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1. Introduction
Humanities research has often relied chiefly on traditional text-based reading approaches to explore textual content. However, when faced with the information explosion of the current information society, research methods that solely rely on manpower to draw conclusions based on manual reading have already been considered inefficient. It is hard to conduct exploratory research on such big data that far exceeds the capacity of manual reading tasks. 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 challenging to discern through traditional means or to conduct research that has 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 digital humanities tools based on exploring the contents of digital databases has also played an essential role 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 text analysis tools is exploring the relationship between named entities within a given text, such as people, places, organization names, etc. Current text analysis tools for humanities research often lack the capability to comprehensively explore both person-to-person and person-to-organization entity relationships within textual data (Chen et al., 2021). Additionally, the user interfaces of these tools are not as user-friendly or 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 These limitations hinder scholars’ ability to analyze historical documents, social movements, and cultural interactions in a holistic manner.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 developing a more advanced tool that leverages recent advancements in natural language processing (NLP) to enable a more comprehensive analysis of these relationships.
Knowledge graph (KG) that allows for a structured and visual representation of knowledge has slowly evolved from semantic networks, knowledge representation, and natural language processing technologies (Al-Khatib et al., 2020). It is a semantic data structure that facilitates meaningful knowledge representation...