A New Metric for Measuring Relatedness of ScientificPapers Based on Non-Textual Features

Fattane Zarrinkalam,Mohsen Kahani

doi:10.4236/iim.2012.44016

Fattane Zarrinkalam, Mohsen Kahani

Open Access

https://doi.org/10.4236/iim.2012.44016

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Abstract

Measuring relatedness of two papers is an issue which arises in many applications, e.g., recommendation, clustering and classification of papers. In this paper, a digital library is modeled as a directed graph; each node representing three different types of entities: papers, authors, and venues, and each edge representing relationships between these entities. Based on this graph model, six different types of relations are considered between two papers, and a new metric is proposed for evaluating relatedness of the papers. This metric only focuses on the relational features, and does not consider textual features. We have used it in combination with a textual similarity measure in the context of citation recommendation systems. Experimental results show that using this metric can successfully improve the quality of the recommendations.

Highlights

Due to the fast pace of papers on the web, a main challenge of a researcher is to acquire appropriate knowledge about current state of his research area
In the ex perimental evaluations discussed in this paper, the measure is used in the context of the citation recommendation systems, and the results show that it improves the quality of the recommendations
The citation recommendation algorithm described above has been implemented in Java, and the role of the proposed relatedness measure has been evaluated through experiments

Summary

Introduction

Due to the fast pace of papers on the web, a main challenge of a researcher is to acquire appropriate knowledge about current state of his research area. One approach is to start with an important related work and trace its citing and cited papers. Another approach is using traditional keyword-based search engines like Google. Both approaches provide a long list of papers to be studied, and they need manual filtering which is tedious and inefficient [1,2]. A recent approach, utilized by most digital libraries, is to provide a facility that automatically recommends papers related to a given paper (e.g. Google Scholar1), and more recently for a given input text (e.g. refseer). Different features of papers can be used to define such a measure

Methods

Results

Conclusion