Paper evolution graph: multi-view structural retrieval for academic literature

Dan-ping LIAO; Yun-tao QIAN

doi:10.1631/FITEE.1700105

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Paper evolution graph: multi-view structural retrieval for academic literature

Regular Papers | Updated：2022-05-19

- Paper evolution graph: multi-view structural retrieval for academic literature
- 论文演化图：学术文献多视角结构化检索
- Frontiers of Information Technology & Electronic Engineering Vol. 20, Issue 2, Pages: 187-205(2019)
- Affiliations：
  
  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  Yun-tao QIAN, E-mail: ytqian@zju.edu.cn
- Funds：
- DOI：10.1631/FITEE.1700105
  CLC： TP391
- Received：13 February 2017，
  
  Revised：2019-;2-15，
  
  Published：2019-02
- Accepted：
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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.
DOI：

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. DOI： 10.1631/FITEE.1700105.

摘要

学术文献检索关注于选取最可能符合用户信息需求的论文。目前大部分检索系统局限于输出相关文献列表，而这些检出文献相互独立。本文旨在揭示检索结果的相互关系。提出一种为学术文献建立结构化检索结果的方法，称为论文演化图（PEG）。PEG采用多个演化链描述查询输入信息在不同主题方向的演化情况。通过论文作者、参考文献引用、论文内容信息这3个视角，PEG能够发现文献之间各种潜在关系，并多视角展示文献演化过程。该文献检索系统支持关键词、单篇论文、双论文3种查询方式。PEG构造主要有3个步骤：首先，采用元图分解法把文献软聚合为多个群落，获取每篇论文的主题分布；其次，从与查询相关的文献群落中提取主题连贯性演化链。每条演化链反映查询信息的某一视角；最后，提取的演化链组合形成论文演化图，可以覆盖查询涉及的所有主题。基于真实文献数据库的实验结果表明，该方法能够建立对用户有意义的论文演化图。

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.

关键词

Keywords

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