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exploration in influence factor, education pattern, promotion strategy and assessment. Meanwhile,
we emphasize security, right, and ethics as the important base of data visualization literacy, and
look into the future of the research. As far as we can see, data visualization literacy will make a
significant impact on LIS, especially for library science, data science, and digital humanities. It will
be a new research and education direction in LIS. In the view of the application, data visualization
literacy will play an excellent role in business development and service promotion for libraries, and
contribute to the construction of smart libraries in ways of visualizing data source and knowledge
source, intelligent recommendation, accelerating the acquirement and adoption of knowledge.
Data visualization literacy will contribute to the pattern of data-driven knowledge discovery rooted
the library. Some suggestions are provided for data visualization literacy education, research, and
application in China.

Types and description rules of problem knowledge units in academic papers
〇a*
SUO Chuanjun & LAI Haimei
The research problem of an academic article usually involves multiple related problems. The
research problems themselves are different in type and structure. The research problems discussed
in each paper are different, and the types and structures are also different. Because academic
retrieval is still at a coarse-grained stage, and the complexity and randomness of natural language
expression, users cannot quickly understand the issues that academic papers need to study.
The purpose of this study is to explore the application of knowledge unit in the content
organization and retrieval of academic literature with the help of the theory of knowledge units,
which has a promising application prospect, and linguistic related knowledge. This research only
analyzes the problem knowledge units in academic papers, and summarizes the types, description
rules and logical structures of the problem knowledge units in academic papers, with the view that
the current academic retrieval method cannot meet the users’ fine-grained needs and the description
of the content structure of academic papers. The method restricts the machine to provide help in
solving problems such as knowledge interpretation and mining.
This research mainly uses content analysis and inductive deduction. According to the IMRD
structure model, the writers of the thesis mainly arrange the research questions in the introduction
part. Therefore, this study selects the introduction part of the academic paper as the corpus
basis to discuss the problem knowledge unit in the introduction part of the academic paper. The
description of problem knowledge units in academic papers is mostly in the form of sentences.
Different types of problem knowledge units have different description methods, and the structure
and complexity of sentences are also quite different. Therefore, this article adopts the interpretation

* Correspondence should be addressed to SUO Chuanjun, Email:suocj@ruc.edu.cn, ORCID:0000-0002-7416-1531.