Europeana: What Users Search for and Why

Clough, Paul; Hill, Timothy; Paramita, Monica Lestari; Goodale, Paula

doi:10.1007/978-3-319-67008-9_17

Cited by 22 publications

(11 citation statements)

References 20 publications

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“…The task aims to process the textual information from the various sources of the systems. To this end, the module integrates proper packages, i.e., Youtube-dl 5 , for accessing and extracting video metadata and OpenCV 6 for extracting visual features from frames. In detail, the following elements are extracted and grouped: (i) The video info: the textual description of each video and its title are extracted and aggregated after removing noisy elements (e.g., links or emojis).…”

Section: Metadata Extractionmentioning

confidence: 99%

CulturAI: Semantic Enrichment of Cultural Data Leveraging Artificial Intelligence

et al. 2022

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Section: Metadata Extractionmentioning

confidence: 99%

CulturAI: Semantic Enrichment of Cultural Data Leveraging Artificial Intelligence

et al. 2022

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“…For instance, according to a study on the Tate's website, 70% of users are non-art specialists (Villaespesa and Stack, 2015), and, similarly, this group represents 76% of the users of the Museums National Liverpool's website (Walsh et al, 2020). Moreover, there are users that are looking for inspiration or browse without a specific goal, and browsing by subject provides an entry point to the collection (Clough et al, 2017;Skov and Ingwersen, 2008;Villaespesa, 2019). Findings from the data point out the need to provide experiences that encourage exploration and serendipity based on other concepts and vocabularies.…”

Section: Human and Machinegenerated Tagsmentioning

confidence: 99%

A critical comparison analysis between human and machine-generated tags for the Metropolitan Museum of Art's collection

Villaespesa

Crider

2021

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PurposeBased on the highlights of The Metropolitan Museum of Art's collection, the purpose of this paper is to examine the similarities and differences between the subject keywords tags assigned by the museum and those produced by three computer vision systems.Design/methodology/approachThis paper uses computer vision tools to generate the data and the Getty Research Institute's Art and Architecture Thesaurus (AAT) to compare the subject keyword tags.FindingsThis paper finds that there are clear opportunities to use computer vision technologies to automatically generate tags that expand the terms used by the museum. This brings a new perspective to the collection that is different from the traditional art historical one. However, the study also surfaces challenges about the accuracy and lack of context within the computer vision results.Practical implicationsThis finding has important implications on how these machine-generated tags complement the current taxonomies and vocabularies inputted in the collection database. In consequence, the museum needs to consider the selection process for choosing which computer vision system to apply to their collection. Furthermore, they also need to think critically about the kind of tags they wish to use, such as colors, materials or objects.Originality/valueThe study results add to the rapidly evolving field of computer vision within the art information context and provide recommendations of aspects to consider before selecting and implementing these technologies.

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“…Darby and Clough found that these users return to their search and preferred resources frequently, that the search is ongoing and open-ended, and resources such as newspaper collections are used more in a later stage of the research. Another study used pop-up surveys to investigate the motives for search within a cultural heritage site (Clough et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

Metadata categorization for identifying search patterns in a digital library

Bogaard

Hollink

Wielemaker

et al. 2019

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For digital libraries, it is useful to understand how users search in a collection. Investigating search patterns can help them to improve the user interface, collection management and search algorithms. However, search patterns may vary widely in different parts of a collection. This study demonstrates how to identify these search patterns within a well-curated historical newspaper collection using the existing metadata. Design/methodology/approach: The authors analyzed search logs combined with metadata records describing the content of the collection, using this metadata to create subsets in the logs corresponding to different parts of the collection. Findings: The study shows that faceted search is more prevalent than non-faceted search in terms of number of unique queries, time spent, clicks and downloads. Distinct search patterns are observed in different parts of the collection, corresponding to historical periods, geographical regions or subject matter. Originality/value: First, this study provides deeper insights into search behavior at a fine granularity in a historical newspaper collection, by the inclusion of the metadata in the analysis. Second, it demonstrates how to use metadata categorization as a way to analyze distinct search patterns in a collection.

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Europeana: What Users Search for and Why

Cited by 22 publications

References 20 publications

CulturAI: Semantic Enrichment of Cultural Data Leveraging Artificial Intelligence

CulturAI: Semantic Enrichment of Cultural Data Leveraging Artificial Intelligence

A critical comparison analysis between human and machine-generated tags for the Metropolitan Museum of Art's collection

Metadata categorization for identifying search patterns in a digital library

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