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


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.

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F. Zarrinkalam and M. Kahani, "A New Metric for Measuring Relatedness of ScientificPapers Based on Non-Textual Features," Intelligent Information Management, Vol. 4 No. 4, 2012, pp. 99-107. doi: 10.4236/iim.2012.44016.

Conflicts of Interest

The authors declare no conflicts of interest.


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