Abstract
Scholarly communication of knowledge is predominantly document-based in digital repositories, and researchers find it tedious to automatically capture and process the semantics among related articles. Despite the present digital era of big data, there is a lack of visual representations of the knowledge present in scholarly articles, and a time-saving approach for a literature search and visual navigation is warranted. The majority of knowledge display tools cannot cope with current big data trends and pose limitations in meeting the requirements of automatic knowledge representation, storage, and dynamic visualization. To address this limitation, the main aim of this paper is to model the visualization of unstructured data and explore the feasibility of achieving visual navigation for researchers to gain insight into the knowledge hidden in scientific articles of digital repositories. Contemporary topics of research and practice, including modifiable risk factors leading to a dramatic increase in Alzheimer’s disease and other forms of dementia, warrant deeper insight into the evidence-based knowledge available in the literature. The goal is to provide researchers with a visual-based easy traversal through a digital repository of research articles. This paper takes the first step in proposing a novel integrated model using knowledge maps and next-generation graph datastores to achieve a semantic visualization with domain-specific knowledge, such as dementia risk factors. The model facilitates a deep conceptual understanding of the literature by automatically establishing visual relationships among the extracted knowledge from the big data resources of research articles. It also serves as an automated tool for a visual navigation through the knowledge repository for faster identification of dementia risk factors reported in scholarly articles. Further, it facilitates a semantic visualization and domain-specific knowledge discovery from a large digital repository and their associations. In this study, the implementation of the proposed model in the Neo4j graph data repository, along with the results achieved, is presented as a proof of concept. Using scholarly research articles on dementia risk factors as a case study, automatic knowledge extraction, storage, intelligent search, and visual navigation are illustrated. The implementation of contextual knowledge and its relationship for a visual exploration by researchers show promising results in the knowledge discovery of dementia risk factors. Overall, this study demonstrates the significance of a semantic visualization with the effective use of knowledge maps and paves the way for extending visual modeling capabilities in the future.
Highlights
In this digital era, massive datasets available in different forms, termed big data, grow rapidly with their complex structures derived from disparate sources such as the Internet, mobile devices, software/network logs, and social media
The following is the first sample text taken from one of the research articles from the collection of research articles related to risk factors for dementia [3]: Our study found a potentially important effect modification between exercise and physical functioning in ...[which does] not prevent dementia but might be associated with a delay in onset
Conclusions and future research In this paper, the initial steps are presented for enhancing knowledge discovery from scholarly articles with an effective use of the knowledge mapping technique for a semantic visualization combined with the non-relational big data paradigm
Summary
Massive datasets available in different forms, termed big data, grow rapidly with their complex structures derived from disparate sources such as the Internet, mobile devices, software/network logs, and social media. Advancements in information and communication technology (ICT) have led to the adoption of a flexible data management system that has the capability to store and process diverse, complex, and massive datasets [1]. A semantic visualization is a powerful data presentation technique that displays hidden knowledge in big data it demonstrates the patterns of knowledge flow. Such visualization plays an important role in understanding, interpreting, and analyzing datasets based on the patterns of massively scaled and complex knowledge flows. There are various options available to visually present the data and knowledge, such as mind, organizational, concept, story, and graph-based maps. A particular visualization technique called a knowledge map is widely used to visually represent data in the database, in the form of nodes and edges that derive roots from graph theory
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