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Formalizing biomedical concepts from textual definitions

Overview of attention for article published in Journal of Biomedical Semantics, April 2015
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Title
Formalizing biomedical concepts from textual definitions
Published in
Journal of Biomedical Semantics, April 2015
DOI 10.1186/s13326-015-0015-3
Pubmed ID
Authors

Alina Petrova, Yue Ma, George Tsatsaronis, Maria Kissa, Felix Distel, Franz Baader, Michael Schroeder

Abstract

Ontologies play a major role in life sciences, enabling a number of applications, from new data integration to knowledge verification. SNOMED CT is a large medical ontology that is formally defined so that it ensures global consistency and support of complex reasoning tasks. Most biomedical ontologies and taxonomies on the other hand define concepts only textually, without the use of logic. Here, we investigate how to automatically generate formal concept definitions from textual ones. We develop a method that uses machine learning in combination with several types of lexical and semantic features and outputs formal definitions that follow the structure of SNOMED CT concept definitions. We evaluate our method on three benchmarks and test both the underlying relation extraction component as well as the overall quality of output concept definitions. In addition, we provide an analysis on the following aspects: (1) How do definitions mined from the Web and literature differ from the ones mined from manually created definitions, e.g., MeSH? (2) How do different feature representations, e.g., the restrictions of relations' domain and range, impact on the generated definition quality?, (3) How do different machine learning algorithms compare to each other for the task of formal definition generation?, and, (4) What is the influence of the learning data size to the task? We discuss all of these settings in detail and show that the suggested approach can achieve success rates of over 90%. In addition, the results show that the choice of corpora, lexical features, learning algorithm and data size do not impact the performance as strongly as semantic types do. Semantic types limit the domain and range of a predicted relation, and as long as relations' domain and range pairs do not overlap, this information is most valuable in formalizing textual definitions. The analysis presented in this manuscript implies that automated methods can provide a valuable contribution to the formalization of biomedical knowledge, thus paving the way for future applications that go beyond retrieval and into complex reasoning. The method is implemented and accessible to the public from: https://github.com/alifahsyamsiyah/learningDL.

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Brazil 1 2%
France 1 2%
United Kingdom 1 2%
Egypt 1 2%
Portugal 1 2%
Unknown 39 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 20%
Student > Ph. D. Student 9 20%
Professor 5 11%
Student > Doctoral Student 5 11%
Student > Postgraduate 3 7%
Other 9 20%
Unknown 6 13%
Readers by discipline Count As %
Computer Science 16 35%
Medicine and Dentistry 7 15%
Agricultural and Biological Sciences 4 9%
Engineering 2 4%
Business, Management and Accounting 1 2%
Other 5 11%
Unknown 11 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 07 September 2015.
All research outputs
#4,417,011
of 6,295,757 outputs
Outputs from Journal of Biomedical Semantics
#183
of 231 outputs
Outputs of similar age
#120,107
of 180,063 outputs
Outputs of similar age from Journal of Biomedical Semantics
#19
of 19 outputs
Altmetric has tracked 6,295,757 research outputs across all sources so far. This one is in the 26th percentile – i.e., 26% of other outputs scored the same or lower than it.
So far Altmetric has tracked 231 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.