Entitymetrics: Measuring the Impact of Entities

TL;DR

This paper introduces entitymetrics, a novel approach to quantify the impact of entities in scientific literature, demonstrated through a case study on Metformin and biological entities, showing its effectiveness in identifying key interactions.

Contribution

The paper presents a new metric for measuring entity impact in literature, using network analysis and validation against curated biological interaction data.

Findings

Entitymetrics effectively identifies significant biological interactions.

Network centrality correlates with curated interaction data.

Method demonstrates potential for knowledge discovery in scientific literature.

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.