Entitymetrics: Measuring the Impact of Entities

Abstract

This paper proposes entitymetrics to measure the impact of knowledge units. Entitymetrics highlight the importance of entities embedded in scientific literature for further knowledge discovery. In this paper, we use Metformin, a drug for diabetes, as an example to form an entity-entity citation network based on literature related to Metformin. We then calculate the network features and compare the centrality ranks of biological entities with results from Comparative Toxicogenomics Database (CTD). The comparison demonstrates the usefulness of entitymetrics to detect most of the outstanding interactions manually curated in CTD.

References in corpus (3)

• Does the h-index have predictive power?
• Ranking Scientific Publications Using a Simple Model of Network Traffic
• Finding Complex Biological Relationships in Recent PubMed Articles Using Bio-LDA

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• Exploring the relationship between team institutional composition and novelty in academic papers based on fine-grained knowledge entities
• Exploring Academic Influence of Algorithms by Co-occurrence Network Based on Full-text of Academic Papers
• Mining Temporal Evolution of Knowledge Graph and Genealogical Features for Literature-based Discovery Prediction