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190 Journal of Library Science in China, Vol.10, 2018

the characteristics and structure of knowledge unit to further verify the research hypothesis
proposed in this paper. In addition, we only select one example of knowledge unit to verify its
descriptive model.

Design and application of scientific paper functional units ontology
〇a*
WANG Xiaoguang〇, LI Menglin & SONG Ningyuan
With the increasing of knowledge resources and the demand of knowledge mining, it is important
to enrich the semantics of academic literature, which can not only help users to quickly and
accurately locate the knowledge units in scientific papers, but also can help readers to conduct
comparative analysis and strategic reading. Therefore, it’s essential to identify and describe
the components and their semantic functions within scientific papers for promoting knowledge
discovery and knowledge services.
Scientific paper content ontology is the standardized knowledge representation of scientific
papers’ content structure and semantic function. It is of great significance for the deep indexing,
information extraction and knowledge mining of scientific papers. After a review on the existing
researches of paper components and attributions as well as the published ontologies, the existing
ontologies, limited to the fundamental theories, have some deficiencies in revealing the deep
semantics of information embedded in scientific papers. In order to design and build a component
ontology, which is more suitable for information extraction, the functional unit theory should be
considered.
The functional unit theory is the fundamental theory that combines information tasks and genre
analysis, which is more suitable for the development of scientific paper content ontology oriented
to knowledge discovery. Based on the functional unit theory, a novel ontology named Scientific
Paper Functional Units Ontology (FUO) is designed. After reviewing the 41 functional units,
28 components are redesigned, including background, goal, motivation, method-description,
conclusion, contributions, etc. Based on the components, 12 classes and 28 subclasses are
designed. The attributions of the classes are also designed by refering to Bio-Event ontology and
News-Event ontology. The classes and attributions of FUO are formally represented with protégé
5.1. Then 10 research papers from JASIST are randomly selected to conduct a deep indexing
experiment by using the GATE, a semantic annotation software. Finally, the distribution of
different functional units within scientific papers is analyzed.
The originality of this research lies in the clear definition of the functional units with their
attributes and the FUO which can reveal semantic features of scientific papers components in a
more comprehensive and detailed manner. The results have also proved the potential availability

* Correspondence should be addressed to WANG Xiaoguang, Email: wxguang@whu.edu.cn, ORCID: 0000-0003-1284-7164