Museums are now digitally enhanced based on smart information 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 e ective CHH preservation and transmission within a smart museum. The services are extended to assist in research by discovering information for applying human expertise 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.
Our society has entered into the digital era, and the term \smart" becomes
widespread in relation to modern technologies [
In this paper, we consider the smart museum concept based on our
previous work [
The rest of the paper is organized as follows. Section 2 overviews the recent progress in smart museum development. Section 3 introduces our smart museum 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
A lot of e orts 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
development 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 [
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
visitors) or evaluate exhibits (collaborative activity). IoT-enabled location-based
services make possible shortening the information distance between objects in
cultural spaces and their visitors [
Visitors are rich sources of new information about CHH objects, which is
captured using annotations [
Smart museum extends the information archive function. The latter becomes supported with Internet-based services, acting as e ective providers of CHH information. Mobile multimedia, ambient intelligence, machine vision, augmented reality, and other IoT-related technologies lead to enhanced museum information services for e ective CHH preservation, transmission, and research. 3.1
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 noti ed (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 e ective and personalized decision-making, e.g., see [
Quality evaluation: Visitors and their activity form a rich source for
qualitative 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 e ciency, so adapting and personalizing the service
provision to the user's needs, e.g., see [
Multimodal interface: Information provision is augmented using context and
multimedia (e.g., visualization of interesting facts on a nearby screen, exhibit
selfstorytelling, or 3D modeling). That is, the transmission e ciency of preserved
CHH knowledge to the visitor can be increased to the level similar to the personal
assistance by an expert, e.g., see [
Collective intelligence: Involving visitors to CHH preservation within their
museum activity (e.g., impression sharing, gami cation, or collaborative
estimates). 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 [
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
preservation [
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 uno cial 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 [
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 supplementary information search function.
Research (and knowledge enriching): A CHH researcher needs many
information facts to create a fused picture from them and to interpret the value of
this picture [
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
The need of semantic integration of available CHH knowledge for creating smart
museum information services has been already understood [
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 [
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
A semantic network is created on top of the museum collection|descriptions
from the various information sources [
The ontological model for the semantic network is de ned by ontology O. First, O describes a system of concepts fCigin=1 (ontology classes) such that any particular node v 2 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 2 V and l 2 L may have to re ect additional semantics (e.g., keywords).
Semantic network construction is implemented as a collective process [
An information service needs to nd k > 0 the most appropriate information facts. A fact can be a node v 2 V , a link l 2 L, or a connected graph structure s in G (e.g., a path from u to v can have CHH-valued interpretation for some u; v 2 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
We consider the following three classes of ranking methods [
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 xed node u 2 V and re ects distance of other nodes from u: rv(u) = 1= (u; v): For instance, if u and v have sets Ku and Kv of annotating keywords then the rank re ects the size of overlapping jKu \ Kvj, 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 ltering: This ranking model assumes that many users generate opinions about each CHH object. The opinions are transformed to some community based score rv (normalized 0 rv 1). Then the scores can be combined with other ranking requirements. For instance, rv = rv + (1 ) 1
dv maxw2W dw ; where W V is nodes of potential interest for the visitor, dw > 0 is a physical reachability metric for node w, 0 1 is a tradeo parameter between community 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 2 V can be computed iteratively starting from some initial values ru(0): r(i+1) = u
X l=(u;v)2L pvurv(i) + (1 ) u ; where pvu is weight of the link l = (u; v), 0 1 is the damping factor denoting the probability of following the connectivity structure of G, and is a jump probability vector for all u 2 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.
The History Museum of Petrozavodsk State University (PetrSU) is a typical
small museum oriented to everyday life history [
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 [
Visit service: The service constructs a personalized exposition of recommended 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 represents 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 educational function becomes more e ective, making an almost personal approach to each visitor and improving the whole PetrSU image.
Visit service uses exhibit ranks based on local ranking (although other ranking models can be applied as well). In particular, rv = 1= (U; v), where the distance (U; v) shows semantic similarity of users U to the exhibit v 2 V . The CHH interests of the user are included into the user pro le and can be represented in the node u 2 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 information 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., implementing 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 e ective extraction of information allow the student to use the services to nd information important for her/his study.
For recommendations local ranking can be used. In particular, rv = 1= (u; v), where the distance (u; v) shows the relation level of information fact v to the recently studied exhibit or fact u (for u; v 2 V ). The recommendation assistance is online (i.e., implementing smart navigation). An e ective 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 modi cation (evolution) of the semantic 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 primary 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 personnel 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 exhibits for which information needs clari cation: the date of creation of photos are sometimes speci ed approximately, in group photos are only individual persons. 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 educational process and now lost their importance. The service supports collecting this clari cation information from visitors and experts.
This service can use ranking for quality evaluation. In particular, let the initial semantic network G have ranks structure RG = frGg. After communitybased enrichment, the new semantic network is G has another ranks structure RG = frG g. Analysis of the di erence between RG and RG supports quantitative evaluation of the added value to the CHH knowledge the museum collects. 6
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 e ectiveness 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 nancially 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.