Knowledge mining and graph visualization of ancient Chinese scientific and technological documents bibliographic summaries based on digital humanities
| Date | 29 May 2023 |
| Pages | 1693-1721 |
| DOI | https://doi.org/10.1108/LHT-11-2022-0538 |
| Published date | 29 May 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 | Xiang Zheng,Mingjie Li,Ze Wan,Yan Zhang |
Knowledge mining and graph
visualization of ancient Chinese
scientific and technological
documents bibliographic
summaries based on
digital humanities
Xiang Zheng and Mingjie Li
School of Information Management, Wuhan University, Wuhan, China and
Intellectual Computing Laboratory for Cultural Heritage, Wuhan University,
Wuhan, China
Ze Wan
School of Information Management, Wuhan University, Wuhan, China;
Intellectual Computing Laboratory for Cultural Heritage, Wuhan University,
Wuhan, China and
Library, Tibet University, Lhasa, China, and
Yan Zhang
State Key Laboratory of Information Engineering in Surveying, Mapping, and
Remote Sensing, Wuhan University, Wuhan, China
Abstract
Purpose –This study aims to extract knowledge of ancient Chinese scientific and technological documents
bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and
systematically. By presenting the relationship among content, discipline, and author, this study focuses on
providing services for knowledge discovery of ancient Chinese scientific and technological documents.
Design/methodology/approach –This study compiles ancient Chinese STDBS and designs a knowledge
mining and graph visualization framework. The authors define the summaries’entities, attributes, and
relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and
rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other
visualization techniques for KG construction and application. This study presents the generation, distribution, and
evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.
Findings –The knowledge mining and graph visualization framework is feasible and effective. The BERT-
BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese
agriculturalscientific and technologicaldocuments has distinctive time features. The knowledge distribution is
uneven and concentrated, mainly concentrated onC1-Planting and cultivation, C2-Silkworm, and C3-Mulberry
and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.
Originality/value –This study is the first to visually present the knowledge connotation and association of
ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation
visualization of ancient Chinese STDBS.
Keywords Bibliographic summary, Knowledge graph, Scientific and technological documents, Agriculture,
Digital humanity, Ancient China
Paper type Research paper
Visualization
of ancient
Chinese
documents
1693
Funding: This work was supported by the Major Project of Philosophy and Social Science Research,
Ministry of Education (19JZD042) and Post-graduate Science Popularization Capability Promotion
Program for 2022 sponsored by China Association for Science and Technology (KXYJS2022084).
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 7 November 2022
Revised 2 February 2023
Accepted 5 March 2023
Library Hi Tech
Vol. 42 No. 6, 2024
pp. 1693-1721
© Emerald Publishing Limited
0737-8831
DOI 10.1108/LHT-11-2022-0538
1. Introduction
In ancient China, brilliant achievements were made in science and technology (Lin, 1996), and
many documents were accumulated. However, most documents were seriously lost due to
conflicts, wars, and revolutions (Li and Niu, 2010). In addition, the content of these documents
is relatively scattered, resulting in people’s vague understanding of the knowledge systems of
ancient Chinese science and technology. Ancient scientific and technological documents are
precious wealth of China. These documents’knowledge mining and graph visualization are
significant in promoting the creative transformation and innovative development of excellent
Chinese traditional culture.
Recently, the Chinese government has begun to attach importance to the bibliographic
research of ancient Chinese scientific and technological documents. Bibliographic databases
have been constructed for ancient Chinese agriculture, medicine, and climate documents
(Wang et al., 2018). However, there are still problems in the existing research, such as
incomplete document collection, lack of knowledge mining, and poor visualization, hindering
the comprehensive presentation, transformation, and utilization of scientific and
technological knowledge in ancient China.
The bibliography is essential for revealing and organizing documentary resources (Lee,
2012). In particular, bibliographic summaries prepared according to a specific style show
documents’characteristics, content, and origin (Tarsala, 2001). To reveal the disciplinary
categories and knowledge system of ancient Chinese science and technology, our team
organized scholars in document editing to compile the General Catalogue of Ancient Chinese
Scientific and Technological Documents.
Based on a comprehensive collection of documents, we set the writing style in advance and
wrote a summary for each document. The summary contains the document’s basic
information, such as title, volume, dynasty, author, and writing mode. It also includes details
such as the document’s description in other bibliographies of the past dynasties, its formation
background, history, academic evaluation, and the preservation place of its surviving copy.
Therefore, knowledge mining of these summaries can present more detailed information
about the author, time, version, and other metadata, which were not clearly shown in the
original text of documents. But one question is to be considered: how to use advanced and
suitable methods to mine and present these summaries to provide knowledge effectively.
KG is an essentialknowledge mining andpresentation method (Tiwari etal., 2021). It stores
and presentsknowledge in structured triples (Wang et al.,2015)through critical steps such as
knowledge representation, extraction, fusion, and visualization (Nickel et al.,2016). With the
emergence of digital humanities, computer methods are used in humanities research (Wang
et al., 2020). For example, KG is widely used in Chineseancient history and culture (Liu et al.,
2020), intangiblecultural heritage(Fan and Wang, 2022),documentary heritage(Bai et al.,2022),
cultural and creative design (Feng et al.,2022), and character relationship (Chen et al.,2021).
However, the current KG still has much room for improvement in the comprehensiveness,
professionalism, andstandardizationof ancient Chinese scientificand technologicaldocuments.
It is necessary to construct a comprehensive and systematic KG forancient Chines STDBS.
Ancient Chinese scientific and technological documents have distinct disciplinary
characteristics, and their bibliographic summaries are rich in semantic connotations, both of
which are challenges for mining and visualizing the knowledge connotations of ancient
Chinese STDBS KG. This paper tries to overcome these difficulties by designing entity,
attribute, and relationship representation strategies based on the general framework of
named entity recognition (NER) and KG presentation. Deep learning techniques are
introduced into the knowledge mining of ancient Chinese STDBS. Taking ancient Chinese
agricultural STDBS as an example, we construct KG with the help of visualization
technology. Finally, we analyze the generation, distribution, and evolution of ancient
agricultural scientific and technological knowledge.
LHT
42,6
1694
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