=Paper=
{{Paper
|id=Vol-2158/paper5
|storemode=property
|title=Smart Museum Information Services to Assist Preservation, Transmission and Research in Cultural and Historical Heritage Domain
|pdfUrl=https://ceur-ws.org/Vol-2158/paper5.pdf
|volume=Vol-2158
|authors=Dmitry Korzun,Svetlana Yalovitsyna,Valentina Volokhova
|dblpUrl=https://dblp.org/rec/conf/balt/KorzunYV18
}}
==Smart Museum Information Services to Assist Preservation, Transmission and Research in Cultural and Historical Heritage Domain==
https://ceur-ws.org/Vol-2158/paper5.pdf
Smart Museum Information Services to Assist
Preservation, Transmission and Research
in Cultural and Historical Heritage Domain
Dmitry Korzun1 , Svetlana Yalovitsyna2 , and Valentina Volokhova3
1
Department of Computer Science, Petrozavodsk State University,
Petrozavodsk, Russia
dkorzun@cs.karelia.ru
2
Institute of Linguistics, Literature and History,
Karelian Research Centre of Russian Academy of Science,
Petrozavodsk, Russia
jalov@yandex.ru
3
Department of Russian History, Petrozavodsk State University,
Petrozavodsk, Russia
vavolokhova@yandex.ru
Abstract. Museums are now digitally enhanced based on smart infor-
mation services that assist visitors and personnel. Cultural and historical
heritage (CHH) is perceived in a personalized and cognitive way and new
knowledge is created by the users. In this paper, we overview the smart
museum concept. We consider services for effective CHH preservation
and transmission within a smart museum. The services are extended to
assist in research by discovering information for applying human exper-
tise and reasoning knowledge. We introduce information representation
models as a semantic network where data mining is reduced to ranking
in the semantic network. As a case study we discuss the services for the
History Museum of Petrozavodsk State University.
Keywords: Cultural and historical heritage, Smart museum, Informa-
tion services, Digital assistance, Semantic network, Data mining
1 Introduction
Our society has entered into the digital era, and the term “smart” becomes
widespread in relation to modern technologies [3]. We limit our scope with smart
information and communication technology (ICT), which has multi-disciplinary
character, and its “smart” aspects are now emerging in many domains, including
social and cultural ones [7]. Cultural and historical heritage (CHH) is a domain
where new ICT and digital services have a special impact on people approach to
preservation, transmission, and research [5,6]. CHH entities can be considered in
terms of various properties; to name some examples: works of fine and applied
arts or folk crafts, archaeological, architectural, ethnographic or historical sites
Lupeikiene A., Matulevičius R., Vasilecas O. (eds.):
Baltic DB&IS 2018 Joint Proceedings of the Conference Forum and Doctoral Consortium.
Copyright © 2018 for this paper by the papers' authors. Copying permitted for private and academic purposes.
2
and complexes, samples of park art and landscape architecture, industrial, docu-
mental or audio-visual heritage, spoken tradition and language or literary values,
customs, rituals, celebrations and beliefs, music, songs and dances, culinary and
ethnological traditions, folk games and sports.
In this paper, we consider the smart museum concept based on our previ-
ous work [9,8,13]. We consider smart museum information services that can be
developed based on a semantic network interlinking the museum CHH collec-
tion, including knowledge acquired from visitors and museum personnel. The
semantic network enhances the existing collection operating with digital repre-
sentations of exhibits, descriptions of CHH-valued objects and facts as well as
with any available fragments of CHH knowledge. This network is subject to data
mining needed for selection of appropriate information as a result provided by
services. Our case study is the History Museum of Petrozavodsk State Univer-
sity (PetrSU) in respect to everyday life history; the museum provides the pilot
testbed to analyze the information services.
The rest of the paper is organized as follows. Section 2 overviews the recent
progress in smart museum development. Section 3 introduces our smart mu-
seum concept based on information services. Section 4 describes mathematical
methods that our appraoch uses for services development. Section 5 discusses
particular smart museum information services using the case study of the History
Museum of PetrSU. Finally, Section 6 concludes the paper.
2 Smart Museum Development
A lot of efforts have been made already in museum digitalization. Museum
databases store the information part of the collection to track all knowledge
related to and about the CHH objects (exhibits) and to ensure the long-term
safety and sustainability of those objects within the museum’s care. The basic
function is an electronic archive (catalogue) administered by museum personnel.
Its extensions lead to “smart services”, emphasizing a certain intelligence level
in service construction and delivery.
The recent ICT progress (including Internet of Things, IoT) supports de-
velopment of on-site personalized services for museum visitors. A visitor has a
personal mobile device (e.g., smartphone) accessing relevant information about
surrounding exhibits and in a personalized and cognitive way [2]. The infor-
mation flow goes from digital cultural heritage to visitors. SMARTMUSEUM
system [14] provides explanatory description and multimedia content associated
with individual objects. A museum exhibition can tell a story [18]. Objects for
a visitor to study can be recommended based on the user profile and context
information. Additional content about an object can be retrieved from the Web.
The on-site visit boundaries of CHH experience at the museum can be extended
to assist the visitors during pre-visit planning and to follow up with post visit
memories and reflections [10].
Museum information services support people to be involved into the process,
e.g., using feedback when visitors leave posts on exhibits (to read by other vis-
44
3
itors) or evaluate exhibits (collaborative activity). IoT-enabled location-based
services make possible shortening the information distance between objects in
cultural spaces and their visitors [5]. Physical or virtual CHH objects can in-
teract with people, environment, other objects, and transmitting the related
information to users through multimedia facilities. Services become oriented to
personalized recommendations when the system captures the event the visitor
studies a CHH object. A set of “close” objects can be selected, ranked, and
arranged to be provided to the visitor for subsequent study.
Visitors are rich sources of new information about CHH objects, which is
captured using annotations [1]. An added annotation enhances digital object
memory, when the object stores data about itself and links other objects [17]. In
a museum environment, object memory can store information about the prove-
nance of the artifact, about its history, and the flow of comments generated by
visitors while interacting with the artifact.
3 Smart Museum Information Services
Smart museum extends the information archive function. The latter becomes
supported with Internet-based services, acting as effective providers of CHH in-
formation. Mobile multimedia, ambient intelligence, machine vision, augmented
reality, and other IoT-related technologies lead to enhanced museum information
services for effective CHH preservation, transmission, and research.
3.1 Possible ICT Use in Smart Museum
The ICT use in a smart museum considers the following services that provide
visitors or personnel with information for assisting their activity.
Smart navigation: The museum visitors are notified (e.g., smarthones) about
the recent situation in exhibition rooms (e.g., occupancy level, absent exhibits,
or ongoing entertainments). The navigation become context-aware and subject
to effective and personalized decision-making, e.g., see [14].
Quality evaluation: Visitors and their activity form a rich source for qualita-
tive analytics on the museum expositions (e.g., most visited rooms, high-interest
exhibits and information, or popular routes). The museum monitors its own
function and evaluate its efficiency, so adapting and personalizing the service
provision to the user’s needs, e.g., see [11].
Multimodal interface: Information provision is augmented using context and
multimedia (e.g., visualization of interesting facts on a nearby screen, exhibit self-
storytelling, or 3D modeling). That is, the transmission efficiency of preserved
CHH knowledge to the visitor can be increased to the level similar to the personal
assistance by an expert, e.g., see [18].
Collective intelligence: Involving visitors to CHH preservation within their
museum activity (e.g., impression sharing, gamification, or collaborative esti-
mates). The museum collection is enriched by information provided by visitors
themselves, including knowledge created collaboratively. This property of social
networks extends the museum to a cyber-physical-socio system, e.g., see [2].
45
4
3.2 Basic Forms of Smart Museum Services
Smart museum information services have the following forms in respect to the
applicability needs in CHH domain.
Preservation (and knowledge promotion): The museum digitally preserves
information about the collected CHH entities, in addition to physical preserva-
tion [15]. The linked-oriented forms of CHH information preservation showed
their effectiveness in museums, e.g., see [14,12]. The collected information and
its stored interlinked knowledge is subject to promotion when various audiences
are advertized and involved to CHH knowledge consumption and use.
Modern history is a special interest example. A museum visitor can be a
participant of the event presented in the exhibition. People’s living memory is
sometimes the only or at least the most accessible source of information about the
events of unofficial history (family holidays and traditions, everyday household
practices, ordinary citizen opinion to the events of “great history”, etc.).
Transmission (and knowledge mastering): A museum environment provides
a space for education activity [2]. In addition to self-education when a person (or
group) individually makes CHH studies, enhanced education activity is possible,
which includes thematic training or even pedagogical interaction. This activity
needs context-aware information search for effective knowledge consumption and
use. In particular, CHH information is visualized on nearby screens, and the users
make interaction and storytelling with smart exhibits [18].
This form for CHH presentation makes the educational activity interesting
for new categories of visitors, especially children and youth. Representatives of
the elder generation also note that personal active participation in the selection
of information for visualization makes the visit to the museum more interesting.
The visitor concentrates more on the subjects of study and less on the supple-
mentary information search function.
Research (and knowledge enriching): A CHH researcher needs many infor-
mation facts to create a fused picture from them and to interpret the value of
this picture [9]. The initial information corpus is large, and the search cannot be
done manually. In the transmission above, an information fragment is found. In
the research, information fragments having potential knowledge are needed. The
particular knowledge is created by an expert and enriches the museum collection.
The search uses context relations between exhibits. Each individual exhibit
has fragmentary information. The whole picture of a CHH phenomenon can be
created when the relationship between individual facts is detected. Then new
hypotheses can be built and grounded conclusions are drawn.
3.3 Semantic Integration
The need of semantic integration of available CHH knowledge for creating smart
museum information services has been already understood [16,12]. It supports
creating new exhibitions, working with visitors on a personal or mini-group level,
contributing to the realization of their expectations.
46
5
A mediation layer is introduced for semantic integration where knowledge
is derived based on a distributed set of multiple data sources, including such
services as DBpedia [4] and other services for semantic publishing, enrichment,
search, and visualization [12,15]. We apply the semantic network model for inte-
gration of digital CHH content [6,9,13]. The smart museum environment includes
the following components.
1. Ontological model for structural representation of collected CHH exhibits as
well as their various descriptions and relations with other objects.
2. The wiki system to transform the semantics from the collected records to
the semantic network using experts and the ontological model.
3. Semantic data mining in the constructed semantic network to take into ac-
count existing relations between collected exhibits and other CHH objects.
An information service provides a search extend of the museum collection.
Several most appropriate information facts are found for a given problem. It is
close to the k-optimization approach (several top solutions are used).
4 Semantic Data Mining
A semantic network is created on top of the museum collection—descriptions
from the various information sources [9]. Formally, a semantic network is a di-
rected graph G = (V, L) consisting of nodes (vertices set V ) representing domain
objects and links (edges set L) representing semantic relations. The nodes corre-
spond to physical exhibits (digitally virtualized) and digital exhibits (e.g., elec-
tronic photos, scanned documents), associated events, persons, and other CHH
objects. The links in L reflect interrelation of the objects.
4.1 Semantic Network
The ontological model for the semantic network is defined by ontology O. First,
O describes a system of concepts {Ci }ni=1 (ontology classes) such that any par-
ticular node v ∈ V (ontology class object, instance or individual) belongs to
one or more concepts. Second, O describes the interlinking structure for L, i.e.,
between which concepts a relation can be and possible types of such relations.
The links represent the primary semantics. Third, O describes attributes that
v ∈ V and l ∈ L may have to reflect additional semantics (e.g., keywords).
Semantic network construction is implemented as a collective process [13]. On
the one hand, many nodes v are straightforwardly derived from existing museum
records (e.g., collected in the museum information system) or correspond to
descriptions available in various open sources (e.g., web pages or photos in the
Internet). On the other hand, for nodes v ∈ V the expert defines semantic
relations (i.e., links l ∈ L) and their attributes.
An information service needs to find k > 0 the most appropriate information
facts. A fact can be a node v ∈ V , a link l ∈ L, or a connected graph structure
s in G (e.g., a path from u to v can have CHH-valued interpretation for some
47
6
u, v ∈ V ). This data mining can be reduced to the ranking problem when rank
values rv ≥ 0 or rl ≥ 0 are associated with nodes or links. The higher rank
the better is appropriateness of the information. The rank of a connected graph
structure is calculated based on ranks of the composed nodes and links.
4.2 Ranking Models
We consider the following three classes of ranking methods [13]: 1) local ranking,
2) collaborative filtering, 3) structural ranking.
Local ranking: Two or more objects are analyzed for similarity based on their
content and overlapping of this content. In this case, the rank is computed in
respect to some fixed node u ∈ V and reflects distance of other nodes from u:
rv (u) = 1/ρ(u, v).
For instance, if u and v have sets Ku and Kv of annotating keywords then the
rank reflects the size of overlapping |Ku ∩ Kv |, i.e., the larger the number of
shared keywords the higher is the similarity. In particular, if u is the recent
exhibit that the visitor studies then the information service can provide the
highest rank nodes v1 , . . . , vk as recommendation for the subsequent study.
Collaborative filtering: This ranking model assumes that many users generate
opinions about each CHH object. The opinions are transformed to some commu-
nity based score rv∗ (normalized 0 ≤ rv∗ ≤ 1). Then the scores can be combined
with other ranking requirements. For instance,
∗ dv
rv = αrv + (1 − α) 1 − ,
maxw∈W dw
where W ⊂ V is nodes of potential interest for the visitor, dw > 0 is a physical
reachability metric for node w, 0 ≤ α ≤ 1 is a tradeoff parameter between com-
munity scores and reachability. In particular, if W is a set of points of interest for
the visitor and dw is the time for the visitor to reach w from the current location
then the information service can provide the highest rank nodes v1 , . . . , vk as
recommendation for the next object to study.
Structural ranking: This ranking model utilizes the connectivity properties of
the semantic network G, similarly as it happens in the well-known PageRank
algorithm for network analysis. For instance, node ranks ru for all u ∈ V can be
(0)
computed iteratively starting from some initial values ru :
X
ru(i+1) = α pvu rv(i) + (1 − α)πu ,
l=(u,v)∈L
where pvu is weight of the link l = (u, v), 0 ≤ α ≤ 1 is the damping factor
denoting the probability of following the connectivity structure of G, and π is
a jump probability vector for all u ∈ V . In particular, if pvu are relative weight
of CHH role of v to u then the information service can provide the highest rank
nodes v1 , . . . , vk as recommendation for the most CHH-valued nodes to study.
48
7
5 Case Study
The History Museum of Petrozavodsk State University (PetrSU) is a typical
small museum oriented to everyday life history [9]. More than 10 digital displays
of various sizes with changing images of photographs, documents, newspaper
articles from different eras of more than 75-year PetrSU history. Transformable
table makes it easy to change the Museum space, making it comfortable for
different forms of collaborative work activities. Some displays show video and
audio interviews with the teachers of the University in different years. Exhibits
presented on windows show everyday life history of teachers, researchers, and
students. Some exhibits, despite their advanced age, can be experienced directly
in the room. Old movies about PetrSU life in the 1970s and 1980s provide the
necessary cultural and historical atmosphere.
Let us consider the three basic information services that we developed for use
in this museum. Visual design and user interface details of the presented smart
museum information services were demonstrated in [13]. The generic role of the
smart museum services is summarized in Table 1.
Visit service: The service constructs a personalized exposition of recom-
mended exhibits for a visitor to study. Such a recommendation is a small set
of selected objects from the presented ones in the museum exhibition room.
This set VU is constructed from the available knowledge such that the set repre-
sents the most interesting facts for the particular visitor u or their group U . This
way, a visit program is constructed for a museum visitor before the visit. The
service is also responsible for program adaptation during the visit depending on
the preferences of the visitor and on the dynamically changing situation.
The Museum is a point from which the guest often starts introduction with
PetrSU. According to psychologists, the memorization of new information occurs
when the knowledge is associated with the existing one. The service discovers
facts in the biography of Museum visitors and intersects them with facts from
the University history or with persons who worked (working) in PetrSU. The ed-
ucational function becomes more effective, making an almost personal approach
to each visitor and improving the whole PetrSU image.
Table 1. The role of smart services in respect to museum collection
Service Visit service Exhibition service Enrichment service
Form
Preservation Global scale structure Local scale structureCommunity-based
collection
Transmission Thematic navigation Personalized Involving the
in CHH knowledge navigation in semantic community to the
collection neighborhood for given activity
exhibit
Research Structured global Focused local view on Community resources
view on CHH given fact within its utilization and
knowledge collection neighborhood knowledge analysis
49
8
Visit service uses exhibit ranks based on local ranking (although other rank-
ing models can be applied as well). In particular, rv = 1/ρ(U, v), where the
distance ρ(U, v) shows semantic similarity of users U to the exhibit v ∈ V . The
CHH interests of the user are included into the user profile and can be repre-
sented in the node u ∈ U in G. Objects in VU can be ordered into the visit
program (i.e., implementing smart navigation), which is in turn visualized on
the public screen in the museum room or on personal mobile devices of the users
(i.e., implementing multimodal interface).
Exhibition service: The service shows selected descriptions and visual infor-
mation about the studied exhibits on exhibition touch screens or on personal
mobile devices of the visitors. In fact, the service creates a kind of virtualization
when a physical exhibition is augmented with digital representation (i.e., imple-
menting multimodal interface). As in Visit service, Exhibition service acts as a
recommender since the screens show the recommended (most interesting) facts
derived from the available CHH knowledge for the current context and situation.
The service makes the Museum space more interesting and diverse. It attracts
the most numerous group of museum visitors—young people. The widespread
use of gadgets and effective extraction of information allow the student to use
the services to find information important for her/his study.
For recommendations local ranking can be used. In particular, rv = 1/ρ(u, v),
where the distance ρ(u, v) shows the relation level of information fact v to the
recently studied exhibit or fact u (for u, v ∈ V ). The recommendation assistance
is online (i.e., implementing smart navigation). An effective visualization can
employ the star graph model, where the internal node u has a small set of rays
(leaves) to show the recommended facts and their interest level (rank).
Enrichment service: The service supports modification (evolution) of the se-
mantic network by museum personnel and visitors (i.e., implementing collective
intelligence). A museum visitor can enrich descriptions of studied exhibits (e.g.,
adding annotations). A personal mobile device (e.g., smartphone) becomes a pri-
mary access tool for this service. First, annotation is useful when the visitor adds
descriptions about an object (e.g., facts from an eyewitness of the event), which
is particularly important in everyday life history. Second, visitors can make the
routine work on establishing known history-valued relations between objects.
The visitor adds some relation (together with its description), and museum per-
sonnel moderate the correctness and value.
The museum collection is for modern history, preserving the events of the
recent past and being constantly updated with modern exhibits. The collection
often provokes visitors’ own memories of the University life. The service captures
these memories and turns them into exhibits. Museum collection has many ex-
hibits for which information needs clarification: the date of creation of photos
are sometimes specified approximately, in group photos are only individual per-
sons. There are many exhibits the purpose and work of which is not always fully
understood. In particular, old devices and mechanisms that were used in the ed-
ucational process and now lost their importance. The service supports collecting
this clarification information from visitors and experts.
50
9
This service can use ranking for quality evaluation. In particular, let the
initial semantic network G have ranks structure RG = {rG }. After community-
based enrichment, the new semantic network is G∗ has another ranks structure
RG∗ = {rG∗ }. Analysis of the difference between RG and RG∗ supports quanti-
tative evaluation of the added value to the CHH knowledge the museum collects.
6 Conclusion
This paper presented our view on smart museum information services in respect
to their role for assisting cultural and historical heritage activity of both museum
visitors and personnel. We reviewed the smart museum concept based on the
recent progress in information and communication technology and in Internet of
Things, in particular. We presented the basic forms of such services applicable
for CHH activity within a smart museum. Our service development is based
on applying the semantic network model for integrating heterogeneous CHH
knowledge. Data mining in a semantic network is reduced to the ranking problem,
which in turn is used for constructing the services. The effectiveness is discussed
on the case study of the History Museum of PetrSU, where the proposed services
are developed for the CHH preservation, transmission, and research needs.
Acknowledgments. This research is financially supported by the Ministry of
Education and Science of Russia within project # 2.5124.2017/8.9 of the basic
part of state research assignment for 2017–2019. The work is implemented within
the Government Program of Flagship University Development for Petrozavodsk
State University in 2017–2021.
References
1. Alzahrani, A.A., Loke, S.W., Lu, H.: An advanced location-aware physical annota-
tion system: From models to implementation. Journal of Ambient Intelligence and
Smart Environments 6(1), 71–91 (2014)
2. Amato, F., Moscato, V., Picariello, A., Colace, F., Santo, M.D., Schreiber, F.A.,
Tanca, L.: Big data meets digital cultural heritage: Design and implementation
of scrabs, a smart context-aware browsing assistant for cultural environments. J.
Comput. Cult. Herit. 10(1), 6:1–6:23 (Apr 2017)
3. Augusto, J., Callaghan, V., Cook, D., Kameas, A., Satoh, I.: Intelligent environ-
ments: a manifesto. Human-centric Computing and Information Sciences 3(1), 1–18
(2013)
4. Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hell-
mann, S.: DBpedia – a crystallization point for the Web of Data. Web Semantics:
Science, Services and Agents on the World Wide Web 7, 154–165 (2009)
5. Chianese, A., Piccialli, F., Valente, I.: Smart environments and cultural heritage:
A novel approach to create intelligent cultural spaces. Journal of Location Based
Services 9(3), 209–234 (Jul 2015)
51
10
6. Kalus, M.: Semantic networks and historical knowledge management: Introduc-
ing new methods of computer-based research. The Journal of the Association for
History and Computing 10 (Dec 2007)
7. Korzun, D.: On the smart spaces approach to semantic-driven design of service-
oriented information systems. In: Proc. 12th Int’l Baltic Conf. on Databases and
Information Systems (DB&IS 2016). pp. 181–195. Springer International Publish-
ing (Jul 2016)
8. Korzun, D., Marchenkov, S., Vdovenko, A., Petrina, O.: A semantic approach to
designing information services for smart museums. International Journal of Em-
bedded and Real-Time Communication Systems (IJERTCS) 7(2), 15–34 (2016)
9. Korzun, D., Varfolomeyev, A., Yalovitsyna, S., Volokhova, V.: Semantic infrastruc-
ture of a smart museum: toward making cultural heritage knowledge usable and
creatable by visitors and professionals. Personal and Ubiquitous Computing 21,
345–354 (Apr 2017)
10. Kuflik, T., Wecker, A., Lanir, J., Stock, O.: An integrative framework for extending
the boundaries of the museum visit experience: linking the pre, during and post
visit phases. Information Technology & Tourism 15(1), 17–47 (2015)
11. Lepouras, G., Naudet, Y., Antoniou, A., Lykourentzou, I., Tobias, E., Rompa, J.:
Museum personalization based on gaming and cognitive styles: The BLUE exper-
iment. Int. J. Virtual Communities Soc. Netw. 7(2), 1–30 (Apr 2015)
12. Mouromtsev, D., Haase, P., Pavlov, D., Cherny, E., Andreev, A., Spiridonova, A.:
Towards the linked Russian heritage cloud: Data enrichment and publishing. In:
The Semantic Web. Latest Advances and New Domains (ESWC2015). pp. 637–651.
LNCS 9088, Springer (May 2015)
13. Petrina, O.B., Korzun, D.G., Volokhova, V.V., Yalovitsyna, S.E., Varfolomeyev,
A.G.: Semantic approach to opening museum collections of everyday life history
for services in Internet of Things environments. International Journal of Embedded
and Real-Time Communication Systems (IJERTCS). Special Issue on Big Data
Analytics and Intelligent Environments in Internet of Things 8(1), 31–44 (2017)
14. Ruotsalo, T., Haav, K., Stoyanov, A., Roche, S., Fani, E., Deliai, R., Makela, E.,
Kauppinen, T., Hyvonen, E.: SMARTMUSEUM: A mobile recommender system
for the Web of Data. Web Semantics: Science, Services and Agents on the World
Wide Web 20, 50–67 (May 2013)
15. Simou, N., Chortaras, A., Stamou, G., Kollias, S.: Enriching and Publishing Cul-
tural Heritage as Linked Open Data, pp. 201–223. Springer International Publish-
ing, Cham (2017)
16. Smirnov, A., Shilov, N., Kashevnik, A.: Ontology-based mobile smart museums
service, approach for small & medium museums. In: Proc. of the 4th Intern. Conf.
on Advances in Future Internet (AFIN 2012). pp. 48–54. IARIA (August 2012)
17. Torre, I.: Interaction with linked digital memories. In: Berkovsky, S., Herder, E.,
Lops, P., Santos, O.C. (eds.) Late-Breaking Results, Project Papers and Work-
shop Proceedings of the 21st Conf. on User Modeling, Adaptation, and Personal-
ization (UMAP 2013). Proc. Workshop on Personal Access to Cultural Heritage
(PATCH 2013). CEUR Workshop Proceedings, vol. 997, pp. 80–87. CEUR-WS.org
(2013)
18. Vassilakis, C., Poulopoulos, V., Antoniou, A., Wallace, M., Lepouras, G., Nores,
M.L.: exhiSTORY: Smart exhibits that tell their own stories. Future Generation
Computer Systems 81, 542–556 (2018)
52