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

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 mainly consists 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. The experimental results on a real-world dataset demonstrate that the proposed method is able to construct meaningful PEGs.