# Entitymetrics: Measuring the Impact of Entities

arXiv: https://arxiv.org/abs/1309.2486 · HTML: https://mlsanity.com/paper/1309.2486/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2013.

## Built on by (most cited first, 5 of 5)

- [A Review on Method Entities in the Academic Literature: Extraction, Evaluation, and Application](https://mlsanity.com/paper/2209.03687/) (2022)
- [Revealing the Technology Development of Natural Language Processing: A Scientific Entity-Centric Perspective](https://mlsanity.com/paper/2606.29836/) (2026)
- [Quantifying the Knowledge Proximity Between Academic and Industry Research: An Entity and Semantic Perspective](https://mlsanity.com/paper/2602.05211/) (2026)
- [Exploring Academic Influence of Algorithms by Co-occurrence Network Based on Full-text of Academic Papers](https://mlsanity.com/paper/2606.24099/) (2026)
- [Exploring the relationship between team institutional composition and novelty in academic papers based on fine-grained knowledge entities](https://mlsanity.com/paper/2606.31058/) (2026)

## Abstract (arXiv)

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.