↓ Skip to main content

The BioHub Knowledge Base: Ontology and Repository for Sustainable Biosourcing

Overview of attention for article published in Journal of Biomedical Semantics, June 2016
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
2 X users

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
28 Mendeley
citeulike
2 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
The BioHub Knowledge Base: Ontology and Repository for Sustainable Biosourcing
Published in
Journal of Biomedical Semantics, June 2016
DOI 10.1186/s13326-016-0071-3
Pubmed ID
Authors

Warren J. Read, George Demetriou, Goran Nenadic, Noel Ruddock, Robert Stevens, Jerry Winter

Abstract

The motivation for the BioHub project is to create an Integrated Knowledge Management System (IKMS) that will enable chemists to source ingredients from bio-renewables, rather than from non-sustainable sources such as fossil oil and its derivatives. The BioHubKB is the data repository of the IKMS; it employs Semantic Web technologies, especially OWL, to host data about chemical transformations, bio-renewable feedstocks, co-product streams and their chemical components. Access to this knowledge base is provided to other modules within the IKMS through a set of RESTful web services, driven by SPARQL queries to a Sesame back-end. The BioHubKB re-uses several bio-ontologies and bespoke extensions, primarily for chemical feedstocks and products, to form its knowledge organisation schema. Parts of plants form feedstocks, while various processes generate co-product streams that contain certain chemicals. Both chemicals and transformations are associated with certain qualities, which the BioHubKB also attempts to capture. Of immediate commercial and industrial importance is to estimate the cost of particular sets of chemical transformations (leading to candidate surfactants) performed in sequence, and these costs too are captured. Data are sourced from companies' internal knowledge and document stores, and from the publicly available literature. Both text analytics and manual curation play their part in populating the ontology. We describe the prototype IKMS, the BioHubKB and the services that it supports for the IKMS. The BioHubKB can be found via http://biohub.cs.manchester.ac.uk/ontology/biohub-kb.owl .

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 14%
Lecturer 3 11%
Researcher 3 11%
Student > Ph. D. Student 3 11%
Student > Bachelor 2 7%
Other 3 11%
Unknown 10 36%
Readers by discipline Count As %
Computer Science 8 29%
Biochemistry, Genetics and Molecular Biology 2 7%
Agricultural and Biological Sciences 2 7%
Business, Management and Accounting 1 4%
Unspecified 1 4%
Other 5 18%
Unknown 9 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 June 2016.
All research outputs
#14,574,276
of 23,344,526 outputs
Outputs from Journal of Biomedical Semantics
#212
of 367 outputs
Outputs of similar age
#192,749
of 340,671 outputs
Outputs of similar age from Journal of Biomedical Semantics
#15
of 24 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 367 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 340,671 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.