Extracting the Evolutionary Backbone of Scientific Domains: The semantic main path network analysis approach based on citation context analysis

Xiaorui Jiang, Junjun Liu

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
103 Downloads (Pure)

Abstract

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT-based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, e.g., extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top-K main paths extracted from various time slices of semantic citation network. In addition, this study proposed a three-way framework for quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics-agnostic counterparts, different types of semantic main path networks provide complementary views scientific knowledge flows. Combining them together, we can obtain a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas.
Original languageEnglish
Pages (from-to)546-569
Number of pages24
JournalJournal of the Association for Information Science and Technology
Volume74
Issue number5
Early online date21 Mar 2023
DOIs
Publication statusPublished - May 2023

Bibliographical note

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Funder

Xiaorui Jiang is partially supported by National Office for Philosophy and Social Sciences of China (18ZDA238).

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