CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-02-15
Cited: 0
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Dan-ping Liao, Yun-tao Qian. Paper evolution graph: multi-view structural retrieval for academic literature[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(2): 187-205.
@article{title="Paper evolution graph: multi-view structural retrieval for academic literature",
author="Dan-ping Liao, Yun-tao Qian",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="2",
pages="187-205",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700105"
}
%0 Journal Article
%T Paper evolution graph: multi-view structural retrieval for academic literature
%A Dan-ping Liao
%A Yun-tao Qian
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 2
%P 187-205
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700105
TY - JOUR
T1 - Paper evolution graph: multi-view structural retrieval for academic literature
A1 - Dan-ping Liao
A1 - Yun-tao Qian
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 2
SP - 187
EP - 205
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700105
Abstract: academic literature retrieval concerns about the selection of papers that are most likely to match a user‘s information needs. Most of the retrieval systems are limited to list-output models, in which the retrieval results are isolated from each other. In this paper, we aim to uncover the relationships between the retrieval results and propose a method to build structural retrieval results for academic literature, which we call a paper evolution graph (PEG). The PEG describes the evolution of diverse aspects of input queries through several evolution chains of papers. By using the author, citation, and content information, PEGs can uncover various underlying relationships among the papers and present the evolution of articles from multiple viewpoints. Our system supports three types of input queries: keyword query, single-paper query, and two-paper query. The construction of a PEG consists mainly of three steps. First, the papers are soft-clustered into communities via metagraph factorization, during which the topic distribution of each paper is obtained. Second, topically cohesive evolution chains are extracted from the communities that are relevant to the query. Each chain focuses on one aspect of the query. Finally, the extracted chains are combined to generate a PEG, which fully covers all the topics of the query. Experimental results on a real-world dataset demonstrate that the proposed method can construct meaningful PEGs.
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