Paper evolution graph: Multi-view structural retrieval for academic literature
Danping Liao, Yuntao Qian

TL;DR
This paper introduces a novel method called Paper Evolution Graph (PEG) for academic literature retrieval, which models the relationships and evolution of papers across multiple viewpoints using multi-view structural analysis.
Contribution
The work presents a new approach to construct structural retrieval results, called PEG, that captures diverse relationships and evolution chains among academic papers based on author, citation, and content data.
Findings
PEG effectively uncovers underlying relationships among papers.
The system supports keyword, single-paper, and two-paper queries.
Experimental results show PEG constructs meaningful and comprehensive evolution graphs.
Abstract
Academic literature retrieval is concerned with 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 work, we aim to uncover the relationships of the retrieval results and propose a method for building structural retrieval results for academic literatures, which we call a paper evolution graph (PEG). A PEG describes the evolution of the diverse aspects of input queries through several evolution chains of papers. By utilizing the author, citation and content information, PEGs can uncover the various underlying relationships among the papers and present the evolution of articles from multiple viewpoints. Our system supports three types of input queries: keyword, single-paper and two-paper queries. The construction of a PEG…
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Taxonomy
TopicsTopic Modeling · Recommender Systems and Techniques · Advanced Text Analysis Techniques
