Since the dawn of the new millennium and even earlier, a coordinated effort has been underway to expand the World Wide Web into a machine-readable web of data known as the Semantic Web. The field of art and culture has been one of the most eager to integrate with the Semantic Web, since metadata, data structures, linked-data, e.g., the Getty vocabularies project and the Europeana LOD initiative—and other building blocks of this web of data are considered essential in cataloging and disseminating art and culture-related content. However, art is a constantly evolving entity and as such it is the subject of a vast number of online media outlets and journalist blogs and websites. During the course of the present study the researchers collected information about how integrated the media outlets that diffuse art and culture-related content and news are to the Semantic Web. The study uses quantitative metrics to evaluate a website’s adherence to Semantic Web standards and it proceeds to draw conclusions regarding how that integration affects their popularity in the modern competitive landscape of the Web.

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The Internet, and specifically the World Wide Web, has always been a useful tool in the effort to achieve more outward-looking economies. The launch of the .eu TLD (top-level domain) in December of 2005 introduced the concept of a pan-European Internet identity that aimed to enhance the status of European citizens and businesses on the global Web. In this study, the countries of origin of websites that choose to use the .eu TLD are investigated and the reasoning behind that choice, as well as its relation to each country’s economy and external trade are discussed. Using the Web as a tool, information regarding a vast number of existing .eu websites was collected, through means of Web data extraction, and this information was analyzed and processed by a detailed algorithm that produced results concerning each website’s most probable country of origin based on a multitude of factors. This acquired knowledge was then used to investigate relations with each member-state’s presence in its local ccTLD, its GDP and its external trade revenue. The study establishes a correlation between presence in the .eu TLD and external trade that is both independent of a country’s GDP and stronger than the relation between its local ccTLD presence and external trade.

Graph-like structures, which are increasingly popular in data representation, stand out since they enable the integration of information from multiple sources. At the same time, clustering algorithms applied on graphs allow for group entities based on similar characteristics, and discover statistically important information. This paper aims to explore the associations between the visual objects of the Renaissance in the Europeana database, based on the results of topic modeling and analysis. For this purpose, we employ Europeana’s Search and Report API to investigate the relations between the visual objects from this era, spanning from the 14th to the 17th century, and to create clusters of similar art objects. This approach will lead in transforming a cultural heritage database with semantic technologies into a dynamic digital knowledge representation graph that will relate art objects and their attributes. Based on associations between metadata, we will conduct a statistic analysis utilizing the knowledge graph of Europeana and topic modeling analysis.

Studying searcher behavior has been a cornerstone of search engine research for decades, since it can lead to a better understanding of user needs and allow for an improved user experience. Going beyond descriptive data analysis and statistics, studies have been utilizing the capabilities of Machine Learning to further investigate how users behave during general purpose searching. But the thematic content of a search greatly affects many aspects of user behavior, which often deviates from general purpose search behavior. Thus, in this study, emphasis is placed specifically on the fields of Art and Cultural Heritage. Insights derived from behavioral data can help Culture and Art institutions streamline their online presence and allow them to better understand their user base. Existing research in this field often focuses on lab studies and explicit user feedback, but this study takes advantage of real usage quantitative data and its analysis through machine learning. Using data collected by real world usage of the Art Boulevard proprietary search engine for content related to Art and Culture and through the means of Machine Learning-powered tools and methodologies, this article investigates the peculiarities of Art-related online searches. Through clustering, various archetypes of Art search sessions were identified, thus providing insight on the variety of ways in which users interacted with the search engine. Additionally, using extreme Gradient boosting, the metrics that were more likely to predict the success of a search session were documented, underlining the importance of various aspects of user activity for search success. Finally, through applying topic modeling on the textual information of user-clicked results, the thematic elements that dominated user interest were investigated, providing an overview of prevalent themes in the fields of Art and Culture. It was established that preferred results revolved mostly around traditional visual Art themes, while academic and historical topics also had a strong presence.

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