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.

Rediscovering Don Swanson:The Past, Present and Future of Literature-based Discovery

“…Don is the father of the field of drug repurposing, which proposes new uses for existing approved drugs (e.g., Weeber et al, 2003; Yang et al, 2017). Prediction of adverse drug effects follows a similar type of logic (e.g., Shang et al, 2014; Hristovski et al, 2016), as does detection of co-morbidities and other relations among drugs, diseases and genes (Frijters et al, 2010; Ding et al, 2013; Vos et al, 2014). Almost all approaches to genomic discovery involve implicit information as well.…”

“…The hope is the B-terms will point to the existence of causal mechanisms that link the literatures, but this is not necessarily the case. Other investigators have proposed ranking measures based on e.g., mutual information, relations, and/or network properties, including citations ((e.g., Wren, 2004; van der Eijk et al, 2004; Smalheiser, 2012b; Ding et al, 2013; Hristovski et al, 2015; Cameron et al, 2015). …”

Tracing database usage: Detecting main paths in database link networks

“…Entities are either evaluative entities or knowledge entities (Ding et al, 2013). Evaluative entities have been widely used to evaluate scholarly impact, including papers (de la Pena, 2011), authors (Ding, Yan, Frazho, & Caverlee, 2009;Sun & Han, 2013;Tan, Li, Zhang, & Guo), journals (Medina & van Leeuwen, 2012), institutions (Vieira & Gomes, 2010), and countries (Bornmann & Leydesdorff, 2013).…”

“…Ding (2011) combined evaluative entities (i.e., authors and papers) and knowledge entities (i.e., topics) to explain whether productive authors tended to collaborate with and/or cite researchers with the same or different topical interests (Ding, 2011). Ding et al (2013) proposed the "Entitymetric," to measure the impact of biological entities, such as genes, drugs, and diseases (Ding et al, 2013). Theories are treated as knowledge entities to explore authors' use of theory in information science research (Pettigrew & McKechnie, 2001) and family therapy research (Hawley & Geske, 2000).…”

“…We have modified the original main-path algorithms so that they can trace important knowledge flow in our database link network. This paper extends current content-based citation analysis to databases by taking the database as one kind of knowledge entity, to form database link networks (Ding et al, 2013). It proposes an easy way to evaluate database usage through citing and cited relationships between databases documented in scholarly publications.…”