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.

中文翻译：

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使用知识图谱和下一代数据模型从学术文献中可视化痴呆症的风险因素

知识的学术交流主要是基于数字存储库中的文档，研究人员发现自动捕获和处理相关文章之间的语义很乏味。尽管现在是大数据的数字时代，但学术文章中缺乏对知识的可视化表示，因此需要一种节省时间的文献搜索和可视化导航方法。大多数知识展示工具无法应对当前的大数据趋势，在满足知识自动表示、存储和动态可视化的需求方面存在局限性。为了解决这个限制，本文的主要目的是对非结构化数据的可视化进行建模，并探索实现可视化导航的可行性，以便研究人员深入了解数字知识库科学文章中隐藏的知识。当代研究和实践主题，包括导致阿尔茨海默病和其他形式的痴呆症急剧增加的可改变风险因素，需要对文献中可用的循证知识进行更深入的了解。目标是通过研究文章的数字存储库为研究人员提供基于视觉的轻松遍历。本文迈出了第一步，提出了一种使用知识图和下一代图形数据存储的新型集成模型，以实现具有特定领域知识（例如痴呆症风险因素）的语义可视化。该模型通过在从研究文章的大数据资源中提取的知识之间自动建立视觉关系，促进了对文献的深刻概念理解。它还用作通过知识库进行视觉导航的自动化工具，以便更快地识别学术文章中报告的痴呆症风险因素。此外，它有助于从大型数字存储库及其关联中进行语义可视化和特定领域的知识发现。在本研究中，所提出模型在 Neo4j 图形数据存储库中的实现以及所取得的结果被作为概念证明。以痴呆危险因素学术研究文章为案例，自动知识提取、存储、智能搜索、和视觉导航。上下文知识的实施及其与研究人员视觉探索的关系在痴呆症风险因素的知识发现中显示出有希望的结果。总的来说，这项研究通过有效利用知识图展示了语义可视化的重要性，并为未来扩展可视化建模能力铺平了道路。