A knowledge graph analysis tool of people and organizations to facilitate digital humanities research
| Date | 08 August 2024 |
| Pages | 82-110 |
| DOI | https://doi.org/10.1108/DTA-01-2024-0009 |
| Published date | 08 August 2024 |
| Author | Chih-Ming Chen,Barbara Witt,Chun-Yu Lin |
A knowledge graph analysis tool
of people and organizations
to facilitate digital
humanities research
Chih-Ming Chen, Barbara Witt and Chun-Yu Lin
National Chengchi University, Taipei, Taiwan
Abstract
Purpose –To support digital humanities research more effectively and efficiently, this study develops a novel
Knowledge Graph Analysis Tool of People and Organizations (KGAT-PO) for the Digital Humanities Research
Platform for Biographies of Chinese Malaysian Personalities (DHRP-BCMP) based on artificial intelligence (AI)
technology that would not only allow humanities scholars to look at the relationships between people but also
has the potential for aiding digital humanities research by identifying latent relationships between people via
relationships between people and organizations.
Design/methodology/approach –To verify the effectiveness of KGAT-PO, a counterbalanced design was
applied to compare research participants in two groups using DHRP-BCMP with and without KGAT-PO,
respectively, to perform people relationship inquiry and to see if there were significant differences in the
effectiveness and efficiency of exploring relationships between people, and the use of technology acceptance
between the two groups. Interviews and Lag Sequential Analysis were also used to observe research
participants’perceptions and behaviors.
Findings –The results show that the DHRP-BCMP with KGAT-PO could help research participants
improve the effectiveness of exploring relationships between people, and the research participants
showed high technology acceptance towards using DHRP-BCMP with KGAT-PO. Moreover, the research
participants who used DHRP-BCMP with KGAT-PO could identify helpful textual patterns to explore
people’s relationships more quickly than DHRP-BCMP without KGAT-PO. The interviews revealed that
most research participants agreed that the KGAT-PO is a good starting point for exploring relationships
between people and improves the effectiveness and efficiency of exploring people’srelationship
networks.
Research limitations/implications –The research’s limitations encompass challenges related to data
quality, complex people relationships, and privacy and ethics concerns. Currently, the KGAT-PO is
limited to recognizing eight types of person-to-person relationships, including couple, sibling, parent-
child, friend, teacher-student, relative, work, and others. These factors should be carefully considered
to ensure the tool’s accuracy, usability, and ethical application in enhancing digital humanities
research.
Practical implications –The study’s practical implications encompass enhancedresearch efficiency, aiding
humanities scholars in uncovering latent interpersonal relationships within historical texts with high
technology acceptance. Additionally, the tool’s applications can extend to social sciences, business and
marketing, educational settings, and innovative research directions, ultimately contributing to data-driven
insights in the field of digital humanities.
Originality/value –The research’s originality lies in creating a Knowledge Graph Analysis Tool of People
and Organizations (KGAT-PO) using AI, bridging the gap between digital humanities research and AI
technology. Its value is evident in its potential to efficiently uncover hidden people relationships, aiding digital
humanities scholars in gaining new insights and perspectives, ultimately enhancing the depth and
effectiveness of their research.
Keywords Digital humanities, Relationships between people and organizations, Knowledge graph,
Text mining, Information visualization, Lag sequence analysis
Paper type Research paper
DTA
59,1
82
The authors would like to thank the Research Center for Chinese Cultural Metaverse in Taiwan for
financially supporting this research under Contract No. 113H21.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2514-9288.htm
Received 4 January 2024
Revised 19 April 2024
Accepted 13 July 2024
Data Technologies and
Applications
Vol. 59 No. 1, 2025
pp. 82-110
© Emerald Publishing Limited
2514-9288
DOI 10.1108/DTA-01-2024-0009
1. Introduction
Humanities research has often relied chiefly on traditional text-based reading approaches to
explore textual content. However,when faced with theinformation explosion of the current
informationsociety, researchmethods that solely rely on manpowerto draw conclusions based
on manual reading havealready been considered inefficient. It is hard to conduct exploratory
researchon such big data thatfar exceeds the capacityof manual readingtasks. Hence, scholars
in the humanities have initiated considerations regarding the integration of information
technologies to enhance their research, aiming to identify phenomena that might have been
challengingto discern through traditionalmeans or to conduct research thathas been hard to
imagine in the pre-digital era (Hsiang and Tu, 2011). In recent years, humanities research
supported by digital databases has gradually become an important research method for
humanities scholars (Hockey, 2004).Additionally, the development of digitalhumanities tools
based on exploringthe contents of digital databaseshas also played an essentialrole in adding
momentum to the development of digital humanities. The development of digital humanities
tools can currently be divided into several areas: text analysis tools, network analysis tools,
Geographic Information System (GIS)/visualization tools, and integrated system platforms
(Yuting et al.,2023;Liu and Wang, 2020). An important research topic in digital humanities
based on textanalysis tools is exploringthe relationship betweennamed entities within a given
text, such as people,places, organizationnames, etc. Current text analysistools for humanities
research often lack the capability to comprehensively explore both person-to-person and
person-to-organization entityrelationships withintextual data (Chen et al., 2021).Additionally,
the user interfaces of these toolsare not as user-friendlyor comprehensive as desired(Du et al.,
2021). These limitations hinder scholars’ability to analyze historical documents, social
movements, and cultural interactions in a holistic manner. This study aims to address these
gaps by developinga more advanced tool that leverages recent advancements in natural
language processing (NLP) to enablea more comprehensive analysis of these relationships.
Knowledgegraph (KG) that allows for a structuredand visual representationof knowledge
has slowly evolved from semantic networks, knowledgerepresentation, and naturallanguage
processing technologies (Al-Khatib et al., 2020). It is a semantic data structure that facilitates
meaningful knowledge representation in digital information systems and supports users in
exploringdigital resourcesmore effectively,and its knowledgerepresentation and visualization
properties are conducive to eliciting and absorbing knowledge (Haslhofer et al., 2018). The
advancement of knowledge graphs holds immense importance and significance within the
linguistics and social sciences. A knowledge graph consists of nodesand edges, where nodes
are entities identified from texts,such as people, places, and organizations’names, and edges
represent the attributes of entities or their relationships to each other, such as friendship,
marriage,work, etc. In other words, knowledgegraphs can compilecomplex information intoa
single diagramconcisely and effectively.They provide users witha macro and distant reading
perspective of the relationship between entities, thus helping interpret relationship contexts.
However, research on applying knowledge reasoning and data visualization techniques to
digitalhumanities studies, suchas genealogy study (Wang et al.,2023) or knowledge discovery
from ancient Chinese scientific and technological documents (Zheng et al., 2023a), etc. is still
limited. In general, the methods for knowledge reasoning in automatically generating
knowledge graphs include logic rules-based, distributed representation-based, and neural
network-basedapproaches (Wang et al., 2023).
Based on Chinese-named entity recognition (CNER) technology (Liu et al., 2022), a neural
network-based machine learning scheme, and the data visualization method, this study
developed a Knowledge Graph Analysis Tool of People and Organizations (KGAT-PO) for
digital humanities research that can express the relationship between people, as well as
people and organizations as knowledge graphs, to assist humanities researchers in
interpreting the relationships in biographical texts. Since biographical texts enable readers to
Data
Technologies and
Applications
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