The contribution of cause-effect link to representing the core of scientific paper—The role of Semantic Link Network

Mengyun Cao, Xiaoping Sun, Hai Zhuge*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The Semantic Link Network is a general semantic model for modeling the structure and the evolution of complex systems. Various semantic links play different roles in rendering the semantics of complex system. One of the basic semantic links represents cause-effect relation, which plays an important role in representation and understanding. This paper verifies the role of the Semantic Link Network in representing the core of text by investigating the contribution of cause-effect link to representing the core of scientific papers. Research carries out with the following steps: (1) Two propositions on the contribution of cause-effect link in rendering the core of paper are proposed and verified through a statistical survey, which shows that the sentences on cause-effect links cover about 65% of key words within each paper on average. (2) An algorithm based on syntactic patterns is designed for automatically extracting cause-effect link from scientific papers, which recalls about 70% of manually annotated cause-effect links on average, indicating that the result adapts to the scale of data sets. (3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated. The experiments show that the quality of the summaries is significantly improved, which verifies the role of semantic links. The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing.
Original languageEnglish
Article numbere0199303
JournalPLoS ONE
Issue number6
Publication statusPublished - 21 Jun 2018

Bibliographical note

© 2018 Cao et al. This is an open access
article distributed under the terms of the Creative
Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in
any medium, provided the original author and
source are credited

Funding: This research was supported by the National Science Foundation of China (No.61640212), the Natural Science Foundation of Jiangsu Province (No. BK20150862), and funding from Guangzhou University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


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