Data sustainability: Data governance in data infrastructures across technological and human generations

Abstract The Fifth Industrial Revolution is ushering in a profound era of technological advancement and societal transformation, prompting a critical examination of the evolving role of libraries in this digital landscape. Traditionally regarded as the custodians of knowledge, libraries must adapt to these changing dynamics of the digital world while preserving their core principles. This study looks into the strategic integration of advanced technologies, aiming to redefine the mission of libraries in the face of complex challenges. The Fifth Industrial Revolution encompasses a convergence of diverse technologies, including the Internet of Things (IoT), cloud computing, biotechnology, nanotechnology, and quantum computing, all underpinned by the currency of data. Artificial intelligence (AI) is a focal point, promising enhancements in user experiences and operational efficiency, however, it also raises critical ethical and privacy concerns that challenge fundamental library principles. This research employs an interpretive content/document analysis approach to examine the evolving roles of libraries, considering their pivotal function in bridging the digital divide, providing equitable access to technology and digital resources, advancing research and innovation, and fostering community engagement. The study underscores the imperative to address challenges related to data privacy, digital inclusion, resource allocation, intellectual property, and sustainability. However, despite these challenges, libraries are poised to remain indispensable pillars of education, research, and community interaction. To do so, they must embrace technology strategically to redefine their roles and expand their influence within evolving knowledge systems. 

pdf/the-intelligent-libraries-innovation-for-a-sustainable-2emn1mm3lv.pdf

The Intelligent Libraries: Innovation for a Sustainable Knowledge System in the Fifth (5th) Industrial Revolution

Business Intelligence (BI) requires the collection and organization of important pieces of information (i.e. entities) from multiple sources to provide valuable insights (e.g. business trends) as events (i.e. a specific happening linked with a specific location and time) to users. Online news articles are one of the important information sources that present business news offered by various companies in the market every day all around the world. These news articles often cover the same events and report redundant information. Existing news platforms aim at collecting the key entities from news articles and providing a mechanism to view the latest and relevant business events based on user interest. However, they do not provide a method to model business events and understand them temporally, spatially, and contextually (i.e. changes in the event). For instance, it is crucial to know for how long a business event has been active? How important is its evolution locally, or worldwide? Or how did different companies come up with this event as competitors in the market? The contribution of this research is the exploration of the possibilities of modeling spatial, temporal, and contextual information evolution related to business events through the application of knowledge graphs and text analytics, more specifically, Natural Language Processing (NLP) methods. The constructed knowledge graphs through Named-Entity Recognition (NER), i.e., an NLP technique, present a compact news representation that tells the key entities of the business event at one glance using linked open data concepts. It enables the assessment of other related news events as well as provides the means for analysis of the influence and evolution of business events.

Business Insights Using Knowledge Graphs by Text Analytics in Dynamic Environments

Mapping between different representations of similar data is a common challenge in digital humanities (DH). In practical DH collections, the ‘person’ is an essential and centric unit and other parts could link to the ‘person’ to form the knowledge base. However, there is still no general and useful person‐oriented ontology in DH community. Many practical DH projects have developed their own ontologies by DH experts, but these ontologies are not interoperable. Therefore, it is important to explore existing biographical ontologies and develop a comprehensive person‐oriented ontology for DH.

Person‐Oriented Ontologies Analysis for Digital Humanities Collections from a Metadata Crosswalk Perspective

ABSTRACT While long recognized in the humanities, there is growing recognition in the sciences and social sciences that primary sources—as diverse as manuscripts, photographs, cultural belongings, and specimens—hold vast data about scientific and human knowledge for use in scholarship, community research, and global knowledge. Yet, data embedded in these sources are largely disconnected from the systems of discovery, access, and structured data that support reuse and insights across globally dispersed repositories. In this paper, we share select findings of a systematic review to explore the use of primary sources, and the data embedded in them, via linked data across the sciences and social sciences. Our results confirm the use of a variety of primary source data across diverse disciplines, particularly those requiring longitudinal studies and data integration from diverse repositories and contexts. We highlight how linked data are understood to: connect collections to communities; support highly granular credit, attribution, and assessment of impact; and interrelate diverse sources of knowledge. While these results suggest the value of linked data for the specific research needs of anthropology, the effectiveness of linked data in achieving these objectives and the suitability of this approach for a diversity of institutions and communities need further study. 

Primary Sources as Linked Data: Exploring Motives Across the Sciences and Social Sciences

The Semantic Web in general and the Linked Open Data Initiative, in particular, are a growing movement for organisations to make their existing data available in a machine-readable format. Thus, institutions are highly encouraged to publish, share and interlink their data publicly. The more data are opened on the Web (Open Data), the more integrated sets of data will be connected in the Semantic Web (Linked Open Data). Within this context, libraries can complement their data by linking it to other, external data sources. The purpose of this article is to identify papers that refer to linked data in libraries, emphasising the ways that linked data empower libraries to put their knowledge in the context of the open-world, thus enhancing semantic technology innovations. The study considered papers published between 2008 and 2019 in English and presents the collected literature by grouping it according to the topic each paper refers to. The results show that libraries are facing a period of continuing change which present several challenges and indicate that they are moving towards developing new practices, policies and services.

Linked data for libraries: Creating a global knowledge space, a systematic literature review

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Linked data for libraries: Creating a global knowledge space, a systematic literature review