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Source

Journal of the Association for Information Science and Technology. 74(2023) no.12, S.1449-1462

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Abstract

The purpose of this work is to find out how different library classification systems and linguistic ontologies arrange a particular domain of interest and what are the limitations for information retrieval. We use knowledge representation techniques and languages for construction of a domain specific ontology. This ontology would help not only in problem solving, but it would demonstrate the ease with which complex queries can be handled using principles of domain ontology, thereby facilitating better information retrieval. Facet-based methodology has been used for ontology formalization for quite some time. Ontology formalization involves different steps such as, Identification of the terminology, Analysis, Synthesis, Standardization and Ordering. Firstly, for purposes of conceptualization OntoUML has been used which is a well-founded and established language for Ontology driven Conceptual Modelling. Phase transformation of "the same mode" has been subsequently obtained by OWL-DL using Protégé software. The final OWL ontology contains a total of around 232 axioms. These axioms comprise 148 logical axioms, 76 declaration axioms and 43 classes. These axioms glue together classes, properties and data types as well as a constraint. Such data clustering cannot be achieved through general use of simple classification schemes. Hence it has been observed and established that domain ontology using faceted principles provide better information retrieval with enhanced precision. This ontology should be seen not only as an alternative of the existing classification system but as a Knowledge Base (KB) system which can handle complex queries well, which is the ultimate purpose of any classification system or indexing system. In this paper, we try to understand how ontology-based information retrieval systems can prove its utility as a useful tool in the field of library science with a particular focus on the education domain.

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Abstract

Purpose This study aimed at understanding the use of paintings outside of an art-related context, in the English version of Wikipedia. Design/methodology/approach For this investigation, the authors identified 8,104 paintings used in 10,008 articles of the English Wikipedia edition. The authors manually coded the topic of the article in question, documented the number of monthly average views and identified the originating museum. They analysed the use of images based on frequency of use, frequency of view, associated topics and location. Early in the analysis three distinct perspectives emerged: the readers of the online encyclopaedia, the editors of the articles and the museum organisations providing the painting images (directly or indirectly). Findings Wikipedia is a widely used online information resource where images of paintings serve as visual reference to illustrate articles, notably also beyond an art-related topic and where no alternative image is available - as in the case of historic portraits. Editors used paintings as illustration of the work itself or art-related movement, but also as illustration of past events, as alternative to photographs, as well as to represent a concept or technique. Images have been used to illustrate up to 76 articles, evidencing the polysemic nature of paintings. The authors conclude that images of paintings are highly valuable information sources, also beyond an art-related context. They also find that Wikipedia is an important dissemination channel for museum collections. While art-related articles contain greater number of paintings, these receive less views than non-art-related articles containing fewer paintings. Readers of all topics, predominantly history, science and geographic articles, viewed art pieces outside of an art context. Painting images in Wikipedia receive a much larger online audience than the physical painting does when compared to the number of museum onsite visitors. The authors' results confirm the presence of a strong long-tail pattern in the frequency of image use (only 3% of painting images are used in a Wikipedia article), image view and museums represented, characteristic of network dynamics of the Internet.