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Quantifying and filtering knowledge generated by literature based discovery.

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  • 1Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield, UK. j.preiss@sheffield.ac.uk.

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|June 16, 2017
PubMed
Summary
This summary is machine-generated.

Literature-based discovery (LBD) can generate vast amounts of hidden knowledge. Intelligent filtering significantly reduces this output, making single-step connections manageable for specific terms.

Keywords:
Biomedical textData miningLiterature based discovery in the biomedical domain

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Knowledge Discovery

Background:

  • Literature-based discovery (LBD) systems infer novel connections from existing literature.
  • A key challenge is managing the large volume of inferred knowledge.

Purpose of the Study:

  • To analyze the quantity of hidden knowledge generated by LBD.
  • To evaluate the impact of filtering approaches on LBD output.

Main Methods:

  • Detailed analysis of hidden knowledge quantity from an LBD system.
  • Investigation of filtering combined with single or multi-step linking term chains.
  • Application to all articles in the PubMed database.

Main Results:

  • Replication of existing discoveries validated multi-step linking chain knowledge.
  • Time-slicing provided a large-scale performance measure.
  • Intelligent filtering substantially reduced the number of generated hidden knowledge pairs.

Conclusions:

  • The volume of hidden knowledge from LBD can be vast but is manageable with intelligent filtering.
  • Single-step connections for specific terms are often within a manageable range.