=Paper=
{{Paper
|id=Vol-2412/paper3
|storemode=property
|title=Personalized Artistic Tour Using Semantic Web Technologies
|pdfUrl=https://ceur-ws.org/Vol-2412/paper3.pdf
|volume=Vol-2412
|authors=Yannis Christodoulou,Markos Konstantakis,Efthymia Moraitou,John Aliprantis,George Caridakis
|dblpUrl=https://dblp.org/rec/conf/smap/ChristodoulouKM19
}}
==Personalized Artistic Tour Using Semantic Web Technologies==
https://ceur-ws.org/Vol-2412/paper3.pdf
Personalized Artistic Tour using Semantic
Web Technologies
Yannis Christodoulou, Markos Konstantakis, Efthymia Moraitou, John Aliprantis
and George Caridakis
University of the Aegean, Mytilene, Greece
Department of Cultural Technology and Communication
Intelligent Interaction Research Group
ii.aegean.gr
mkonstadakis@aegean.gr, yannischris@gmail.com,
e.moraitou@aegean.gr, ialiprantis@aegean.gr, gcari@aegean.gr
Abstract. Based on current trends in the domain of Cultural Heritage promotion, visitors
seek to engage in exceptional and unique experiences beyond established visiting practices.
Meanwhile, the latest developments in information and communication facilitate access to cul-
tural databases and repositories, bringing out the potential of new cultural products and services.
In this direction, a variety of typologies and visitor categorizations have been developed that
however do not take into account the complexity of visitors’ demands and motivations, or that
visitors tend to experience a journey based on new technologies and social media. Semantic Web
technologies could be the key for designing personalized services, among other things, facilitat-
ing data interoperability in different repositories, making possible the correlation of data with
different visitor profiles. In this context, Intelligent Interaction research group works towards an
innovative approach that will enrich and enhance the experience of the modern cultural visitor.
Keywords: Personalization, Semantic Web, Linked Open Data, User eXperi-
ence (UX).
1 Introduction
Preservation and promotion of Cultural Heritage (CH) can be greatly enhanced by im-
plementing efficient methods for collection, storage and processing of cultural data.
Remarkably increasing amounts of CH data regarding artefacts and collections hosted
in GLAMs (Galleries, Libraries, Archives and Museums) are stored in online reposito-
ries around the world, while a big part of this knowledge is open and accessible to
researchers, visitors and developers of cultural applications. However, information di-
rectly or indirectly related with a cultural artefact often lies scattered in multiple repos-
itories. Therefore, it is important, not only for the scientific community but also for the
general public, to share knowledge, which eventually will benefit researchers, profes-
sionals, as well as visitors, while offering a more complete understanding of the arte-
fact/collection in question.
In recent years, significant efforts have been made to integrate CH knowledge stored in
different repositories using Semantic Web technologies. In this direction, structured
data and knowledge models have been used to define rules for storing, querying and
analyzing data. The term Linked Open Data (LOD), proposed by the inventor of the
World Wide Web Tim Berners-Lee in 2006, refers to data that are published and linked
Cultural Informatics 2019, June 9, 2019, Larnaca, Cyprus. Copyright held by the
author(s).
according to specific rules for linking structured metadata on the Web, in a way that
their meaning is explicitly defined through formal semantic models in order to become
machine-processable. Through LOD, datasets are linked to external data sets, and can
in turn be linked to other external data sets (Bizer et al., 2011). Interlinked datasets (in
other words, the LOD cloud) integrate, complement and expand the scattered infor-
mation, thereby bridging repositories of different organizations and possibly of differ-
ent geographic location. By adopting LOD techniques, a significant amount of remote
knowledge is now available in a structured form allowing full and global access to val-
uable information.
Intelligent Interaction (II - http://ii.aegean.gr/) research group, established in 2016, is
active in the areas of Semantic Web technologies, User eXperience and Personalization
methods, Intelligent Systems and Cultural Heritage Management, and it has partici-
pated in national and european conferences with publications in reputable scientific
journals in the respective research fields. Taking into consideration the urgent need for
a common interpretation and management of CH knowledge and data, the II research
group aims to propose new methods and techniques for classifying, preserving and pro-
moting the CH knowledge domain, by exploiting Semantic Web technologies and
Linked Open Data techniques. A milestone step towards this direction, which becomes
possible by adopting these technologies, is disambiguating the definitions of concepts
and relationships belonging to different sub-fields and activities of the broader CH do-
main. A conceptual knowledge model, integrated with reasoning mechanisms, can cap-
ture and highlight significant correlations between semantically represented data,
thereby contributing to information retrieval efficiency, optimal presentation of re-
trieved information, decision-making support, and eventually to a common understand-
ing of the underlying knowledge on behalf of scientists, researchers as well as common
users.
Drawing on the above and being motivated by the broader vision of interconnected
global knowledge, the II research group investigates methods for efficient dissemina-
tion of information and knowledge by addressing individual user needs, eventually
leading to a better overall Cultural User eXperience (CUX). As such, this work focuses
on investigating noel methods for presenting personalized information to the visitor,
taking into account individual characteristics such as artistic background, artistic but
also wider interests, as well as environmental elements describing the context of inter-
action (context awareness) (Konstantakis, 2018; Antoniou, 2016). More specifically,
the presented research investigates ways of making personalized recommendations to
the visitor in the form of cultural paths, i.e., sets of visiting points of cultural interest
(PoCI) that in conjunction formulate a cultural narrative, tailored to the visitor’s cultural
background and interests. Examples of PoCIs include (but are not limited to) a partic-
ular visual artwork in a museum, an art collection or an entire cultural venue.
2 Technological issues
3
As our work so far indicates, the idea that Semantic Web technologies can be vital
in defining personalized cultural paths is based on two factors: i) semantic technologies
ensure data interoperability and interconnection between different online data sources
(repositories), ii) semantic technologies facilitate the correlation of data with different
visitor profiles. Combining personalization techniques with semantic technologies in
the context of Cultural Heritage (CH) can lead to more effective presentation of cultural
content, through semantic modeling of user profiles and correlation with semantically-
enabled cultural data, using a reasoning mechanism. In this respect, our main goal is to
study and analyze the different issues that this approach may address in order to max-
imize CUX experience through semantic technologies combined with LOD and per-
sonalization methods (Deladiennee, 2017).
To begin with, personalization is the ability of a system to adapt its interface to dif-
ferent user profiles and requirements in order to satisfy particular needs, based on per-
sonal information. As depicted in Figure 1, the information may be provided either
explicitly by the user, or implicitly by monitoring user actions (Antoniou, 2016; Bowen,
2004). In case a system requires explicitly provided information, users have to submit
information about personal interests and preferences, usually by filling in surveys. On
the other hand, implicit data collection doesn’t require interaction with users, who often
do not realize that the displayed content is tailored to their interests, since the system
extracts their preferences from monitored interaction (e.g., web usage mining, cookies,
collaborative filtering, accessing by search) (Kuusik, 2009).
Figure 1. User profiling techniques
Explicit provision of user profiles may be achieved with the use of predefined user
profiles. According to (Falk, 2006; Morris, Hargreaves & McIntyre, 2004), there are
four different modes of visitor behavior in CHI, especially when engaging with the
exhibits: ‘browsers’, ‘followers’, ‘searchers’ and ‘researchers’. The different visitor
types may prefer different types of information presentation; as such, different technol-
ogies may have to be adopted to accommodate their preferences. Additionally, (Walsh,
Clough & Foster, 2016) have identified different categories of users of Digital Cultural
Heritage (DCH) systems and services. The authors suggest that it may be more efficient
to categorize users by expertise, rather than by label or user type. Alternatively, some
combination of multiple criteria could be applied. However, predefined user profiles
may not correspond well to every visitor, failing to capture current user needs and
expectations. Furthermore, user profiles are usually created at the beginning of a visit
when visitors are usually more reluctant to carry out form-filling activities (Konstan-
takis, 2017). Therefore, those methods are helpful though not always effective and ap-
plicable.
Indicative methods of capturing visitor behavior may include recording i) visiting
history at different cultural spaces and GLAM’s (Konstantakis, 2017), ii) visitor’s be-
havior and preferences based on user-generated content in social media (social data
mining analysis), iii) visitor’s activity while moving within a particular cultural space.
These methods could be expanded to include a wider frame of interaction that is inher-
ent to the user’s cultural experience. Behavior recording can then be utilized for rec-
ommending exhibits that correspond to the visitor’s interests, experiences and
knowledge background. However, defining accurate visitor personas remains a chal-
lenging issue, since it requires to rely on alternative sources for retrieving user infor-
mation in order to limit the visitor’s distraction from their cultural experience to the
minimum.
A similar issue emerges when a visitor uses a system for the first time. In such cases,
the system will most likely fail to effectively recommend content to the user. This prob-
lem is commonly known as cold start and is a common issue in recommendation sys-
tems. Many solutions and methods have been proposed to address the cold start issue.
Common recommendation strategies are based on association rules and clustering tech-
niques (Sobhanam, 2013), social information (Zhang et al., 2010; Noor & Martinez,
2009), ontological classification of knowledge (Noor & Martinez, 2009) and hybrid
user modelling (Wang et al., 2008). Particularly in the CH domain, multiple methods
have been proposed to address the user profiling and classification task, aiming to im-
prove the overall UX. A rather common technique is classifying users under persona
profiles based on replies to multiple-choice queries.
Regarding the utilization of LOD techniques, there are also some challenging issues
that require attention. Information exchange between different data aggregators still
suffers from significant flows, often due to lack of heterogeneity/interoperability in data
mapping, as well as redundancy of cataloging rules and standards. In the CH domain,
the significant heterogeneity degree of cultural information makes it challenging to
achieve syntactic, structural and, more importantly, semantic interoperability between
remote datasets and databases. Another relevant issue is the one widely known as the
semantic gap (Freitas et al., 2012). Often, critical differences can be found between the
user’s informational needs expressed in a natural-language query and the underlying
data representation of the targeted dataset. Therefore, creating a unifying global
knowledge model of the broader CH domain, by creating and exploiting as many linked
data as possible, remains an issue to date.
Defining effective user profiles is a complicated and dynamic process. In this re-
spect, semantic modeling of user profiles and requirements can offer valuable aid. Data
provided within the LOD cloud are structured using standard rules and common seman-
tics. By utilizing formal ontologies to describe concepts related to user profiling, we
can achieve a deeper, complete and more structured representation of user features,
which in turn can lead to a more efficient interpretation of the user’s informational
5
needs (Di Noia & Ostuni, 2015), and by extension to effective recommendations. Bib-
liographic research has shown that there have been several attempts to conceptually
model the broader knowledge set that synthesizes the concept of user profile (Niaraki,
2009; Pretschner, 1999; Sieg, 2007; Skillen, 2012; Trajkova, 2004; Weißenberg, 2006;
Zhou, 2006; He, 2016), although relevant research in the context of identifying the user
as a visitor remains limited. Nonetheless, there are still issues to be addressed when
combining the aforementioned technologies and techniques (Semantic Web, personal-
ization, LOD) in order to offer personalized services to groups of users, for example,
performance issues when required to efficiently and timely handle large volumes of
users and content, let alone if the user information (e.g., preferences, needs, require-
ments) is constantly changing.
3 Open Challenges
Taking into account the aforementioned issues, we aim to provide the visitor with
rich and personalized cultural information, towards optimizing their overall cultural
experience. In particular, we propose the implementation and recommendation of per-
sonalized cultural paths, as described in Introduction. Selecting cultural points of inter-
est in order to form a cultural path can be based on thematic, conceptual or spatial
relevance, or some combination of the above, and always in juxtaposition with the vis-
itor’s cultural profile.
While both explicit and implicit methods for providing user information focus on
user profile modeling for presenting data in a personalized fashion, it is not yet clear
which method is bound to provide the most satisfying results. Based on our previous
work (Konstantakis, 2018), we argue that some combination of implicit and explicit
collection of data can be proved more efficient, especially when intending to minimize
distraction in UX. Additionally, we recommend the use of formal ontologies and re-
lated semantic technologies for creating conceptual schemata that incorporate semantic
reasoning mechanisms, through which novel information and conceptual interrelations
can be generated (Kadima, 2010; Golemati, 2007). Finally, integrating LOD in person-
alized cultural paths will greatly enhance access to available cultural information from
multiple data repositories, enabling useful semantic interrelations between information
hosted in remote repositories and aggregators, such as EUROPEANA Digital Library
(Europeana Foundation, 2018), SEARCH CULTURE Greek aggregator (Georgiadis,
2016) .
In conclusion, given that personalized cultural and art services are far from being
characterized as saturated, as well as there is no online service offering personalized
recommendations to visitors of a cultural site based on personal preferences in the form
of a single narrative, our research aims to analyze, comprehend and eventually match
the visitor’s profile and preferences with available cultural content, thereby improving
their overall UX.
Acknowledgements
The research and writing of this paper were financially supported by the General
Secretariat for Research and Technology (GSRT) and the Hellenic Foundation for Re-
search and Innovation (HFRI). John Aliprantis has been awarded with a scholarship for
his PhD research from the “1st Call for PhD Scholarships by HFRI” – “Grant Code
234”.
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