possible reasoning capabilities. We highlight this through use cases in disaster management and planned land use that are significantly different. This paper illustrates the work that firstly follows existing Semantic Web standards when dealing with vector geodata and secondly extends current standards when dealing with raster geodata and more advanced geospatial operations.