A framework of genealogy knowledge reasoning and visualization based on a knowledge graph
| Date | 30 June 2023 |
| Pages | 1977-1999 |
| DOI | https://doi.org/10.1108/LHT-05-2022-0265 |
| Published date | 30 June 2023 |
| Subject Matter | Library & information science,Librarianship/library management,Library technology,Information behaviour & retrieval,Information user studies,Metadata,Information & knowledge management,Information & communications technology,Internet |
| Author | Ruan Wang,Jun Deng,Xinhui Guan,Yuming He |
A framework of genealogy
knowledge reasoning and
visualization based on
a knowledge graph
Ruan Wang and Jun Deng
Jilin University, Changchun, China
Xinhui Guan
Jilin Province Library, Changchun, China, and
Yuming He
Old Dominion University, Norfolk, Virginia, USA
Abstract
Purpose –With the development of data mining technology, diverse and broader domain knowledge can be
extracted automatically. However, the research on applying knowledge mapping and data visualization
techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic
framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.
Design/methodology/approach –Based on a literature review of genealogy’s current knowledge reasoning
research, the authors constructed an integrated framework for knowledge inference and visualization
application using a knowledge graph. Additionally, the authors applied this framework in a case study using
“Manchu Clan Genealogy”as the data source.
Findings –The case study shows that the proposed framework can effectively decompose and reconstruct
genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit
information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting
the intricate relationships among people, places, and time entities.
Originality/value –This study proposed a framework for genealogy knowledge reasoning and visual
analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data
layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and
establish a data network with semantic correlations while establishing reasoning rules to enable inference
discovery and visualization of hidden relationships.
Keywords Knowledge graph, Knowledge reasoning, Knowledge completion, Visualization, Genealogy,
Ontology
Paper type Research paper
1. Introduction
Genealogy, also known as family trees, involves gathering information and working
backward in time based on the knowledge of a family (Chang, 2018). Genealogy has received
extensive attention from experts and scholars in many fields, such as history, sociology,
folklore, demography, and computer science. It has become important for building family
morale, maintaining emotional ties, and documenting historical information (Yeh and Chen,
2003). Genealogy also serves as a form of historical critique by tracing the network of
Framework of
genealogy
knowledge
reasoning
1977
The author claims there is no potential conflict of interest in this paper.
Funding: This work was supported by the Science Programs of the Education Department of Jilin
Province [JJKH20221113SK].
Declaration of competing interest: The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence the work reported in this paper.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0737-8831.htm
Received 22 May 2022
Revised 26 November 2022
30 April 2023
Accepted 5 May 2023
Library Hi Tech
Vol. 42 No. 6, 2024
pp. 1977-1999
© Emerald Publishing Limited
0737-8831
DOI 10.1108/LHT-05-2022-0265
associations between seemingly disparate historical events (Yang, 2020). The genealogy and
paradigm changes of technical knowledge are essential for development (Yamada, 2022). The
rapid development of digital technologies is boosting the growth of humanities resources, and
the new data-driven paradigm is accelerating the transformation of humanities research. As a
representative text analysis and visualization technology in the digital humanities (El-Hajj
and Valleriani, 2021), a knowledge graph effectively reveals associations with graph
structure. Due to the limitation of the information completeness of data sources, the
knowledge architecture contained in knowledge graphs is not perfect; it requires the effort to
mine the pattern rules by the associated triples for graph inference and complementation
(Yu et al., 2021). The increasing adoption of digital humanities technologies has facilitated the
application of knowledge mapping and associative reasoning techniques in information
systems research, leading to the development of various knowledge organization and
discovery approaches (Qin et al., 2021). This progress has created a valuable opportunity for
the reorganization of humanities resources.
With the development of data mining technology, diverse and broader domain knowledge
is extracted automatically (Lytras et al., 2019). However, few researchers have applied
knowledge mapping and data visualization techniques to genealogical data. Some focus on
background introduction and algorithm innovation but pay less attention to genealogical
data fusion, representation, and inference (Wu et al., 2021). Also, some of them lack a
systematic framework and process guidance (Shrivastava and Pal, 2009). This paper aims to
fill this research gap through three main aspects. First, we identify the current development
and issues about knowledge graphs and knowledge inference; second, we construct an
integrated framework for knowledge inference and visualization application based on the
knowledge graph; finally, we illustrate the implementation of this framework with an
example. Specifically, we apply knowledge mapping technology to mine genealogical
resources, which allows for the automatic discove ry of hidden knowledge through
association rules and the visualization of the resulting knowledge graph. Our
contributions are mainly in:
(1) creating an integrated framework for knowledge reasoning and visualization
applications based on knowledge graphs,
(2) optimizing the knowledge inference and knowledge complementation methods, and
(3) realizing the dynamic mutual promotion of users and resources from the perspective
of the users’experience with visualization technology
The rest of this paper is organized as follows: Section 2 offers a brief survey of the related
work on KG creation. In Section 3, the details of our KG framework are presented. The
experiment and its results are reported in Section 4. Finally, this work is discussed and
concluded in Sections 5 and 6.
2. Literature review
2.1 Knowledge graph: a structured semantic knowledge base
Initially developed to enhance Google’s search engine, the knowledge graph (KG) operates on
the semantic web principles, allowing for intelligent retrieval of vast amounts of knowledge
through network-based processes (Singhal, 2012). With the semantic triad structure of entity,
relation and entity, with nodes and edges serving as the organizing elements in its graph
module, KG is often used for the normative description of domain concepts and their relations
(Y. Chen et al., 2020). In recent years, KG has emerged as a new form of knowledge
organization and representation, strongly related to semantic web technologies, large-scale
data analysis, and cloud computing in research and business (He et al., 2021).
LHT
42,6
1978
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