=Paper=
{{Paper
|id=None
|storemode=property
|title=Supporting Integrated Tourism Services with Semantic Technologies and Machine Learning
|pdfUrl=https://ceur-ws.org/Vol-1272/paper_98.pdf
|volume=Vol-1272
|dblpUrl=https://dblp.org/rec/conf/semweb/LisiE14
}}
==Supporting Integrated Tourism Services with Semantic Technologies and Machine Learning==
https://ceur-ws.org/Vol-1272/paper_98.pdf
Supporting Integrated Tourism Services with
Semantic Technologies and Machine Learning
Francesca A. Lisi and Floriana Esposito
Dipartimento di Informatica, Università degli Studi di Bari “Aldo Moro”, Italy
{francesca.lisi,floriana.esposito}@uniba.it
Abstract. In this paper we report our ongoing work on the application
of semantic technologies and machine learning to Integrated Tourism in
the Apulia Region, Italy, within the Puglia@Service project.
1 Introduction
Integrated Tourism can be defined as the kind of tourism which is explicitly linked
to the localities in which it takes place and, in practical terms, has clear connec-
tions with local resources, activities, products, production and service industries,
and a participatory local community. Integrated Tourism thus needs ICTs that
should go beyond the mere technological support for tourism marketing, differ-
ently from most approaches in eTourism research (see [1] for a comprehensive
yet not very recent review). In this paper, we report our experience in support-
ing Integrated Tourism services with Semantic Technologies (STs) and Machine
Learning (ML). The work has been conducted within Puglia@Service,1 an Italian
PON Research & Competitivity project aimed at creating an innovative service
infrastructure for the Apulia Region, Italy.
The paper is structured as follows. Section 2.1 shortly describes a domain
ontology for Integrated Tourism, named OnTourism, which has been modeled
for being used in Puglia@Service. Section 2.2 briefly presents a Web Informa-
tion Extraction (WIE) tool, named WIE-OnTour, which has been developed
for populating OnTourism with data automatically retrieved from the Web. Sec-
tion 2.3 illustrates some of the Semantic Web Services (SWSes) which have been
defined on top of OnTourism for supporting Integrated Tourism in Apulia. Sec-
tion 3 outlines an application scenario for a ML tool, named Foil-DL, to better
adapt the automated composition of these services to user demands. Section 4
concludes the paper with final remarks and directions of future work.
2 Semantic Technologies for Integrated Tourism
2.1 A Domain Ontology
Domain ontologies for tourism are already available, e.g. the travel 2 ontology is
centered around the concept of Destination. However, it is not fully satisfactory
1
http://www.ponrec.it/open-data/progetti/scheda-progetto?ProgettoID=5807
2
http://www.protege.cim3.net/file/pub/ontologies/travel/travel.owl
from the viewpoint of Integrated Tourism because, e.g., it lacks concepts mod-
eling the reachability of places. In Puglia@Service, we have decided to build a
domain ontology, named OnTourism, 3 more suitable for the project objectives
and compliant with the OWL 2 standard. It consists of 379 axioms, 205 logical
axioms, 117 classes, 9 object properties, and 14 data properties, and has the
expressivity of the DL ALCOF(D).
The main classes of the terminology are Site, Place and Distance. The first
is the root of a taxonomy which covers several types of sites of interest (e.g.,
Hotel and Church). The second models the places where sites are located at. The
third, together with the object properties hasDistance and isDistanceFor and
the data properties hasLengthValue/hasTimeValue, allows to represent the dis-
tance relation between sites with values in either length or time units. Distances
are further specified according to the transportation means used (see, e.g., the
class Distance on Foot). Other relevant classes in the terminology are Amenity
(with subclasses such as Wheelchair Access) and Service (with subclasses such as
Bike Rental ) that model, respectively, amenities and services available at the ac-
commodations. Finally, the terminology includes the official 5-star classification
system for hotel ranking.
2.2 Ontology Population with Web Information Extraction
WIE-OnTour is a wrapper-based WIE tool implemented in Java and conceived
for the population of OnTourism with data concerning hotels and B&Bs available
in the web site of TripAdvisor4 . The tool is also able to compute distances of
the extracted accommodations from sites of interest (e.g., touristic attractions)
by means of the Google Maps5 API. Finally, the tool supports the user in the
specification of sites of interest.
Instantiations of OnTourism for the main destinations of urban tourism in
Apulia have been obtained with WIE-OnTour. Here, we consider an instan-
tiation for the city of Bari (the capital town of Apulia). It contains 34 hotels,
70 B&Bs, 106 places, and 208 foot distances for a total of 440 individuals. The
distances are provided in time and length on foot and have been computed with
respect to Basilica di San Nicola and Cattedrale di San Sabino (both instances
of Church and located in Bari). The restriction to foot distances is due to the
aforementioned preference of Integrated Tourism for eco-mobility.
2.3 Semantic Web Services
In Puglia@Service, we have defined several atomic services in OWL-S on top
of the aforementioned domain ontologies, travel and OnTourism. For example,
city churches service returns the churches (o.p. of type Church) located in a given
city (i.p. of type City) whereas near attraction accomodations service returns all
the accommodations (o.p. of type Accommodation) near a given attraction (i.p.
3
http://www.di.uniba.it/~lisi/ontologies/OnTourism.owl
4
http://www.tripadvisor.com/
5
http://maps.google.com/
of type Attraction). Note that closeness can be defined on the basis of distance
either in a crisp way (i.e., when the distance value is under a fixed threshold)
or in a fuzzy way (i.e., through grades of closeness). In both ways, however,
the computation should consider the transportation means used as well as the
measure units adopted according to the OnTourism ontology.
In Puglia@Service, we intend to obtain composite services by applying meth-
ods such as [3]. For example, the sequence composed of city churches service
and near attraction accomodations service could satisfy, e.g., the user request for
accommodations around Basilica di San Nicola. Indeed, since Bari is a major
destination of religious tourism in Apulia, it could effectively support the de-
mand from pilgrims who prefer to find an accommodation in the neighborhood
of places of worship so that they can practise their own religions at any hour of
the day. Also, if the suggested accommodations are easy to reach (i.e., at foot
distance) from the site of interest, the service will bring benefit also to the city,
by reducing the car traffic. In a more complex scenario, disabled pilgrims might
need a wheelchair-accessible accommodation. The service composition mecha-
nism should then append also wheelchairaccess accommodations service, so that
the resulting composite service could be considered more compatible with the
special needs of this user profile.
3 Towards Learning from Users’ Feedback
In Puglia@Service, automated service composition will be enhanced by exploiting
users’ feedback. The idea is to apply ML tools in order to induce ontology ax-
ioms which can be used for discarding those compositions that do not reflect the
preferences/expectations/needs of a certain user profile. Here, we illustrate this
idea with an application scenario which builds upon the accommodation rating
provided by TripAdvisor’s users. More precisely, we consider the task of accom-
modation finding. This task strongly relies on a classification problem aimed at
distinguishing good accommodations from bad ones according to the amenities
available, the services offered, the location and the distance from sites of interest,
etc. In order to address this classification problem, we need ML tools able to
deal with the inherent incompleteness of Web data and the inherent vagueness of
concepts such as the closeness. One such tool is Foil-DL [2], a ML system able
to induce a set of fuzzy General Concept Inclusion (GCI) EL(D) axioms from
positive and negative examples for a target class in any OWL ontology.
As an illustration of the potential usefulness of Foil-DL in the Puglia@Service
context, we report here a couple of experiments concerning the filtering of results
returned by the SWSes reported in the previous section for the case of Bari. We
set up a learning problem with the class Bad Accommodation as target of the
learning process. Ratings from TripAdvisor users have been exploited for provid-
ing Foil-DL with positive and negative examples. Out of the 104 accommoda-
tions, 57 with a higher percentage (say, over 0.7) of positive users’ feedback are
asserted as instances of Good Accommodation, whereas 15 with a lower percent-
age (say, under 0.5) are asserted as instances of Bad Accommodation. The latter,
of course, play the role of positive examples in our learning problem. Syntactic
restrictions are imposed on the form of the learnable GCI axioms.
In the first experiment, we have not considered the distances of the accom-
modations from the sites of interest. With this configuration, Foil-DL returns
just the following GCI with confidence 0.5:
Bed_and_Breakfast and hasAmenity some (Pets_Allowed) and hasAmenity some (Wheelchair_Access)
subclass of Bad_Accommodation
The GCI suggests that B&Bs are not recommended even though they provide
disabled facilities. It can be used to filter out from the result set of wheelchairac-
cess accommodations service those accommodations which are classified as bad.
In the second experiment, conversely, we have considered the distances of
the accommodations from the sites of interest. With this configuration, Foil-DL
returns the following GCI with confidence 1.0
hasAmenity some (Bar) and hasAmenity some (Wheelchair_Access) and
hasDistance some (isDistanceFor some (Bed_and_Breakfast) and isDistanceFor some (Church))
subclass of Bad_Accommodation
The GCI strenghtens the opinion that B&Bs are not recommendable accommo-
dations for disabled people whatever their distance from the churches is.
As a further experiment, we have restricted our analysis of accommodations
in Bari to only B&Bs. Starting from 12 positive examples and 39 negative ex-
amples for Bad Accommodation, Foil-DL returns the following two GCIs with
confidence 0.154 and 0.067 respectively:
hasAmenity some (Pets_Allowed) and hasAmenity some (Wheelchair_Access) subclass of Bad_Accommodation
hasAmenity some (Bar) and hasAmenity some (Wheelchair_Access) subclass of Bad_Accommodation
which confirm that B&Bs should not be recommended to disabled tourists.
4 Conclusions and future work
In this paper we have reported our ongoing work on the use of STs and ML
for Integrated Tourism in Apulia within the Puglia@Service project. Though
developed for the purposes of the project, the technical solutions here described
are nevertheless general enough to be reusable for similar applications in other
geographical contexts. Notably, they show the added value of having ontologies
and ontology reasoning (including also non-standard inferences like induction as
exemplified by Foil-DL) behind a Web Service infrastructure.
For the future we intend to carry on the work on the application of Foil-DL
to the automated service composition. Notably, we shall consider the problem of
learning from the feedback provided by specific user profiles.
References
1. Buhalis, D., Law, R.: Progress in information technology and tourism management:
20 years on and 10 years after the internet - the state of eTourism research. Tourism
Management 29(4), 609–623 (2008)
2. Lisi, F.A., Straccia, U.: A System for Learning GCI Axioms in Fuzzy Description
Logics. In: Eiter, T., Glimm, B., Kazakov, Y., Kroetzsch, M. (eds.) Proc. of the
26th Int. Workshop on Description Logics. CEUR Workshop Proceedings, vol. 1014.
CEUR-WS.org (2013)
3. Redavid, D., Iannone, L., Payne, T.R., Semeraro, G.: OWL-S atomic services com-
position with SWRL rules. In: An, A., Matwin, S., Ras, Z.W., Slezak, D. (eds.)
Foundations of Intelligent Systems. LNCS, vol. 4994, pp. 605–611. Springer (2008)