A Generic Framework for Adding Semantics to Digital Libraries

Aslam, Muhammad Ahtisham; Aljohani, Naif Radi; Abbasi, Rabeeh Ayaz; Lytras, Miltiadis D.; Kabir, Muhammad Ashad

doi:10.1007/978-3-319-55961-2_28

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Multiple researchers agreed (Hansen and Kautz (2004) cited in Balaida et al (2016), Tiwana (1999, White (2002) that there are advantages of using a knowledge map, such as effective storage and traversal of knowledge represented by the knowledge map. Researchers Kaur and Chopra (2016), Aslam et al (2017) concluded that unstructured data in the form of text are very complex; therefore, powerful techniques are required to extract the data. Most text mining tools used NLP techniques and in some cases the combination of machine learning techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Knowledge graph model development for knowledge discovery in dementia research using cognitive scripting and next-generation graph-based database: a design science research approach

Fahd

Miao

Miah

et al. 2022

Soc. Netw. Anal. Min.

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Recent studies report doubling numbers of deaths due to dementia. With such an escalating mortality rate related to cognitive decline diseases, like dementia, timely information on contributing factors and knowledge discovery from evidence-based repositories is warranted. A large amount of scholarly knowledge extracted from research findings on dementia can be understood only using human intelligence for arriving at quality inferences. Due to the unstructured data presented in such a massive dataset of scientific articles available online, gaining insights from the knowledge hidden in the literature is complex and time-consuming. Hence, there is a need for developing a knowledge management model to create, query and maintain a knowledge repository of key elements and their relationships extracted from scholarly articles in a structured manner. In this paper, an innovative knowledge discovery computing model to process key findings from unstructured data from scholarly articles by using the design science research (DSR) methodology is proposed. The solution caters to a novel composition of the cognitive script of crucial knowledge related to dementia and its subsequent transformation from unstructured into a structured format using graph-based next-generation infrastructures. The computing model contains three phases to assist the research community to have a better understanding of the related knowledge in the existing unstructured research articles: (i) article collection and construction of cognitive script, (ii) generation of Cypher statements (a knowledge graph query language) and (iii) creation of graph-based repository and visualization. The performance of the computing model is demonstrated by visualizing the outcome of various search criteria in the form of nodes and their relationships. Our results also demonstrate the effectiveness of visual query and navigation highlighting its usability.

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Section: Literature Reviewmentioning

confidence: 99%

Knowledge graph model development for knowledge discovery in dementia research using cognitive scripting and next-generation graph-based database: a design science research approach

Fahd

Miao

Miah

et al. 2022

Soc. Netw. Anal. Min.

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“…In the academic world and communities of practice, scholarly articles in the literature provide domain-specific knowledge, and their importance is weighed based on the credibility of the source of information, authors, and many other factors. It is common practice to extract metadata from research articles to provide relevant data in a structured manner [4]. The major findings from the literature review show that some of the standard information extracted from the research articles includes the title, author, year of publication, publisher, and editors, which are recorded in a database to create an open library publication framework.…”

Section: Related Studies and The Need For The Present Researchmentioning

confidence: 99%

Visualizing risk factors of dementia from scholarly literature using knowledge maps and next-generation data models

Fahd¹,

Venkatraman²

2021

Vis. Comput. Ind. Biomed. Art

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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.

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A Generic Framework for Adding Semantics to Digital Libraries

Cited by 2 publications

References 7 publications

Knowledge graph model development for knowledge discovery in dementia research using cognitive scripting and next-generation graph-based database: a design science research approach

Knowledge graph model development for knowledge discovery in dementia research using cognitive scripting and next-generation graph-based database: a design science research approach

Visualizing risk factors of dementia from scholarly literature using knowledge maps and next-generation data models

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