Smart Museum: Semantic Approach to Generation and Presenting Information of Museum Collections

Smart Museum: Semantic Approach to Generation and Presenting Information of Museum Collections

Svetlana E. Yalovitsyna, Valentina V. Volokhova, Dmitry G. Korzun
DOI: 10.4018/978-1-7998-1974-5.ch009
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The chapter presents the authors' study on the smart museum concept. Semantic Web technology and ontology modeling methods are applied to construct advanced digital services, supporting the study and evolution of museum collections. The concept aims at significant increase of the information impact of museum exhibits by providing augmented annotations, identifying semantic relations, assisting the visitors to follow individual trajectories in exposition study, finding relevant information, opening the collection to knowledge from visitors. A museum collection is advanced to a knowledge base where new information is created and evolved by museum visitors and personnel. The chapter discusses reference information assistance services, which are oriented for use as mobile applications on users' smartphones. The proof-of-the-concept case study is the History Museum of Petrozavodsk State University. The pilot implementation demonstrates the feasibility of the smart museum concept in respect to the user mobility, service personalization, and collaborative work opportunity.
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Nowadays, the museum digitalization is a topical area for applying information and communication technology. Various information services are constructed to extend the value of existing museum collections. A typical museum collection is implemented as a database for storing descriptions on exhibits. This way, the museum information system stores the information part of collection to keep all knowledge related to and about cultural and historical heritage objects (exhibits). The basic function is an electronic archive (catalogue). Its digital extensions lead to “smart services”, emphasizing a certain intelligence level in information search for and delivery to the users.

As we sequentially elaborated previously (Korzun, Marchenkov, Vdovenko, & Petrina, 2016; Petrina, Korzun, Volokhova, Yalovitsyna, & Varfolomeyev, 2017; Korzun, Yalovitsyna, & Volokhova, 2018; Yalovitsyna, Volokhova, & Korzun, 2019), the Internet of Things (IoT) enables advancing a museum information system to “a smart space” where visitors and personnel operate in the shared service-oriented information-centric environment. In particular, study activity of visitors is involved to the museum processes, hence opening many possibilities to engage the museum visitors with exhibits and available descriptive information. This chapter summarizes the authors’ smart museum concept in order to form a semantic service-oriented approach to generating and presenting information of museum collections.

The semantic approach introduces an additional layer on the top of museum information system (the semantic layer). (Marchenkov, Vdovenko, Petrina, & Korzun, 2016) The layer maintains a semantic network of available digitalized descriptions (meaningful information fragments). The semantic layer connects the involved actors (museum personnel and visitors) with the physical exposition. The museum collection becomes not just a large database, where information is consumed in the traditional passive style (visitors are walking around exhibits and reading information from the database). Instead, the museum provides a digital environment where all fragments of the museum exposition become semantically related, leading to easy use and further elaboration by visitors and museum personnel.

To implement the semantic layer, the Semantic Web technology and ontology modeling methods are applied. The semantic network is represented based on the Semantic MediaWiki (SMW) technique (Krötzsch & Vrandečić, 2011). Nodes in the semantic networks are wiki-pages where information representation follows a specific format, both human- and machine- readable. Each page is augmented with semantic information in the form of tags (keywords) and links to other pages. As a result, one can search information based on keywords and connection structure, similarly as information study happens in web browsing in the Internet. This kind of information search is advanced with information ranking when a small set of the most relevant information is provided among many appropriate information fragments. The advance also follows the web technology, where the search results are sorted in accordance with certain priorities to the user (e.g., the well-known PageRank algorithm).

Our proof-of-the-concept case study is the History Museum of Petrozavodsk State University (the History Museum of PetrSU), where the focus is on everyday life history. The pilot implementation considers the following reference museum information services: 1) Visit service to support the visitor with personalized exhibition study plan, 2) Exhibition service to support the visitor with personalized delivery of knowledge on a given exhibit, and 3) Enrichment service to support the museum with a tool for extracting knowledge from visitors.

The goal of this chapter is to summarize the own authors’ experience in the development of smart museum concept. The semantic approach is presented to generating and presenting information of museum collections. The presentation is structured as follows. First, to overview the existing IoT-enabled background related to smart museums. Second, to analyze the key problems of semantic layer construction on the top of a museum information system. Third, to elaborate appropriate models of Semantic Web to apply to creation of the semantic layer. Fourth, to discuss possible ranking algorithm to search the most relevant information in the museum collection. Then we summarize the key solutions and recommendations in the smart museum concept, overview possible future research directions, and conclude the chapter.

Key Terms in this Chapter

Recommendation: Information that can be interpreted by the user in order to make reasoned decisions.

Semantic Network: A knowledge base that represents semantic relations between concepts in a network. The model of knowledge representation is based on a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts, mapping or connecting semantic fields.

Semantic Web: A technology extending the world wide web (WWW) through the standards by the world wide web consortium (W3C). The technology provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. The technology is regarded as an integrator across different content, information applications, and systems.

Ontology: Formal representation of knowledge as a set of concepts within a domain, using a shared vocabulary to denote the types, properties, and interrelationships of those concepts.

Information Ranking: Arrangement of information fragments based on their relevance. Each rank value quantitatively reflects the relevance (i.e., a fragment of zero rank is irrelevant to the considered problem).

Service: The process of information search, selection, and reasoning to provide meaningful information (typically in a visual form) to the user in respect to the current user’s needs and context.

Semantic MediaWiki (SMW): A full-fledged framework to create a powerful and flexible knowledge management system. All data created within SMW are easily exported or published via the Semantic Web, allowing other systems to use this data seamlessly.

Context: Any information that can be used to characterize the situation of a person or the object the person is studying.

Museum Information System: An information system to store the information part of the museum collection as well as to access information about the museum collection.

Museum Collection: A set of physical exhibits as well as all information descriptions associated with the collected exhibits.

Smart Space: A search extend of information fused from many resources in computational networked environment (computer devices, databases, information systems, data sensors, information processors, etc.).

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