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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 cultural 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, facilitating 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.
Personalized Cultural Tours using Semantic Web Technologies
Yannis Christodoulou1, Markos Konstantakis1, Efthymia Moraitou1, John Aliprantis1 and George Caridakis1
1University of the Aegean, Mytilene, Greece
Department of Cultural Technology and Communication
Intelligent Interaction Research Group
ii.aegean.gr
yannischris@gmail.com, mkonstadakis@aegean.gr, e.moraitou@aegean.gr,
jalip@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 a nd communication
facilitate access to cultural 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,
facilitating 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 t hat w ill enrich and
enhance the experience of the modern cultural visitor.
Keywords: Personalization, Semantic Web, Web Ontologies, Linked Open Data, User eXperience (UX).
1 Introduction
Preservation and promotion of Cultural Heritage (CH) can be greatly enhanced by implementing efficient
methods for collection, storage and processing of cultural data and metadata derived from processes related to
its study, interpretation and conservation. Remarkably increasing amounts of CH data regarding artefacts and
collections hosted in GLAMs (Galleries, Libraries, Archives and Museums) are stored in online repositories
around the world, while a big part of this knowledge is open and accessible to researchers, visitors and
developers of cultural applications. However, information directly or closely related with a cultural artefact
often lies scattered in multiple repositories. Therefore, it is important, not only for the scientific community but
also for the general public, to share knowledge that may eventually benefit researchers, professionals, as well as
visitors, while offering a more complete understanding of the artefact itself.
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
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 by other external data sets (Bizer et al., 2011). Interlinked
datasets (in other words, the LOD cloud) integrate, complement, illustrate and expand the underlying scattered
information, thereby bridging at the informational level repositories of different organizations and possibly
different 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 valuable 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 participated 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 information, the II research group aims to propose new methods
and techniques for classifying, preserving and promoting 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 domain. A conceptual knowledge model
integrated with reasoning mechanisms can capture and highlight interesting correlations between semantically
represented data, thereby contributing to information retrieval efficiency, optimal presentation of retrieved
information, decision-making support, and eventually to a common understanding of the underlying knowledge
on behalf of scientists 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 dissemination 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 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 interaction (context awareness) (Konstantakis, 2018; Antoniou, 2016). Taking one further step in the same
direction, 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 particular visual artwork in a museum, an art collection or an entire cultural venue.
2 Technological Ιssues
As our work so far indicates, the idea that Semantic Web technologies can be vital in defining personalized
cultural paths is based on two basic 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
reasoning mechanisms. In this respect, our main goal is to study and analyze the different issues that this
approach may address in order to maximize CUX experience through semantic technologies combined with
LOD and personalization methods (Deladiennee, 2017).
To begin with, personalization is the ability of a system to adapt its interface to different user profiles and
requirements in order to satisfy their needs, based on personal information. The information may be provided
either explicitly by the user, or implicitly by monitoring the user's actions (Antoniou, 2016; Bowen, 2004). In
case a system requires explicitly provided information, users have to submit information about their personal
interests and preferences, usually by filling in surveys, as shown in Figure 1. On the other hand, implicit data
collection doesn’t require interaction with users, who they 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, and as such different technologies may have
to be adopted to accommodate their preference. Additionally, (Walsh, Clough & Foster, 2016) identify different
ways in which users of Digital Cultural Heritage (DCH) systems and services have been categorized. 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 (Konstantakis, 2017). Therefore, those methods are helpful though not always effective and
applicable.
Indicative methods of capturing visitor behavior may include recording i) visiting history in different cultural
spaces and GLAM’s (Konstantakis, 2017), ii) visitor’s behavior and preferences based on user-generated
content in social media (social data mining analysis), iii) visitor’s activity while moving within a particular
cultural area. The above methods could be expanded to include a wider context of interaction that is inherent to
the user’s cultural experience. Behavior recording can then be utilized for recommending exhibits that
correspond to the visitor’s interests, experiences and knowledge background. However, defining accurate visitor
personas remains a challenging issue, since it requires to rely on alternative sources for retrieving user
information 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 problem is known as cold start and is a common issue
among recommendation systems. Many solutions and methods have been proposed to address the cold start
issue. Common recommendation strategies are based on association rules and clustering techniques (Sobhanam,
2013), social information (Zhang et al., 2010; Noor & Martinez, 2009), ontological knowledge classification
(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 at a better
overall UX. A rather common technique is sorting users into 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 related to
lack of interoperability such as data heterogeneity in data/metadata mapping and multiplicity of cataloging rules
and standards. In our case, the significant heterogeneity degree of cultural information makes it challenging to
achieve syntactic, structural and, more importantly, semantic interoperability between remote datasets or
databases. Another related 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 broad 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 respect, semantic modeling of user
profiles and requirements can offer a valuable assistance. Data provided within the LOD cloud are structured
using standard rules and common semantics. By utilizing Web 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 effective interpretation of the user’s informational needs (Di Noia & Ostuni, 2015).
Bibliographic 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, personalization, LOD) 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,
requirements) 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 personalized cultural paths, as described in Introduction. Selecting cultural points of
interest 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 visitor’s 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 data collection seems to be more efficient, especially when intending to minimize distraction in
UX. Additionally, we consider the use of Web Ontologies and related 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
personalized cultural paths will greatly enhance the available cultural information by encompassing multiple
cultural data repositories, paving the way to useful semantic interrelations between remote information hosted in
major cultural data repositories and aggregators, such as the European Aggregator EUROPEANA, the Greek
Aggregator SEARCH CULTURE and the platform WITH.
In conclusion, given that personalized cultural and arts services are far from being characterized as saturated, as
well as there is no online service offering personalized recommendations to visitors of a site based on personal
preferences in the form of a single narrative, our research aims to analyze, comprehend and eventually satisfy
the need to match the visitor’s profile and preferences with available cultural options.
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 Research 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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... Standard recommendation strategies are based on association rules and clustering techniques [23], social information [24], ontological classification of knowledge [25] and hybrid user modelling [26]. Multiple methods have been proposed to address the user profiling and classification task, particularly in the CH domain, improving the overall UX. ...
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