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Entitymetrics: measuring the impact of entities.

Ding Y, Song M, Han J, Yu Q, Yan E, Lin L, Chambers T - PLoS ONE (2013)

Bottom Line: Entitymetrics highlight the importance of entities embedded in scientific literature for further knowledge discovery.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Information and Library Science, Indiana University, Bloomington, Indiana, USA.

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.

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Related in: MedlinePlus

An overview of Entity Citation Network generation.
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pone-0071416-g004: An overview of Entity Citation Network generation.

Mentions: Figure 4 illustrates the process used to create the entity citation network which includes three components: ArticleFilter, EntityFetcher, and GraphCreator. The ArticleFilter component extracts a set of referenences from the reference section of papers related to a target object (e.g., a diseasese, a concept, and a method), which are showed in squared parenthesis. Subsequently, the EntityFetcher component retrieves entities for this set of references. Finally, the GraphCreator component generates a hash table of entity citation relationships and counts the number of times each relationship occurs. In the final graph, vertices represent entities and edges represent citation relationships with number of citations as weights. In this paper, the ArticleFilter is applied to get the list of references from the PubMed papers related to Metformin, then the EntityFetcher collected extracted entities from this list of references, finally the GraphCreator generated a entity citation graph based on the entities retrieved from the EntityFetcher and citation relationships captured by the ArticleFilter.


Entitymetrics: measuring the impact of entities.

Ding Y, Song M, Han J, Yu Q, Yan E, Lin L, Chambers T - PLoS ONE (2013)

An overview of Entity Citation Network generation.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3756961&req=5

pone-0071416-g004: An overview of Entity Citation Network generation.
Mentions: Figure 4 illustrates the process used to create the entity citation network which includes three components: ArticleFilter, EntityFetcher, and GraphCreator. The ArticleFilter component extracts a set of referenences from the reference section of papers related to a target object (e.g., a diseasese, a concept, and a method), which are showed in squared parenthesis. Subsequently, the EntityFetcher component retrieves entities for this set of references. Finally, the GraphCreator component generates a hash table of entity citation relationships and counts the number of times each relationship occurs. In the final graph, vertices represent entities and edges represent citation relationships with number of citations as weights. In this paper, the ArticleFilter is applied to get the list of references from the PubMed papers related to Metformin, then the EntityFetcher collected extracted entities from this list of references, finally the GraphCreator generated a entity citation graph based on the entities retrieved from the EntityFetcher and citation relationships captured by the ArticleFilter.

Bottom Line: Entitymetrics highlight the importance of entities embedded in scientific literature for further knowledge discovery.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Information and Library Science, Indiana University, Bloomington, Indiana, USA.

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.

Show MeSH
Related in: MedlinePlus